# Billing
Source: https://docs.aptlystar.ai/account-details/billing



The **Billing** section gives you a clear view of your current subscription, payment records, and usage limits. This area is split into two tabs: **Subscription** and **Payment History**—each serving a different purpose in tracking and managing your account finances.

***

## Subscription Tab

The **Subscription** tab provides a detailed summary of your active plan, including billing frequency, amount paid, renewal date, and associated limits such as Q\&A sessions and data size.

<Frame>
  <img alt="Billing: Subscription Overview" />
</Frame>

You can also:

* View your next billing cycle and invoice status
* Check usage metrics (e.g., Q\&A sessions, data size)
* Explore upgrade options or cancel your plan
* Navigate directly to detailed analytics and data store dashboards

This tab is your go-to for understanding **current plan details** and **managing upgrades or add-ons**.

## ⚡ Auto Add-ons

The **Auto Add-ons** feature allows automatic purchasing of extra Q\&A packs or storage when your organization reaches its plan limit.\
When enabled, the system seamlessly tops up your account — preventing service interruptions and ensuring continued access to data and AI sessions without manual action.

<Frame>
  <img alt="Billing: Auto Add-ons view" />
</Frame>

### Key Highlights

* Automatically activates additional Q\&A packs or storage once limits are reached
* Ensures uninterrupted usage even during high-traffic periods
* Sends an email confirmation each time a top-up occurs
* You can toggle this feature **on or off** at any time within the billing page

> 💡 Auto Add-ons are especially useful for teams with fluctuating workloads or dynamic data usage patterns.

***

## Payment History Tab

The **Payment History** tab helps you track all past transactions, both for full plan subscriptions and any purchased add-ons.

<Frame>
  <img alt="Billing: Payment History Records" />
</Frame>

It shows:

* Date of purchase or subscription
* Plan or Add-on type
* Transaction ID, invoice links
* Amount paid, billing term, and payment status

This is helpful for compliance, accounting, and ensuring your team always has access to accurate financial records.

***


# Upgrade
Source: https://docs.aptlystar.ai/account-details/upgrade



The **Upgrade** page allows you to explore and purchase plans that align with your team's usage needs — whether you're just starting out or scaling fast. You can toggle between **Monthly** and **Annual** billing options and purchase **one-time add-ons** for Q\&A sessions and datastore storage whenever needed.

***

## Choose a Plan That Fits

Each AptlyStar subscription plan comes with usage limits across:

* **Q\&A sessions** per month
* **Team members**
* **Webpage connections**
* **File upload size**
* **AI knowledge base (datastore) size**

You can view and switch between **monthly** and **annual** billing cycles using the toggle at the top of the page.

***

## 📅 Monthly Billing

If you prefer flexible, pay-as-you-go pricing, the monthly billing option offers three tiers:

* **Starter** – \$12/month\
  Great for small teams or solo creators\
  → 1,200 Q\&A, 3 members, 10 webpages, 1GB data size

* **Scale** – \$60/month\
  Best for mid-sized teams with moderate usage\
  → 3,600 Q\&A, 25 members, 25 webpages, 2.5GB data size

* **Growth** – \$200/month\
  Ideal for growing businesses and large AI deployments\
  → 12,000 Q\&A, 50 members, 100 webpages, 5GB data size

<Frame>
  <img alt="Upgrade Monthly Pricing" />
</Frame>

***

## 🗓️ Annual Billing

Annual plans offer the same tiers — but at a discounted yearly rate. These are perfect for teams who want to commit long-term and save:

* **Starter** – \$100/year\
  → 14,400 Q\&A, 3 members, 1.4GB data size

* **Scale** – \$720/year\
  → 43,200 Q\&A, 25 members, 2.5GB data size

* **Growth** – \$2,400/year\
  → 144,000 Q\&A, 50 members, 5GB data size

<Frame>
  <img alt="Upgrade Annual Pricing" />
</Frame>

> All plans include access to core features, analytics, chat embedding, data stores, and multi-agent workflows.

***

## Need More? Add-on Packs

You don’t have to upgrade the entire plan to get more resources. AptlyStar supports **one-time add-on purchases** for both **Q\&A usage** and **datastore size** — with no limits on how many times you top up.

These add-ons are applied instantly and stack with your current plan.

***

### Add Extra Q\&A Sessions

Perfect for months where your AI agents experience a spike in conversations.

| Additional Q\&A | Price |
| --------------- | ----- |
| 500             | \$15  |
| 1000            | \$30  |
| 2000            | \$45  |

→ Purchase as many as needed — they do not expire monthly.

***

### Expand Datastore Storage

Add more capacity to your AI knowledge base without changing your plan.

| Additional Storage | Price |
| ------------------ | ----- |
| 500 MB             | \$50  |
| 1000 MB            | \$100 |
| 2000 MB            | \$180 |

→ Useful when uploading large PDFs, documents, or web crawls.

***

## ✅ Summary

* **Switch plans anytime** between monthly and annual tiers
* **Add Q\&A or storage add-ons** whenever you hit limits
* **Only pay for what you need**, when you need it
* **Current plan and add-on status** shown clearly for full transparency

Everything is designed to give you full control over your usage and costs — while scaling smoothly with your team.

***


# Agent Grouping
Source: https://docs.aptlystar.ai/ai-bots-console/agentgrouping



Agent Grouping allows you to bundle multiple specialized agents under a single main agent.\
This enables users to dynamically switch between experts within the same chat interface, based on context.

With Agent Grouping, users can:

* Switch between agents dynamically inside the chat widget
* Interact with each agent’s unique knowledge base
* Optimize token usage by isolating prompts per sub-agent
* Organize agents for specialized tasks (e.g., Interviewer, Excel helper, Pronunciation coach)

***

## What is a Main Agent?

The **Main Agent** acts as the anchor. Other agents (called **Linked Agents**) will be accessed through the main agent’s widget.

Each Linked Agent:

* Is independently configured
* Has its own datastore and knowledge
* Can be selected in the frontend chat widget for specialized responses

***

## Requirements

Before setting up grouping:

* All agents (main and sub-agents) must be **set to live**
* Each agent should have at least one valid datastore
* Grouping must be **explicitly enabled** in the Main Agent’s channel config

***

## How to Group Agents

<Steps>
  <Step title="Create and activate your agents">
    Go to <strong>My Agents</strong> and ensure all agents you want to group are created and active.

    <Frame>
      <img alt="My Agents: All agents live and ready for grouping" />
    </Frame>
  </Step>

  <Step title="Open the main agent playground">
    Select the agent that will act as the <strong>Main Agent</strong> and navigate to the <strong>Channels</strong> tab.

    <Frame>
      <img alt="Channels: Navigate to Main Agent’s playground" />
    </Frame>
  </Step>

  <Step title="Enable Agent Grouping">
    Scroll to the <strong>Agent Grouping</strong> section and toggle “Enable Grouping”.

    <Frame>
      <img alt="Enable Grouping: Toggle to activate agent grouping mode" />
    </Frame>
  </Step>

  <Step title="Set the Main Agent">
    Once grouping is enabled, the selected agent is tagged as the <strong>Main Agent</strong>.\
    Click <strong>Link Agent</strong> to start selecting sub-agents.

    <Frame>
      <img alt="Link Agents: Assign specialized sub-agents to the main agent" />
    </Frame>
  </Step>

  <Step title="Select Linked Agents">
    Only active agents are shown in the dropdown. Select the agents you want to group and click <strong>Link Agent</strong>.

    <Frame>
      <img alt="Dropdown: Select which agents to group under the Main Agent" />
    </Frame>

    Once you've selected your desired agents, they will be listed as part of the agent group under the main agent.

    <Frame>
      <img alt="Success: Agents added to the group successfully" />
    </Frame>
  </Step>

  <Step title="Chat Widget Experience">
    When the widget is loaded for the main agent, users can switch between all linked agents in real-time based on context.

    <Frame>
      <img alt="Widget View: Switch between grouped agents at runtime" />
    </Frame>
  </Step>
</Steps>

***

## Tips & Notes

* Sub-agents do **not share** knowledge across the group.
* Switching agents does **not reset** the conversation unless designed to.
* Each sub-agent retains its independent model and prompt style.

***

## ✅ Example Use Cases

***

### 🧑‍💼 HR Assistant Suite

* **Main Agent**: *Job Application Guide*
* **Linked Agents**:
  * 📄 *Resume Reviewer* – Analyzes uploaded resumes and offers formatting or content improvements.
  * 🧪 *Interview Simulator* – Conducts mock interviews tailored to job roles.
  * 📬 *Offer Negotiator* – Helps craft polite counteroffers and answers negotiation-related queries.

**Why this works**:\
Candidates receive end-to-end job search support within a single chat interface — from resume polishing and interview practice to final-stage negotiation help.

***

### 🧑‍🎓 AI Student Mentor

* **Main Agent**: *Academic Coach*
* **Linked Agents**:
  * 📚 *Math Problem Solver* – Provides step-by-step solutions for algebra, calculus, and statistics problems.
  * 🧪 *Science Explainer* – Simplifies complex physics, chemistry, or biology concepts into digestible chunks.
  * 📝 *Essay Feedback Agent* – Reviews written submissions and suggests grammar and clarity improvements.

**Why this works**:\
Students can switch between topic-specific AI mentors to support homework, exam prep, and writing — without breaking flow or opening separate tools.

***


# Canvas
Source: https://docs.aptlystar.ai/ai-bots-console/canvas



The **Canvas** is your interactive workspace to communicate with an AI agent in real time. It delivers fast, accurate responses and shows the **sources used** for each answer, ensuring **transparency** and **trust**.

Whether you're troubleshooting, exploring knowledge, or just experimenting—Canvas is where conversations become insights.

***

## 🎯 Key Features

* **Live Query Interface**: Engage directly with the agent using natural language
* **Source Traceability**: View documents or data used in the agent’s response
* **Session History**: Track your conversation flow and total query count
* **Download Chat**: Save entire conversations with a single click
* **Conversation Sharing**: Share either a full conversation or a specific message as a public link

***

## How to Use the Canvas

To get started:

1. Navigate to the **Canvas** section from the left sidebar
2. Type your question into the query bar
3. View the agent’s real-time response, suggested follow-ups, and source trace
4. Click **Download** to export the conversation
5. Use **Share Conversation** or **Share** (per-message) to generate public links

<Frame>
  <img alt="Canvas Full View" />
</Frame>

***

## 🔗 Share Conversations

You can now share your conversations in two ways:

### 1. Share Entire Conversation

* Click the **Share Conversation** button at the top right
* A public view-only link will be generated

<Frame>
  <img alt="Share Full Conversation" />
</Frame>

* You can revoke this access at any time

<Frame>
  <img alt="Share Full Conversation" />
</Frame>

### 2. Share Specific Answer

* Hover over an individual answer
* Click the **Share** button underneath

<Frame>
  <img alt="Share Individual Message" />
</Frame>

* This generates a link for just that response and question

<Frame>
  <img alt="Share Individual Message" />
</Frame>

***

## ⚡ Enable Live Mode for Integrations

Before you can use the integrations inside the **Channels** tab, you must turn **Live Mode** ON.

Located at the top-right corner of your agent interface, this toggle ensures that your agent is ready to interact across external platforms like your **website**, **chat apps**, or **APIs**.

<Frame>
  <img alt="Live Mode Toggle" />
</Frame>

***

## 🛠 Tip: Simulate Real Interactions

<Note>
  Use Canvas to simulate real user interactions and validate how your AI agent performs in production-like scenarios. Ideal for QA, feedback review, and regression testing.
</Note>

## 🧠 Multi-Model Comparison

The Canvas also supports **Multi-Model Comparison** — a feature that lets you test and compare responses from **up to 3 different AI models simultaneously**.

This is especially useful for:

* Comparing model capabilities side-by-side
* Evaluating new models before rollout
* Ensuring consistent answers across providers (e.g., GPT-4 vs Claude vs Mistral)

To use this feature:

1. Click on the **Multi-Model** toggle in the top-right of Canvas
2. Add up to 3 models to the canvas using the dropdowns
3. Enter a question once and receive responses from all selected models

<Frame>
  <img alt="Multi-Model Comparison View" />
</Frame>

This mode can be easily toggled off if you want to return to single-model focus.


# Channels
Source: https://docs.aptlystar.ai/ai-bots-console/channels



The **Channels** section lets you connect your AI agent to platforms like **Telegram**, **Discord**, **WhatsApp**, and **HTML websites** — enabling your agent to operate wherever your users are.

By setting up these integrations, you can:

* Reach users directly inside their preferred apps and tools
* Create consistent multi-platform agent experiences
* Seamlessly embed or launch agents on any digital interface

This section serves as your hub to deploy the agent across external channels.

<Frame>
  <img alt="Channels Section" />
</Frame>

## Enable Live Mode for Integrations

Before using any integrations in the **Channels** tab, you must activate **Live Mode**.

This toggle — located at the top-right of your agent page — ensures your agent is ready to communicate on external platforms.

<Frame>
  <img alt="Live On Toggle Switch" />
</Frame>

<Note>
  Without enabling Live Mode, integration options will remain disabled.
</Note>

## Embedding the Agent via HTML

To embed your AI agent into your website:

<Steps>
  <Step title="Select HTML Embed">
    Open the **Channels** section and choose the **HTML Embed** option.

    <Frame>
      <img alt="HTML Embed Option" />
    </Frame>
  </Step>

  <Step title="Copy the Script">
    Once inside HTML Embed, copy the auto-generated embed script.

    <Frame>
      <img alt="Embed Script" />
    </Frame>
  </Step>

  <Step title="Paste Into Your Website">
    ```javascript Code Block Example theme={null}
    <html lang="en">
    <head>
      <meta charset="UTF-8">
      <meta name="viewport" content="width=device-width, initial-scale=1.0">
      <title>Document</title>
    </head>
    <body>
      <script src="http://localhost:3000/chat.min.js" data-chat-url="http://localhost:2939/agent/e7856d02-bf18-4487-a081-dec2e8857ce3" data-btn-position="bottom-right" data-widget-type="chat bubble" data-widget-btn-color="#9b59b6 " defer> </script>
    </body>
    </html>
    ```

    Paste the script before the closing `</body>` tag of your HTML file and refresh the page to view the widget.

    <Frame>
      <img alt="Widget Live on Site" />
    </Frame>
  </Step>
</Steps>

***

## Widget Customization Options

The HTML Embed interface includes flexible controls to adapt the widget to your brand or use case.

### Customization Tabs

<Frame>
  <img alt="Widget Customization Options" />
</Frame>

The **HTML Embed** tool provides three tab options:

* **Script to embed into your website**
* **Public URL**: shareable hosted link to the chat interface
* **Iframe**: embed the agent using an iframe tag inside your site layout

### Visual Customizations

<Frame>
  <img alt="Widget Customization Options" />
</Frame>

You can adjust:

* **Widget Position**: Bottom Right, Bottom Left, Top Right, Top Left
* **Widget Type**: Chat Bubble or Search Box
* **Open Mode**: Open via widget click or automatically on page load
* **Widget Icon**: Supply a custom icon URL
* **Widget Button Color**: Customize button color using a hex code
* **Starter Questions Font Size**: Choose between Small (12px), Medium (14px), and Large (16px)

These options let you tailor the look and behavior of the agent widget to suit your site’s layout and branding.

***


# Conversations
Source: https://docs.aptlystar.ai/ai-bots-console/conversations



Track, review, and analyze all interactions users have with your AI agent—whether it's on your website or integrated platforms like Telegram and Discord.

***

## What Are Conversations?

The **Conversations** section logs every chat your users have with the agent. These include queries submitted on:

* Your **website** (via the embedded widget)
* External channels (Telegram, Discord, etc.)

Each message, response, and follow-up is recorded with timestamps and source identifiers for easy traceability.

***

## How to Enable Website Conversations

<Steps>
  <Step title="Step 1: Embed the Agent on Your Site">
    Add the provided HTML embed snippet to your website.

    Refer to [Agent Channel Integration Guide](https://docs.aptlystar.ai/ai-bots-console/channels) for full setup instructions.

    Once embedded, your agent will appear as a floating chat icon at the bottom-right of your webpage:

    <Frame>
      <img alt="Agent Entry on Website" />
    </Frame>

    Clicking the chat icon will open the chat widget for interaction.

    <Frame>
      <img alt="Agent Opened in Website" />
    </Frame>
  </Step>

  <Step title="Step 2: View Conversations in AptlyStar Portal">
    After integration, user conversations are recorded in the AptlyStar portal.

    * Navigate to your agent dashboard
    * Click on the **Conversations** tab in the sidebar
    * Filter by platform, session, or keyword to review exchanges

    <Frame>
      <img alt="View Chat Logs from Website" />
    </Frame>
  </Step>
</Steps>

***

## 🗂️ Exporting Chat History

Users can export the most recent chat from the agent interface directly as a PDF. This helps in documentation, auditing, and knowledge tracking.

<Steps>
  <Step>
    <Frame>
      <img alt="Download Chat History as PDF" />
    </Frame>
  </Step>

  Clicking the download option will allow users to export the chat transcript as a PDF file for their records.

  <Step>
    <Frame>
      <img alt="Download Chat History as PDF" />
    </Frame>
  </Step>
</Steps>

You can also analyze these conversations to improve your agent’s responses or refine your training data.


# AI Agents using Data Stores
Source: https://docs.aptlystar.ai/ai-bots-console/datastore



The **AI Agents in Data Stores** section enables you to connect intelligent agents to organizational data stores, allowing them to retrieve, understand, and respond using stored information.

Linking a data store to your agent ensures that it can access and utilize relevant knowledge effectively.

***

## Add a Data Store to an AI Agent

Follow the steps below to add one or more data stores to your agent:

<Steps>
  <Step title="Navigate to the Agent">
    From the **AptlyStar Dashboard**, go to the **My Agents** tab (1), then click on the **AI Agent** you want to enhance with data (2).

    <Frame>
      <img alt="Open AI Agent from My Agents" />
    </Frame>
  </Step>

  <Step title="Link a Data Store">
    Inside the agent view:

    * Click on the **Data Stores** tab (1)
    * Click **Add Data Stores (Knowledge)** (2)

    <Info>
      **Pre-requisite:** The embedding model used by the selected data store **must match** the embedding model used by the AI agent. Otherwise, the linking will be disabled.
    </Info>

    <Frame>
      <img alt="Data Stores Linked to Agent" />
    </Frame>
  </Step>

  <Step title="View Linked Data Stores">
    * Select the desired data stores (3)
    * Click **Add to Agent** (4)
      Once added, your selected data stores will be visible under the agent’s **Data Stores** tab.

    You can link multiple data stores to one agent based on the same embedding model.

    <Frame>
      <img alt="Data Stores Linked to Agent" />
    </Frame>
  </Step>

  <Step title="Verify from Data Store Console">
    To cross-verify, go to the **AI Data Stores** section of the platform.\
    Click on a data store and open the **AI Agents** tab to see which agents are currently linked to it.

    <Frame>
      <img alt="Agent Listed Under Data Store" />
    </Frame>
  </Step>
</Steps>

> Want to learn how to create a agent first?\
> Check out our [AI Agent Creation Guide](/ai-bots-console/create-bot)


# Feedbacks
Source: https://docs.aptlystar.ai/ai-bots-console/feedbacks



## Feedbacks Overview

Track and analyze user sentiment in real time using the **Feedbacks** feature in the Agent Console. Feedback helps improve your agent’s accuracy and user experience by collecting direct input on its responses.

***

## Steps to Test Feedback Feature

<Steps>
  <Step title="Step 1: React to a Agent Answer">
    Once the agent responds, give a **thumbs-up** or **thumbs-down** to express your satisfaction.

    <Frame>
      <img alt="Thumbs up/down on canvas" />
    </Frame>
  </Step>

  <Step title="Step 2: Add a Comment (Optional)">
    After reacting, a popup appears prompting for additional comments. You can enter how helpful the answer was and submit it.

    <Frame>
      <img alt="Comment box after feedback" />
    </Frame>
  </Step>

  <Step title="Step 3: View Submitted Feedback">
    Go to the **Feedbacks** tab from the left sidebar. All submitted feedback will be listed here. Click for detailed responses.

    <Frame>
      <img alt="Feedback panel with entries" />
    </Frame>
  </Step>

  <Step title="Step 4: Review Feedback Details">
    A side panel appears showing detailed comments, sentiment (positive/negative), timestamp, and feedback source, **Q\&A** and
    **Previous Q\&A** could also be viewed here.

    <Frame>
      <img alt="Detailed feedback popup" />
    </Frame>
  </Step>
</Steps>

***

## Why Use Feedback?

* Identify gaps in agent performance
* Capture user satisfaction in real-time
* Improve agent prompts and logic over time
* Filter by positive or negative sentiment

<Note>
  Feedback works best when reviewed regularly and used to iterate on your prompt designs or model configurations.
</Note>


# Lead Collection
Source: https://docs.aptlystar.ai/ai-bots-console/leads



## Lead Collection

Lead Collection lets your agent capture user details such as name, email, and phone directly during a chat session. This turns every meaningful conversation into a potential customer touchpoint and helps your team follow up efficiently.

Organizations often use this feature to:

* Identify high intent users
* Collect contact details before the conversation goes too far
* Enrich CRM records with real conversational context
* Reduce manual effort and automate lead qualification

<Frame>
  <img alt="Lead collection UI" />
</Frame>

***

## Customizing Lead Capture

Once you enable the **Lead Collection** toggle, you can fine tune how and when the agent requests this information.

### Smart Leads

Smart Leads let the bot decide the best moment to ask for user details based on the flow of the conversation. You can control the behavior by adjusting:

**Number of responses before asking**\
Choose whether the bot should ask immediately or wait for one, two, or three user responses before prompting for details.

**Number of times to ask**\
Set how persistent the agent should be. For example, you may choose to ask only once, or try again a second or third time if the user skips the form.

**Lead form title**\
You can let the bot generate the title dynamically based on context, or specify a fixed static title for consistency.

### Capture Mode

You can choose how the lead information is collected:

* **Inline Form**\
  The user sees a small form directly embedded inside the chat interface, appearing as a single compact block.

* **Conversational Mode**\
  The bot asks each detail step by step in a natural question and answer format. This creates a smoother experience and keeps the conversation flowing.

### Fields to Collect

You can select which fields the form should request:

* Name
* Email
* Phone
* Consent checkbox

These fields will appear using the capture mode you selected.

## Lead Form in the Chat Widget

Once lead collection is enabled, users will see the form automatically during the chat. This may appear at the start of the conversation or after a few messages, depending on your Smart Leads configuration.

<Frame>
  <img alt="Lead form displayed inside the widget" />
</Frame>

This ensures you capture relevant contact information while the user is actively engaged and interested.

***

## HubSpot Integration

AptlyStar supports direct integration with **HubSpot** to automatically sync leads collected from your bot.

To connect HubSpot:

1. Navigate to **Agent Settings → Collect Leads**
2. Toggle **Enable Lead Collection**
3. Under **CRM Integration**, click **Connect** next to HubSpot
4. Authenticate your HubSpot account
5. Save your settings

<Frame>
  <img alt="HubSpot integration flow" />
</Frame>

Once connected:

* Every collected lead is automatically pushed to HubSpot
* Contact details include name, email, phone, and any consent info
* Conversation context can be attached for sales follow-ups

<Frame>
  <img alt="Synced HubSpot leads list" />
</Frame>

***

## Other CRM Integrations

In addition to HubSpot, AptlyStar supports several other CRM platforms. You can view and connect them under **CRM Integrations**.

Available integrations include:

* **Salesforce**
* **Zoho CRM**
* **AptlyPlus CRM**

Each integration lets you sync collected leads directly into your preferred CRM workflow.

<Frame>
  <img alt="List of CRM integrations" />
</Frame>

<Note>
  Lead Collection combined with CRM integrations creates a seamless path from first conversation to CRM record, helping teams act faster on warm leads.
</Note>


# Look
Source: https://docs.aptlystar.ai/ai-bots-console/look



## 🎨 Customize the Look of Your Agent

The **Look** tab helps you personalize your agent’s interface to match your brand and tone. Whether it's for internal use or external deployment, a polished visual identity increases engagement and user trust.

***

## 🖼️ Overview of Look Settings

When you open the **Look** section from the agent sidebar, you’re presented with a clear layout to modify various appearance-related fields.

<Frame>
  <img alt="Look Overview Page" />
</Frame>

Here's what you can configure:

* **Agent Name**: Title shown on the agent header.
* **Splash Screen** and **Bubble Style** toggles: Switch between launch screen and chat styling.
* **Greeting Messages**: Two lines of welcome text for the splash screen.
* **Style Settings**: Colors for agent and human chat bubbles.

***

## 💬 Bubble Style View

Switching to the **Bubble Style** tab updates the chat view to resemble a real-time message flow. This is useful for tailoring agent interactions after the splash screen is dismissed.

Use this mode to:

* Match your **chat bubbles** to brand colors
* Preview how responses will appear in actual conversations
* Ensure a consistent, accessible user experience

<Frame>
  <img alt="Bubble Style Preview" />
</Frame>

***

## 🌍 Customize Greeting in Any Language

You can configure the initial greeting shown to users by updating **Greeting Message Line 1** and **Greeting Message Line 2**. This is perfect for multi-lingual agents or region-specific experiences.

<Frame>
  <img alt="Edit Greeting Messages" />
</Frame>

Just replace the default text with your desired language. For example:

* Greeting 1: `Bonjour`
* Greeting 2: `Bonjour, je suis là pour vous aider. Que puis-je faire pour vous aujourd’hui ?`

***

## 👀 Preview Greeting in Splash Screen

After saving your custom greetings, the splash screen preview instantly reflects the new welcome messages.

<Frame>
  <img alt="Greeting Applied in UI" />
</Frame>

***

## 🎨 Style Chat Bubbles

At the bottom of the Look tab, you’ll find **Style Settings** to update your agent's and user’s message bubbles.

* **Agent bubble style**: Set background and text color for agent replies.
* **Human bubble style**: Customize how user messages appear.

This helps match your brand identity and improves accessibility.

You can also upload avatar images to humanize your agent responses. Use familiar names like *Bob*, *Sarah*, or *Anna* to show who is replying within a group or team-based agent.

<Frame>
  <img alt="Agent Avatar Upload" />
</Frame>

Uploaded avatars will appear beside responses in chat, giving a personal and warm touch.

***

## 🧠 Humanization Settings & 🗣️ Text to Speech (TTS)

Two additional options help you make your agent more natural and accessible:

* **Disable Response Streaming**: Turns off token-by-token typing animation and shows full reply at once.
* **Enable TTS**: Allows your agent to speak replies out loud for enhanced accessibility or kiosk-style interfaces.

<Frame>
  <img alt="Styling and Voice Settings" />
</Frame>

These features are especially useful for voice-first interfaces, multilingual agents, or for improving inclusivity.

***

<Check>
  By fine-tuning the Look settings, you ensure your agent not only sounds intelligent but also looks and feels aligned with your product’s tone and user expectations.
</Check>


# Query Suggestions
Source: https://docs.aptlystar.ai/ai-bots-console/querysuggestions



The **Query Suggestions** feature in AptlyStar's AI Agent Canvas is designed to guide users through a seamless, interactive experience. As users ask questions, the agent not only responds intelligently but also generates **follow-up suggestions**—relevant queries tailored to the current conversation context.

This feature enables deeper exploration, enhances engagement, and allows for **multi-turn conversations**, helping users gain comprehensive insights without needing to phrase every new question from scratch.

***

## ✨ How It Works

<Steps>
  <Step title="Select Agent canvas">
    when you click on any of your agents you will be redirected to canvas page as shown belown where you can interact with the agent:

    <Frame>
      <img alt="Initial Canvas Screen" />
    </Frame>
  </Step>

  <Step title="Ask Your First Question">
    Upon accessing the **Canvas**, you'll be greeted with a friendly welcome and two options:

    * `Add your trained data`
    * `Skip for now`

    Begin by typing your query in the prompt input box.

    <Frame>
      <img alt="Initial Canvas Screen" />
    </Frame>
  </Step>

  <Step title="Receive Answer with Suggestions">
    Once you submit your query, the agent generates a response based on the selected data (web or internal). Below the response, you’ll automatically see **smart follow-up suggestions**.

    These suggestions are:

    * Generated dynamically based on the context of your original question
    * Fully clickable to extend the current conversation

    <Frame>
      <img alt="Query Suggestions Appearing After Response" />
    </Frame>
  </Step>

  <Step title="Continue the Conversation">
    Click on any suggested question to continue the dialogue. The agent treats these as part of the same session and continues building on the previous context, creating a fluid and intelligent conversation.

    <Frame>
      <img alt="Extended Dialogue Using Suggestions" />
    </Frame>
  </Step>
</Steps>

***

## Enabling Query Suggestions

To use this feature, ensure it's turned on in your agent settings.

* Go to your agent’s **Settings**
* Scroll to the bottom and toggle **"Enable Suggested Questions"**
* Click **Save** to apply the changes

<Frame>
  <img alt="Enable Suggestions in Settings" />
</Frame>

***

## 💡 Benefits of Query Suggestions

* **No need to guess** what to ask next — just pick from curated suggestions
* **Speed up exploration** of your knowledge base or web data
* **Improve engagement** by maintaining the flow of conversation
* **Ensure contextual continuity** across questions within a session

<Note>
  These suggestions are especially helpful in enterprise scenarios, where users may not always know the exact phrasing for technical or domain-specific queries.
</Note>

Continue exploring other features like [Feedbacks](/ai-bots-console/feedbacks), [Look](/ai-bots-console/look), or [Conversations](/ai-bots-console/conversations) to further enhance your AI Agent's capabilities.


# Settings
Source: https://docs.aptlystar.ai/ai-bots-console/settings



Properly configuring your agent is essential for accuracy, performance, and reliability. The **Settings** section lets you manage prompts, models, query behavior, and search precision.

***

## Settings Navigation

<CardGroup>
  <Card title="Agent Settings: Part 1" icon="star" href="#agent-settings-part-1">
    Set Chat & Embedding Models
  </Card>

  <Card title="Agent Settings: Part 2" icon="chart-simple" href="#agent-settings-part-2">
    Prompt Types & Prompt Library
  </Card>

  <Card title="Agent Settings: Part 3" icon="sliders" href="#agent-settings-part-3">
    Adjust Agent Temperature
  </Card>

  <Card title="Starter Questions" icon="message" href="#starter-questions">
    Define Starter Prompts
  </Card>

  <Card title="Query Parameters" icon="sliders" href="#query-parameters">
    Pass Contextual Inputs to Agent
  </Card>

  <Card title="Semantic Similarity Score" icon="scale" href="#semantic-search-similarity">
    Fine-Tune Result Relevance
  </Card>
</CardGroup>

***

## Agent Settings: Part 1

Configure the base models for your agent.

* Navigate to **Agent Settings**
* Update the **Agent name** (if needed)
* Choose your **Chat model**
* Select an **Embedding model**
* Save the settings

<Frame>
  <img alt="Agent Settings Part 1" />
</Frame>

***

## Agent Settings: Part 2 – Prompts & Library

### ✅ Configure Prompt Behavior

* **Prompt for Own Data**: Agent only responds using provided data. No hallucinations — replies with *“I don’t know”* if unsure.
* **Prompt for Web Data**: Good for standalone, follow-up questions without context.
* **Prompt Library**: Use system-defined prompts for use cases like Interviewer, Educator, or Assistant.

<Frame>
  <img alt="Prompt Configuration" />
</Frame>

Let’s now look at how to explore and apply these predefined prompts.

***

### Using the Prompt Library

<Steps>
  <Step title="Browse and Apply Predefined Prompts">
    Click **Prompt lib** to access the built-in library of system prompts.\
    Select a category that matches your agent's purpose — e.g., Position Interviewer, Storyteller, FAQ Agent.

    <Frame>
      <img alt="Prompt Library View" />
    </Frame>
  </Step>

  <Step title="Preview and Confirm Selection">
    After selecting a prompt, preview its description and sample usage.\
    Click **Use Prompt** to confirm and apply it to your agent.

    <Frame>
      <img alt="Prompt Confirmation Dialog" />
    </Frame>
  </Step>
</Steps>

***

## Agent Settings: Part 3 - Temperature

Control the creativity or precision of your agent responses:

* `0.0 – 0.3`: Very accurate and focused
* `0.4 – 0.7`: Balanced response generation
* `0.8 – 1.0`: More imaginative and open-ended

<Frame>
  <img alt="Temperature Setting" />
</Frame>

***

## Starter Questions

Let users quickly start conversations with predefined questions.

### How to Add Starter Questions

* Go to **Agent Settings → Starter Questions**
* Add up to 4 questions relevant to your agent
* Click **Add** and then **Save**

<Frame>
  <img alt="Starter Config" />
</Frame>

Add up to 4 questions relevant to your agent:

<Frame>
  <img alt="Starter Config 2" />
</Frame>

To preview how your saved starter questions look in your console and user experience:

<Frame>
  <img alt="Playground View with Starter Questions" />
</Frame>

***

## Query Parameters

Query parameters allow you to inject dynamic data — like user ID, department, or context — directly into your agent’s logic.

### How to Add Query Parameters

* Go to **Agent Settings**
* Click **Add Query Parameter**
* Provide a name and reference in your prompt
* Save the changes

<Frame>
  <img alt="Add Query Param" />
</Frame>

Then, test the parameter live in the Playground UI:

<Frame>
  <img alt="Param Visible to User" />
</Frame>

***

## 🎯 Semantic Search Similarity

Control how precisely the agent matches your knowledge base.

### Relevance Thresholds

* `≥ 0.2`: Broad, flexible match
* `≥ 0.5`: Moderate contextual relevance
* `≥ 0.7`: Strict precision

Change it under **Agent Settings** with a simple dropdown.

<Frame>
  <img alt="Semantic Score Slider" />
</Frame>

***

## 📩 Lead Collection

You can now enable lead collection directly from the **Settings** panel.

### How it Works

* When **Enable Lead Collection** is turned on, a contact form will be shown inside the agent widget.
* This form gathers user information like name, email, and optional notes.
* It helps convert casual visitors into potential leads or customers.

<Frame>
  <img alt="Lead Collection Toggle" />
</Frame>

<Note>
  Lead collection is a powerful way to capture qualified interest and convert chat interactions into actionable sales or support opportunities.
</Note>

For more details about CRM integration and how collected leads sync with **HubSpot**, visit the [Lead Collection page](/ai-bots-console/leads).

<Check>
  Configure these settings thoughtfully to ensure your agent delivers helpful, relevant, and contextually aware responses every time.
</Check>


# Usage Analytics
Source: https://docs.aptlystar.ai/ai-bots-console/usageanalytics



The **Usage Analytics** section gives you a breakdown of how your AI agent is being used across different channels and platforms. It helps you monitor engagement, identify high-traffic sources, and make informed decisions about where to focus improvements or expansion.

This page shows the total number of **questions asked** and **answers delivered** by your agent, grouped by the source of interaction.

***

## 📊 Available Analytics Categories

Each card on this page tracks a specific usage type:

* **Playground Message**: Interactions from the internal Aptly agent playground.
* **Web History**: Messages exchanged via the embedded widget on your website.
* **WhatsApp History**: Agent usage data from WhatsApp integration.
* **Discord History**: All queries received via your Discord channel.
* **Telegram History**: Logs of messages exchanged through the Telegram agent.
* **API History**: Tracks calls made to your agent via API.

<Frame>
  <img alt="Usage Analytics Dashboard" />
</Frame>

***

## 🔍 Why Usage Analytics Matter

Here’s how this page helps you make smarter decisions:

* **Track performance**: Know where your agent is most active.
* **Identify adoption trends**: Compare traction across platforms like Discord vs WhatsApp.
* **Optimize support**: Focus improvements on channels with the most traffic.
* **Monitor effectiveness**: Spot if answers match up to queries across sources.

> Note: Data here reflects only **successful message delivery** — failed interactions aren’t counted.

***

## 🛠️ Enabling Accurate Analytics

To ensure this dashboard collects data:

* Your agent must be **Live** (toggle must be switched on).
* Channels must be properly set up in the [Channels tab](https://docs.aptlystar.ai/ai-bots-console/channels).
* The agent should be actively engaged by users via any of the platforms above.

***


# API to fetch avialable model from inputed url
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-fetch-avialable-model-from-inputed-url

api-reference/openapi-1.json post /api/v1/admin/models/fetch



# API to get all models
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-get-all-models

api-reference/openapi-1.json get /api/v1/admin/models



# API to get all users
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-get-all-users

api-reference/openapi-1.json get /api/v1/admin/users



# API to get models by Org Id
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-get-models-by-org-id

api-reference/openapi-1.json get /api/v1/admin/{org_id}/models/getModelsListNotInChatModels



# API to get StarFish settings
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-get-starfish-settings

api-reference/openapi-1.json get /api/v1/admin/starfish-settings



# API to register user by admin
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-register-user-by-admin

api-reference/openapi-1.json post /api/v1/admin/register-user



# API to reset user password by admin
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-reset-user-password-by-admin

api-reference/openapi-1.json post /api/v1/admin/reset-user-password



# API to save embedding model from inputed url
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-save-embedding-model-from-inputed-url

api-reference/openapi-1.json post /api/v1/admin/models/embedding



# API to save model from inputed url
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-save-model-from-inputed-url

api-reference/openapi-1.json post /api/v1/admin/models



# API to toogle model
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-toogle-model

api-reference/openapi-1.json post /api/v1/admin/models/delete



# API to toogle model
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-toogle-model-1

api-reference/openapi-1.json post /api/v1/admin/models/hide



# API to update StarFish RAG settings
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-update-starfish-rag-settings

api-reference/openapi-1.json post /api/v1/admin/rag-settings



# API to update StarFish settings
Source: https://docs.aptlystar.ai/api-reference/admin/api-to-update-starfish-settings

api-reference/openapi-1.json post /api/v1/admin/starfish-settings



# API to create bot integration
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-create-bot-integration

api-reference/openapi-1.json post /api/v1/bot/integration/{id}



# API to create new bot based on project
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-create-new-bot-based-on-project

api-reference/openapi-1.json post /api/v1/bot/org/{org_id}/project/{project_id}/createBot



# API to delete bot playground by id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-delete-bot-playground-by-id

api-reference/openapi-1.json delete /api/v1/bot/playground/history/{id}



# API to generate API key for bot integration
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-generate-api-key-for-bot-integration

api-reference/openapi-1.json post /api/v1/bot/integration/{id}/api



# API to get all bots
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-all-bots

api-reference/openapi-1.json get /api/v1/bot/



# API to get API key for bot integration
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-api-key-for-bot-integration

api-reference/openapi-1.json get /api/v1/bot/integration/{id}/api



# API to get audio settings from bot on playground
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-audio-settings-from-bot-on-playground

api-reference/openapi-1.json post /api/v1/bot/playground/{id}/voice



# API to get bot appearance by id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-bot-appearance-by-id

api-reference/openapi-1.json get /api/v1/bot/appearance/{id}



# API to get bot config by organization id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-bot-config-by-organization-id

api-reference/openapi-1.json get /api/v1/bot/config



# API to get feedbacks by bot id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-feedbacks-by-bot-id

api-reference/openapi-1.json get /api/v1/bot/org/{org_id}/bot/{id}/getBotFeedbacks



# API to get history of message from bot on playground
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-history-of-message-from-bot-on-playground

api-reference/openapi-1.json get /api/v1/bot/playground/{id}/history



# API to get history of message from bot on playground by history id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-history-of-message-from-bot-on-playground-by-history-id

api-reference/openapi-1.json get /api/v1/bot/playground/{id}/history/{history_id}



# API to get prompts by bot id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-prompts-by-bot-id

api-reference/openapi-1.json get /api/v1/marketplace/{org_id}/prompts



# API to get prompts by bot id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-prompts-by-bot-id-1

api-reference/openapi-1.json get /api/v1/bot/{id}/prompts



# API to get stream of message from bot on playground
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-get-stream-of-message-from-bot-on-playground

api-reference/openapi-1.json post /api/v1/bot/playground/{id}/stream



# API to pause or resume bot integration
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-pause-or-resume-bot-integration

api-reference/openapi-1.json post /api/v1/bot/integration/{id}/toggle



# API to regenerate API key for bot integration
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-regenerate-api-key-for-bot-integration

api-reference/openapi-1.json put /api/v1/bot/integration/{id}/api



# API to save bot appearance by id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-save-bot-appearance-by-id

api-reference/openapi-1.json post /api/v1/bot/appearance/{id}



# API to send message to bot
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-send-message-to-bot

api-reference/openapi-1.json post /api/v1/bot/{id}/chat



# API to send message to bot on playground
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-send-message-to-bot-on-playground

api-reference/openapi-1.json post /api/v1/bot/playground/{id}



# API to update bot playground title by id
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-update-bot-playground-title-by-id

api-reference/openapi-1.json put /api/v1/bot/playground/history/{id}



# API to upload files
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-upload-files

api-reference/openapi-1.json post /api/v1/bot/upload



# API to upload files
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-upload-files-1

api-reference/openapi-1.json post /api/v1/bot/{id}/source/upload



# API to upload files as source
Source: https://docs.aptlystar.ai/api-reference/bot/api-to-upload-files-as-source

api-reference/openapi-1.json post /api/v1/bot/{id}/source/upload/bulk



# Delete apiv1bot
Source: https://docs.aptlystar.ai/api-reference/bot/delete-apiv1bot

api-reference/openapi-1.json delete /api/v1/bot/{id}



# Delete apiv1bot source
Source: https://docs.aptlystar.ai/api-reference/bot/delete-apiv1bot-source

api-reference/openapi-1.json delete /api/v1/bot/{id}/source/{sourceId}



# Get all bot channels
Source: https://docs.aptlystar.ai/api-reference/bot/get-all-bot-channels

api-reference/openapi-1.json get /api/v1/bot/integration/{id}



# Get apiv1bot
Source: https://docs.aptlystar.ai/api-reference/bot/get-apiv1bot

api-reference/openapi-1.json get /api/v1/bot/{id}



# Get apiv1bot embed
Source: https://docs.aptlystar.ai/api-reference/bot/get-apiv1bot-embed

api-reference/openapi-1.json get /api/v1/bot/{id}/embed



# Get apiv1bot is ready
Source: https://docs.aptlystar.ai/api-reference/bot/get-apiv1bot-is-ready

api-reference/openapi-1.json get /api/v1/bot/{id}/is-ready



# Get apiv1bot settings
Source: https://docs.aptlystar.ai/api-reference/bot/get-apiv1bot-settings

api-reference/openapi-1.json get /api/v1/bot/{id}/settings



# Get apiv1bot source
Source: https://docs.aptlystar.ai/api-reference/bot/get-apiv1bot-source

api-reference/openapi-1.json get /api/v1/bot/{id}/source



# Get apiv1botorg project agents
Source: https://docs.aptlystar.ai/api-reference/bot/get-apiv1botorg-project-agents

api-reference/openapi-1.json get /api/v1/bot/org/{org_id}/project/{project_id}/agents



# Get chat history by chat id
Source: https://docs.aptlystar.ai/api-reference/bot/get-chat-history-by-chat-id

api-reference/openapi-1.json get /api/v1/bot/conversations/{id}/{type}/{chat_id}



# Get chat history by intergation type
Source: https://docs.aptlystar.ai/api-reference/bot/get-chat-history-by-intergation-type

api-reference/openapi-1.json get /api/v1/bot/conversations/{id}/{type}



# Post apiv1bot
Source: https://docs.aptlystar.ai/api-reference/bot/post-apiv1bot

api-reference/openapi-1.json post /api/v1/bot/



# Post apiv1bot copy
Source: https://docs.aptlystar.ai/api-reference/bot/post-apiv1bot-copy

api-reference/openapi-1.json post /api/v1/bot/{bot_id}/copy



# Post apiv1bot source
Source: https://docs.aptlystar.ai/api-reference/bot/post-apiv1bot-source

api-reference/openapi-1.json post /api/v1/bot/{id}/source



# Post apiv1bot source refresh
Source: https://docs.aptlystar.ai/api-reference/bot/post-apiv1bot-source-refresh

api-reference/openapi-1.json post /api/v1/bot/{id}/source/{sourceId}/refresh



# Post apiv1bot sourcebulk
Source: https://docs.aptlystar.ai/api-reference/bot/post-apiv1bot-sourcebulk

api-reference/openapi-1.json post /api/v1/bot/{id}/source/bulk



# Post apiv1botapi
Source: https://docs.aptlystar.ai/api-reference/bot/post-apiv1botapi

api-reference/openapi-1.json post /api/v1/bot/api



# Provide feedback for bot answer
Source: https://docs.aptlystar.ai/api-reference/bot/provide-feedback-for-bot-answer

api-reference/openapi-1.json put /api/v1/bot/playground/org/{org_id}/project/{project_id}/bot/{bot_id}/history/{history_id}/botMessage/{answerMessage_id}



# Put apiv1bot
Source: https://docs.aptlystar.ai/api-reference/bot/put-apiv1bot

api-reference/openapi-1.json put /api/v1/bot/{id}



# Put apiv1bot password
Source: https://docs.aptlystar.ai/api-reference/bot/put-apiv1bot-password

api-reference/openapi-1.json put /api/v1/bot/{id}/password



# Put apiv1bot source
Source: https://docs.aptlystar.ai/api-reference/bot/put-apiv1bot-source

api-reference/openapi-1.json put /api/v1/bot/{id}/source/{sourceId}



# Put apiv1bot update
Source: https://docs.aptlystar.ai/api-reference/bot/put-apiv1bot-update

api-reference/openapi-1.json put /api/v1/bot/{id}/update



# Add data source ( file type only) into datastore
Source: https://docs.aptlystar.ai/api-reference/datastore/add-data-source-file-type-only-into-datastore

api-reference/openapi-1.json post /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/addDataSourceFileUpload



# Add data source into datastore
Source: https://docs.aptlystar.ai/api-reference/datastore/add-data-source-into-datastore

api-reference/openapi-1.json post /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/addDataSource



# Add or remove data source from datastore knowledge corpus
Source: https://docs.aptlystar.ai/api-reference/datastore/add-or-remove-data-source-from-datastore-knowledge-corpus

api-reference/openapi-1.json put /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/dataSource/{dataSource_id}/removeOrAddDataSourceFromDataStoreKnowledgeCorpus



# API to add datastore to bot
Source: https://docs.aptlystar.ai/api-reference/datastore/api-to-add-datastore-to-bot

api-reference/openapi-1.json post /api/v1/dataStore/{org_id}/dataStore/{bot_id}/addDataStoreToBot



# API to get bots and data source count by organization id and DataStore id
Source: https://docs.aptlystar.ai/api-reference/datastore/api-to-get-bots-and-data-source-count-by-organization-id-and-datastore-id

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/getBotsAndDataSourceCountByDataStoreId



# API to update existing data store detail
Source: https://docs.aptlystar.ai/api-reference/datastore/api-to-update-existing-data-store-detail

api-reference/openapi-1.json put /api/v1/dataStore/{org_id}/dataStores/{dataStore_id}/update



# Delete data source from Data Source List
Source: https://docs.aptlystar.ai/api-reference/datastore/delete-data-source-from-data-source-list

api-reference/openapi-1.json delete /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/dataSource/{dataSource_id}/delete



# Delete data store that added to bot 
Source: https://docs.aptlystar.ai/api-reference/datastore/delete-data-store-that-added-to-bot

api-reference/openapi-1.json delete /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/bot_id/{bot_id}/delete



# Get Data sources list by org id and datastore id
Source: https://docs.aptlystar.ai/api-reference/datastore/get-data-sources-list-by-org-id-and-datastore-id

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/getDataSourcesList



# Get Data sources list by org id and not eqaul to datastore id
Source: https://docs.aptlystar.ai/api-reference/datastore/get-data-sources-list-by-org-id-and-not-eqaul-to-datastore-id

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/getDataSourcesListNotInDataStore



# Get datastore overview details by org id and datastore id
Source: https://docs.aptlystar.ai/api-reference/datastore/get-datastore-overview-details-by-org-id-and-datastore-id

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/getDatStoreOverview



# Get Datastores list by org id and  bot id
Source: https://docs.aptlystar.ai/api-reference/datastore/get-datastores-list-by-org-id-and-bot-id

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/bot/{bot_id}/getDataStoresListAddedIntoBot



# Get Datastores list by org id and embedding model
Source: https://docs.aptlystar.ai/api-reference/datastore/get-datastores-list-by-org-id-and-embedding-model

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/dataStore/{embedding_model}/getDataStoresListByOrgIdAndEmbeddingModel



# Get Datastores list by org id and not eqaul to bot id
Source: https://docs.aptlystar.ai/api-reference/datastore/get-datastores-list-by-org-id-and-not-eqaul-to-bot-id

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/dataStore/{bot_id}/getDataStoresListByOrgIdAndNotAddedToTheBot



# Get the content of  data source by data source id
Source: https://docs.aptlystar.ai/api-reference/datastore/get-the-content-of-data-source-by-data-source-id

api-reference/openapi-1.json get /api/v1/dataStore/{org_id}/dataStore/{dataStore_id}/dataSource/{dataSource_id}/getEmbeddingContentByDataSourceIdAndOrgId



# Update chunk status and content
Source: https://docs.aptlystar.ai/api-reference/datastore/update-chunk-status-and-content

api-reference/openapi-1.json put /api/v1/dataStore/{org_id}/dataSource/{dataSource_id}/id/{id}/updateEmbeddingContentData



# Delete apiv1aptlyteam
Source: https://docs.aptlystar.ai/api-reference/delete-apiv1aptlyteam

api-reference/openapi-1.json delete /api/v1/aptlyteam/{id}



# Delete apiv1aptlyteampermission
Source: https://docs.aptlystar.ai/api-reference/delete-apiv1aptlyteampermission

api-reference/openapi-1.json delete /api/v1/aptlyteam/permission/{id}



# Delete apiv1aptlyteamrole
Source: https://docs.aptlystar.ai/api-reference/delete-apiv1aptlyteamrole

api-reference/openapi-1.json delete /api/v1/aptlyteam/role/{id}



# Delete apiv1aptlyteamrole permission detail
Source: https://docs.aptlystar.ai/api-reference/delete-apiv1aptlyteamrole-permission-detail

api-reference/openapi-1.json delete /api/v1/aptlyteam/role-permission-detail/{id}



# Delete apiv1org chatmodelsonorg
Source: https://docs.aptlystar.ai/api-reference/delete-apiv1org-chatmodelsonorg

api-reference/openapi-1.json delete /api/v1/org/{org_id}/chatModelsOnOrg/{id}



# Get apiv1adminadmin details by userid
Source: https://docs.aptlystar.ai/api-reference/get-apiv1adminadmin-details-by-userid

api-reference/openapi-1.json get /api/v1/admin/admin-details-by-userid



# Get apiv1aptlyteamall
Source: https://docs.aptlystar.ai/api-reference/get-apiv1aptlyteamall

api-reference/openapi-1.json get /api/v1/aptlyteam/all



# Get apiv1aptlyteambotslist
Source: https://docs.aptlystar.ai/api-reference/get-apiv1aptlyteambotslist

api-reference/openapi-1.json get /api/v1/aptlyteam/botsList



# Get apiv1aptlyteaminfo
Source: https://docs.aptlystar.ai/api-reference/get-apiv1aptlyteaminfo

api-reference/openapi-1.json get /api/v1/aptlyteam/info



# Get apiv1aptlyteampermission
Source: https://docs.aptlystar.ai/api-reference/get-apiv1aptlyteampermission

api-reference/openapi-1.json get /api/v1/aptlyteam/permission



# Get apiv1aptlyteamrole
Source: https://docs.aptlystar.ai/api-reference/get-apiv1aptlyteamrole

api-reference/openapi-1.json get /api/v1/aptlyteam/role



# Get apiv1aptlyteamrole permission detail
Source: https://docs.aptlystar.ai/api-reference/get-apiv1aptlyteamrole-permission-detail

api-reference/openapi-1.json get /api/v1/aptlyteam/role-permission-detail/{id}



# Get apiv1oauthgithub
Source: https://docs.aptlystar.ai/api-reference/get-apiv1oauthgithub

api-reference/openapi-1.json get /api/v1/oauth/github



# Get apiv1oauthgithubcallback
Source: https://docs.aptlystar.ai/api-reference/get-apiv1oauthgithubcallback

api-reference/openapi-1.json get /api/v1/oauth/github/callback



# Get apiv1oauthmicrosoft
Source: https://docs.aptlystar.ai/api-reference/get-apiv1oauthmicrosoft

api-reference/openapi-1.json get /api/v1/oauth/microsoft



# Get apiv1oauthmicrosoftcallback
Source: https://docs.aptlystar.ai/api-reference/get-apiv1oauthmicrosoftcallback

api-reference/openapi-1.json get /api/v1/oauth/microsoft/callback



# Get apiv1org getaimodelsoforg
Source: https://docs.aptlystar.ai/api-reference/get-apiv1org-getaimodelsoforg

api-reference/openapi-1.json get /api/v1/org/{org_id}/getAIModelsoforg



# Get apiv1orgmy organization
Source: https://docs.aptlystar.ai/api-reference/get-apiv1orgmy-organization

api-reference/openapi-1.json get /api/v1/org/my-organization



# Get apiv1pricingmy payment
Source: https://docs.aptlystar.ai/api-reference/get-apiv1pricingmy-payment

api-reference/openapi-1.json get /api/v1/pricing/my-payment



# Get apiv1pricingplans
Source: https://docs.aptlystar.ai/api-reference/get-apiv1pricingplans

api-reference/openapi-1.json get /api/v1/pricing/plans



# Get apiv1userreset password
Source: https://docs.aptlystar.ai/api-reference/get-apiv1userreset-password

api-reference/openapi-1.json get /api/v1/user/reset-password



# Get apiv1useruser info
Source: https://docs.aptlystar.ai/api-reference/get-apiv1useruser-info

api-reference/openapi-1.json get /api/v1/user/user-info



# Get apiv1userverify email
Source: https://docs.aptlystar.ai/api-reference/get-apiv1userverify-email

api-reference/openapi-1.json get /api/v1/user/verify-email/{key}



# API to add chat models to organization
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-add-chat-models-to-organization

api-reference/openapi-1.json post /api/v1/org/{org_id}/addModelsToOrg/{modelType}



# API to add new project
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-add-new-project

api-reference/openapi-1.json post /api/v1/org/{org_id}/projects/create



# API to create new data store
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-create-new-data-store

api-reference/openapi-1.json post /api/v1/org/{org_id}/dataStores/create



# API to create new Obstralog Instance for organization
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-create-new-obstralog-instance-for-organization

api-reference/openapi-1.json post /api/v1/org/{org_id}/obstralogInstance/create



# API to create new user
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-create-new-user

api-reference/openapi-1.json post /api/v1/org/{org_id}/users/create



# API to delete user on organization
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-delete-user-on-organization

api-reference/openapi-1.json delete /api/v1/org/{org_id}/users/{id}



# API to get admin users by organization id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-admin-users-by-organization-id

api-reference/openapi-1.json get /api/v1/org/{org_id}/user/admins



# API to get counts of projects, teams, users, and bots on the organization
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-counts-of-projects-teams-users-and-bots-on-the-organization

api-reference/openapi-1.json get /api/v1/org/{org_id}/counts



# API to get data stores by organization id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-data-stores-by-organization-id

api-reference/openapi-1.json get /api/v1/org/{org_id}/dataStores



# API to get data stores size by organization id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-data-stores-size-by-organization-id

api-reference/openapi-1.json get /api/v1/org/{org_id}/{dataStore_id}/dataStoresSize



# API to get obstralog instance details by org id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-obstralog-instance-details-by-org-id

api-reference/openapi-1.json get /api/v1/org/{org_id}/getObstralogInstanceDetails



# API to get organization by id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-organization-by-id

api-reference/openapi-1.json get /api/v1/org/{org_id}



# API to get organization by id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-organization-by-id-1

api-reference/openapi-1.json get /api/v1/org/{org_id}/public



# API to get Organization Details by organization id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-organization-details-by-organization-id

api-reference/openapi-1.json get /api/v1/org/{org_id}/getOrgInfo



# API to get projects by organization id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-projects-by-organization-id

api-reference/openapi-1.json get /api/v1/org/{org_id}/project



# API to get users by invitation key
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-users-by-invitation-key

api-reference/openapi-1.json get /api/v1/org/invitation/{invitationVerificationKey}



# API to get users by organization id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-get-users-by-organization-id

api-reference/openapi-1.json get /api/v1/org/{org_id}/user



# API to update activeStatus of existing user
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-update-activestatus-of-existing-user

api-reference/openapi-1.json put /api/v1/org/{org_id}/users/{id}/update



# API to update existing project
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-update-existing-project

api-reference/openapi-1.json put /api/v1/project/{org_id}/project/{project_id}/update



# API to update IsWorkEmailBasedInvitations by org id
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-update-isworkemailbasedinvitations-by-org-id

api-reference/openapi-1.json put /api/v1/org/{org_id}/updateIsWorkEmailBasedInvitations



# API to update org details of user
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-update-org-details-of-user

api-reference/openapi-1.json put /api/v1/org/{org_id}/update



# API to update user role on organization
Source: https://docs.aptlystar.ai/api-reference/organization/api-to-update-user-role-on-organization

api-reference/openapi-1.json put /api/v1/org/{org_id}/users/{id}



# Get subscribed details by organization ID
Source: https://docs.aptlystar.ai/api-reference/plan/get-subscribed-details-by-organization-id

api-reference/openapi-1.json get /api/v1/pricing/subscribed-details/{org_id}/subscribedPlanDetails



# Post apiv1adminsendloginremindermessage
Source: https://docs.aptlystar.ai/api-reference/post-apiv1adminsendloginremindermessage

api-reference/openapi-1.json post /api/v1/admin/sendLoginReminderMessage



# Post apiv1admintoggle account status
Source: https://docs.aptlystar.ai/api-reference/post-apiv1admintoggle-account-status

api-reference/openapi-1.json post /api/v1/admin/toggle-account-status



# Post apiv1aptlyteamadd
Source: https://docs.aptlystar.ai/api-reference/post-apiv1aptlyteamadd

api-reference/openapi-1.json post /api/v1/aptlyteam/add



# Post apiv1aptlyteampermissionadd
Source: https://docs.aptlystar.ai/api-reference/post-apiv1aptlyteampermissionadd

api-reference/openapi-1.json post /api/v1/aptlyteam/permission/add



# Post apiv1aptlyteamrole permission detail
Source: https://docs.aptlystar.ai/api-reference/post-apiv1aptlyteamrole-permission-detail

api-reference/openapi-1.json post /api/v1/aptlyteam/role-permission-detail/{id}



# Post apiv1aptlyteamroleadd
Source: https://docs.aptlystar.ai/api-reference/post-apiv1aptlyteamroleadd

api-reference/openapi-1.json post /api/v1/aptlyteam/role/add



# Post apiv1aptlyteamteam register
Source: https://docs.aptlystar.ai/api-reference/post-apiv1aptlyteamteam-register

api-reference/openapi-1.json post /api/v1/aptlyteam/team-register



# Post apiv1aptlyteamteamlogin
Source: https://docs.aptlystar.ai/api-reference/post-apiv1aptlyteamteamlogin

api-reference/openapi-1.json post /api/v1/aptlyteam/teamlogin



# Post apiv1oauthgooglecallback
Source: https://docs.aptlystar.ai/api-reference/post-apiv1oauthgooglecallback

api-reference/openapi-1.json post /api/v1/oauth/google/callback



# Post apiv1pricingsubscribe
Source: https://docs.aptlystar.ai/api-reference/post-apiv1pricingsubscribe

api-reference/openapi-1.json post /api/v1/pricing/subscribe



# Post apiv1stripewebhook
Source: https://docs.aptlystar.ai/api-reference/post-apiv1stripewebhook

api-reference/openapi-1.json post /api/v1/stripe/webhook



# Post apiv1userforgot password
Source: https://docs.aptlystar.ai/api-reference/post-apiv1userforgot-password

api-reference/openapi-1.json post /api/v1/user/forgot-password



# Post apiv1userregisterwithinvitation
Source: https://docs.aptlystar.ai/api-reference/post-apiv1userregisterwithinvitation

api-reference/openapi-1.json post /api/v1/user/registerWithInvitation/{invitationVerificationKey}



# Post apiv1userreset password
Source: https://docs.aptlystar.ai/api-reference/post-apiv1userreset-password

api-reference/openapi-1.json post /api/v1/user/reset-password



# API to add new team into the project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-add-new-team-into-the-project

api-reference/openapi-1.json post /api/v1/project/{org_id}/{project_id}/createNewTeamOnProject



# API to add users to the team of the project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-add-users-to-the-team-of-the-project

api-reference/openapi-1.json post /api/v1/project/{org_id}/{project_id}/{team_id}/addUsersToTheTeam



# API to create new Obstralog Instance for project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-create-new-obstralog-instance-for-project

api-reference/openapi-1.json post /api/v1/project/{org_id}/{project_id}/createObstralogInstance



# API to delete team from this project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-delete-team-from-this-project

api-reference/openapi-1.json delete /api/v1/project/{org_id}/{project_id}/{team_id}/deleteTeamFromProject



# API to delete user from team of project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-delete-user-from-team-of-project

api-reference/openapi-1.json delete /api/v1/project/{org_id}/{project_id}/{team_id}/{userid}/deleteUserFromTeam



# API to delete users from the project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-delete-users-from-the-project

api-reference/openapi-1.json post /api/v1/project/{org_id}/{project_id}/deleteUsersFromTheProject



# API to get all users from this project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-get-all-users-from-this-project

api-reference/openapi-1.json get /api/v1/project/{org_id}/{project_id}/getUsersOnProject



# API to get bots count by organization id and project id
Source: https://docs.aptlystar.ai/api-reference/project/api-to-get-bots-count-by-organization-id-and-project-id

api-reference/openapi-1.json get /api/v1/project/{org_id}/{project_id}/getBotsCountByProjectId



# API to get obstralog instance details by organization id and project id
Source: https://docs.aptlystar.ai/api-reference/project/api-to-get-obstralog-instance-details-by-organization-id-and-project-id

api-reference/openapi-1.json get /api/v1/project/{org_id}/{project_id}/getObstralogInstanceDetailsByProjectId



# API to get teams list by organization id and project id
Source: https://docs.aptlystar.ai/api-reference/project/api-to-get-teams-list-by-organization-id-and-project-id

api-reference/openapi-1.json get /api/v1/project/{org_id}/{project_id}/getTeamsListOnProject



# API to get users list added to the team of project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-get-users-list-added-to-the-team-of-project

api-reference/openapi-1.json get /api/v1/project/{org_id}/{project_id}/{team_id}/getUsersListAddedToTheTeam



# API to get users list added to the team of project
Source: https://docs.aptlystar.ai/api-reference/project/api-to-get-users-list-added-to-the-team-of-project-1

api-reference/openapi-1.json get /api/v1/project/{org_id}/{project_id}/{team_id}/getUsersListToAddToTheTeam



# Get project overview details by org id and project id
Source: https://docs.aptlystar.ai/api-reference/project/get-project-overview-details-by-org-id-and-project-id

api-reference/openapi-1.json get /api/v1/project/{org_id}/{project_id}/getProjectOverview



# Put apiv1aptlyteam
Source: https://docs.aptlystar.ai/api-reference/put-apiv1aptlyteam

api-reference/openapi-1.json put /api/v1/aptlyteam/{id}



# Put apiv1aptlyteampermission
Source: https://docs.aptlystar.ai/api-reference/put-apiv1aptlyteampermission

api-reference/openapi-1.json put /api/v1/aptlyteam/permission/{id}



# Put apiv1aptlyteamrole
Source: https://docs.aptlystar.ai/api-reference/put-apiv1aptlyteamrole

api-reference/openapi-1.json put /api/v1/aptlyteam/role/{id}



# API to check if registeration is allowed
Source: https://docs.aptlystar.ai/api-reference/user/api-to-check-if-registeration-is-allowed

api-reference/openapi-1.json get /api/v1/user/info



# API to get user overview by organization id and user id
Source: https://docs.aptlystar.ai/api-reference/user/api-to-get-user-overview-by-organization-id-and-user-id

api-reference/openapi-1.json get /api/v1/user/{org_id}/{user_id}/getUserOverview



# API to get user team details by organization id and user id
Source: https://docs.aptlystar.ai/api-reference/user/api-to-get-user-team-details-by-organization-id-and-user-id

api-reference/openapi-1.json get /api/v1/user/{org_id}/{user_id}/getUserTeamDetails



# API to login user
Source: https://docs.aptlystar.ai/api-reference/user/api-to-login-user

api-reference/openapi-1.json post /api/v1/user/login



# API to login user
Source: https://docs.aptlystar.ai/api-reference/user/api-to-login-user-1

api-reference/openapi-1.json post /api/v1/user/feedback



# API to register user
Source: https://docs.aptlystar.ai/api-reference/user/api-to-register-user

api-reference/openapi-1.json post /api/v1/user/register



# API to update user password
Source: https://docs.aptlystar.ai/api-reference/user/api-to-update-user-password

api-reference/openapi-1.json post /api/v1/user/update-password



# API to update user profile
Source: https://docs.aptlystar.ai/api-reference/user/api-to-update-user-profile

api-reference/openapi-1.json post /api/v1/user/me



# Check if user is admin
Source: https://docs.aptlystar.ai/api-reference/user/check-if-user-is-admin

api-reference/openapi-1.json get /api/v1/user/is-admin



# Create new api key
Source: https://docs.aptlystar.ai/api-reference/user/create-new-api-key

api-reference/openapi-1.json post /api/v1/user/api-key



# Delete api key
Source: https://docs.aptlystar.ai/api-reference/user/delete-api-key

api-reference/openapi-1.json delete /api/v1/user/api-key/{id}



# Get all api keys by user
Source: https://docs.aptlystar.ai/api-reference/user/get-all-api-keys-by-user

api-reference/openapi-1.json get /api/v1/user/api-key



# Get the users by filtering inactiveDays and pagination
Source: https://docs.aptlystar.ai/api-reference/user/get-the-users-by-filtering-inactivedays-and-pagination

api-reference/openapi-1.json get /api/v1/user/



# Get user profile
Source: https://docs.aptlystar.ai/api-reference/user/get-user-profile

api-reference/openapi-1.json get /api/v1/user/me



# Update user profile
Source: https://docs.aptlystar.ai/api-reference/user/update-user-profile

api-reference/openapi-1.json post /api/v1/user/update-user



# API to get voice from text
Source: https://docs.aptlystar.ai/api-reference/voice/api-to-get-voice-from-text

api-reference/openapi-1.json post /api/v1/voice/11labs/tts



# API to get stream of message from bot
Source: https://docs.aptlystar.ai/api-reference/widget/api-to-get-stream-of-message-from-bot

api-reference/openapi-1.json post /bot/{id}/stream



# API to get style of widget
Source: https://docs.aptlystar.ai/api-reference/widget/api-to-get-style-of-widget

api-reference/openapi-1.json get /bot/{id}/style



# API to get TTS of message
Source: https://docs.aptlystar.ai/api-reference/widget/api-to-get-tts-of-message

api-reference/openapi-1.json post /bot/{id}/tts



# API to login bot
Source: https://docs.aptlystar.ai/api-reference/widget/api-to-login-bot

api-reference/openapi-1.json post /bot/{id}/login



# API to send message to bot
Source: https://docs.aptlystar.ai/api-reference/widget/api-to-send-message-to-bot

api-reference/openapi-1.json post /bot/{id}



# Public API to send message to bot on widget
Source: https://docs.aptlystar.ai/api-reference/widget/public-api-to-send-message-to-bot-on-widget

api-reference/openapi-1.json post /bot/{id}/api



# API to get available transcript from youtube video url
Source: https://docs.aptlystar.ai/api-reference/youtube/api-to-get-available-transcript-from-youtube-video-url

api-reference/openapi-1.json get /api/v1/yt/transcript



# Product updates
Source: https://docs.aptlystar.ai/changelog



<Update label="December 2025">
  ## ✨ [Platform Experience](/)

  * Improved navigation consistency across organization, project, and agent views.
  * Refined dropdowns, filters, and tables for more predictable interactions.
  * Enhanced empty states and loading feedback for clearer user guidance.

  ## 🧠 [Agents: Experience & Reliability](/ai-bots-console/canvas)

  * Improved agent session handling, visibility, and history consistency.
  * Enhanced feedback and retry flows for real-world usage scenarios.
  * Fixed multiple edge cases across live and embedded environments.

  ## 🤖 [Astra & Assisted Workflows](/)

  * Improved Astra reliability across canvas and widget contexts.
  * Enhanced human-in-the-loop handling and assisted interaction clarity.

  ## 📊 [Analytics & Telemetry](/oraganization-console/usage-analytics)

  * Improved accuracy and stability of usage analytics and charts.
  * Refined pagination, filtering, and historical data visibility.

  ## 📚 [AI Datastores](/data-stores-console/overview)

  * Improved datastore validation, loading behavior, and error handling.
  * Refined datastore list and detail views for better consistency.

  ## 👥 [Team & Access Management](/project-console/teams)

  * Improved permission handling and visibility across projects and agents.
  * Fixed inconsistencies in team updates and member assignments.

  ## ⚙️ [Settings & Console Improvements](/oraganization-console/settings)

  * Polished UI across settings, billing, and profile flows.
  * Expanded dark mode coverage and improved inline feedback.

  ## 🔧 Quality & Stability

  * Fixed a wide range of UI inconsistencies and interaction edge cases.
  * Improved overall reliability, performance, and visual consistency.
</Update>

<Update label="November 2025">
  ## [Agents: Creation & Intelligence](/howtocreateaiagent#creating-agents-with-ask-astra)

  * Added **Ask Astra** prompt based agent creation with:
    * Suggested prompts
    * Shuffle suggestions
    * Custom prompt option
  * Ask Astra now auto generates agent configuration using existing LLM and embedding models.

  ## [Lead Collection & CRM Integrations](/ai-bots-console/leads)

  * Expanded **Lead Collection** options including capture modes, dynamic titles, and smart lead timing settings.
  * Added support for multiple **CRM Integrations** (HubSpot, Salesforce, Zoho, AptlyPlus CRM) with connection setup from the Collect Leads tab.

  ## [Telemetry & Analytics](/oraganization-console/telemetry)

  * Introduced the new **Telemetry Dashboard** showing:
    * User activity
    * Event trends
    * Status distribution
    * Category and feature area insights
    * Actor level event metrics
    * Real time event logs
  * Added advanced filtering across categories, actions, feature areas, roles, and sources.

  ## [Feature Flags](/oraganization-console/settings#feature-flags)

  * Released the **Feature Flags** control panel, allowing org owners to enable or disable experimental features.
</Update>

<Update label="October 2025">
  ## 🎉[New Featured Release](/)

  * Released new QA version for internal testing and validation.
  * Added Q\&A data size analysis on usage analytics page which introduces detailed insights into the volume and size of Q\&A data.

  ## 🧠[Agents: Chat & Interactions](/ai-bots-console/canvas)

  * Introduced the new select text and **Ask Aptlystar** assistant for instant in canvas conversations guidance.
  * Added timestamps in chat messages and exported PDFs for easier reference.
  * Removed the duplicated citations in agents' responses.
  * Added clearer **tooltips** when uploads are disabled to help users understand why actions are unavailable.
  * Disable Delete Option for agent chat History for **all user types or roles**.

  ## 🧠[Agents: Accessibility](/ai-bots-console/canvas)

  * Highlight prompt text and button in agent library dark mode compatible
  * Notification would appear When No Embedding Model Is Available During Agent Creation
  * [CRM Integrations](/ai-bots-console/settings): Integrated HubSpot with AptlyStar, enabling create JSON file with CRM tools object and added CTA to CRM

  ## 👥[Customerlist](/oraganization-console/customerdetails)

  * Imported **Export Customers** feature to quickly download client lists.

  ## 👥[AI Projects: Team Management](/project-console/teams)

  * Users now get instant team list update & success message for Create/Delete Action
  * User Deletion in project doesn't affect the global User List now, and Re-Add option is provided.
  * Make Datastores and Agent permissions optional when someone other than the owner were selected to have permissions to one project.

  ## 📚[AI Catalogue: searching and filter](/oraganization-console/ai-models)

  * Added the search functionality in catalogue
  * Imported Filter for Configured and Non-Configured Models in List

  ## 💳[Billing](/account-details/billing)

  * Progress bars were added on to make the card layout more readable

  ## ⚙️[Settings: Accessibility and Customization](/oraganization-console/settings)

  * Enable users to upload their **personalized logo** in substitution of default aptlystar logo.
  * Added progress indicators to dashboard cards for better tracking.
  * Locked the email field in profile settings to prevent accidental changes.

  ## 🧩[AI Datastore: ](/oraganization-console/data-stores)

  * Imported Chunking techniques when importing datasources.
  * Information Block was added to AI Agents panel.

  ## 🔒Login options and security update

  * Enabled login using OTP accounts.
  * Email Notification will be send to User After Password Change

  ## Quality & Testing

  * Optimized background processes for faster document handling.
  * Improved overall system stability and load performance.
  * Fixed several login and connection issues to deliver a smoother experience.
  * Enhanced CRM synchronization to improve connection stability and reliability.
</Update>

<Update label="September 2025">
  ## [Agents Library](/marketplace)

  * Pre-curated agents to help you quick start building AI solutions.

  ## [Model Comparison](/ai-bots-console/canvas#🧠-multi-model-comparison)

  * Compare responses to the same question across multiple models.
  * Identify the best-fit AI model for your use case.

  ## [Human Identity Integration](/ai-bots-console/look#🎨-style-chat-bubbles)

  * Connect human identity with AI agents.
  * Integrated directly into your website for secure and personalized experiences.

  ## [Pricing & Billing](/account-details/billing)

  * New pricing plans with add-on top-ups for Q\&A and datastore size.
  * New billing page for clear visibility and management.

  ## [Billing: Auto Add-ons](/account-details/billing#⚡-auto-add-ons)

  * Introduced **Auto Add-ons** for seamless top-ups of Q\&A sessions and storage.
  * Automatically purchases additional packs when limits are reached.
  * Sends confirmation emails and prevents workflow interruptions.

  ## [Organization Settings: Feature Flags](/oraganization-console/settings#feature-flags)

  * Added **Feature Flags** tab to control visibility and availability of internal features.
  * Enable or disable modules (e.g., Users Navigation, Legacy Catalogue view, Usage Alerts).
  * Assign visibility by audience or keep features global.

  ## [Organization Settings: Usage Alerts](/oraganization-console/settings#usage-alerts)

  * Introduced **Usage Alerts** tab for automated notifications when usage exceeds defined thresholds.
  * Configure alert percentages, add CC recipients, and track alert history.
  * Helps teams proactively monitor usage and prevent plan overruns.

  ## [Lead Generation](/ai-bots-console/settings#📩-lead-collection)

  * Capture and manage leads directly from AI agent interactions.

  ## [Team & Access Controls](/oraganization-console/users)

  * Domain restrictions for inviting users to your team.
  * Improved security and access management.

  ## [Analytics](/oraganization-console/usage-analytics)

  * Agent-wise and organization-wise usage analytics.
  * Deeper insights into performance and engagement.

  ## [Canvas Enhancements](/ai-bots-console/canvas)

  * Share agent replies (Canvas only).
  * Export conversations (Canvas only).

  ## [Agent Interactions](/ai-bots-console/look)

  * Human feedback option on agent replies.
  * Open agent chat window mode.
  * Customizable splash screen for agents.

  ## [Agents Grouping](/ai-bots-console/agentgrouping)

  * Combine multiple agents into a single chat window.
  * Provide users with access to specialized agents in a unified experience without switching windows.

  ## [AI Models: Image Analysis Support](/oraganization-console/ai-models-with-image-analysis)

  * Added support for models with **Image Analysis** capability.
  * Use the new **Image Analysis filter** to quickly find and add compatible models (e.g., GPT-4o, Claude with vision).
  * Enables agents to process and reason over uploaded images alongside text.

  ## [AI Models: Settings](/oraganization-console/ai-models#ai-model-settings)

  * Configure **custom API keys** for supported AI providers (e.g., OpenAI).
  * Validate keys and apply them at the **organization level** for all agents.
  * Option to **override keys at agent level** for more control.
  * API keys are **encrypted and stored securely**, ensuring compliance and security.

  ## [AI Models: Settings](/oraganization-console/ai-models#ai-model-settings)

  * Configure **custom API keys** for supported AI providers (e.g., OpenAI).
  * Validate keys and apply them at the **organization level** for all bots.
  * Option to **override keys at bot level** for more control.
  * API keys are **encrypted and stored securely**, ensuring compliance and security.

  ## [Datastore: Webpage Auto-Sync](/data-stores-console/web-data-source)

  * Added **Auto-Sync** support for webpage sources.
  * Keep your Datastore updated automatically by scheduling sync intervals (hourly, daily, weekly).
  * Use **Sync Now** for instant updates and check **Sync History** for logs and changes.
  * Perfect for keeping agents current with product docs, FAQs, or changelogs.
</Update>

<Update label="January 2025">
  ## Launch Week Highlights

  * **Quickstart Agent Creation**: Create your first AI agent with an intuitive step-by-step guide, designed to get you up and running fast.
  * **Marketplace Agent Deployment**: Deploy pre-built AI agents from the marketplace to instantly leverage common use cases—no building from scratch required.
  * **Data Source Integration**: Seamlessly enrich your AI agents by importing data from PDFs, web pages, APIs, YouTube, GitHub, and more for superior context-aware responses.
  * **Multi-Channel Analytics**: Track agent performance with detailed usage analytics across Chat Playground, web widgets, WhatsApp, Discord, Telegram, and API channels to understand engagement and optimize reach.
  * **Team & Workspace Management**: Organize collaboration with project-specific Teams—assign members, define roles, and streamline workflows in your AptlyStar workspace.
</Update>


# AI Agents using Data Stores
Source: https://docs.aptlystar.ai/data-stores-console/ai-bots



The **AI Agents in Data Stores** section enables you to connect intelligent agents to organizational data stores, allowing them to retrieve, understand, and respond using stored information.

Linking a data store to your agent ensures that it can access and utilize relevant knowledge effectively.

***

## Add a Data Store to an AI Agent

Follow the steps below to add one or more data stores to your agent:

<Steps>
  <Step title="Navigate to the Agent">
    From the **AptlyStar Dashboard**, go to the **My Agents** tab (1), then click on the **AI Agent** you want to enhance with data (2).

    <Frame>
      <img alt="Open AI Agent from My Agents" />
    </Frame>
  </Step>

  <Step title="Link a Data Store">
    Inside the agent view:

    * Click on the **Data Stores** tab (1)
    * Click **Add Data Stores (Knowledge)** (2)

    <Info>
      **Pre-requisite:** The embedding model used by the selected data store **must match** the embedding model used by the AI agent. Otherwise, the linking will be disabled.
    </Info>

    <Frame>
      <img alt="Data Stores Linked to Agent" />
    </Frame>
  </Step>

  <Step title="View Linked Data Stores">
    * Select the desired data stores (3)
    * Click **Add to Agent** (4)
      Once added, your selected data stores will be visible under the agent’s **Data Stores** tab.

    You can link multiple data stores to one agent based on the same embedding model.

    <Frame>
      <img alt="Data Stores Linked to Agent" />
    </Frame>
  </Step>

  <Step title="Verify from Data Store Console">
    To cross-verify, go to the **AI Data Stores** section of the platform.\
    Click on a data store and open the **AI Agents** tab to see which agents are currently linked to it.

    <Frame>
      <img alt="Agent Listed Under Data Store" />
    </Frame>
  </Step>
</Steps>

> Want to learn how to create a agent first?\
> Check out our [AI Agent Creation Guide](/ai-bots-console/create-bot)


# Data Sources in Data Store Console
Source: https://docs.aptlystar.ai/data-stores-console/data-sources



AptlyStar.ai enables you to **seamlessly integrate a variety of data sources** into your data stores, enriching your AI agent's knowledge base. Supported source types include:

* **Webpage links**
* **Text files**
* **File uploads (PDFs, DOCs, etc.)**
* **REST APIs**
* **YouTube links**
* **GitHub repositories**

This allows for more powerful and context-aware query responses from your AI agents.

***

## 🧩 Steps to Add Data Sources

<Steps>
  <Step title="Select a Data Store and Open the Data Sources Tab">
    From the **AI Data Stores** console, choose your desired data store.

    Then:

    * Click on the **Data Sources** tab (1)
    * Click **Add new data source** (2)

    <Frame>
      <img alt="Add Data Source in Console" />
    </Frame>
  </Step>

  <Step title="Choose & Import Data Source">
    Now you can import data by:

    * Entering a **webpage URL**
    * Uploading a **file**
    * Adding plain **text**
    * Connecting via **REST API**, **YouTube**, or **GitHub**

    After selecting and filling the input, click **Create** (4).\
    You can add multiple sources to populate your data store.

    <Frame>
      <img alt="Data Source Input Modal" />
    </Frame>
  </Step>

  <Step title="Verify Data Import">
    Once completed, a **“Finished”** success message will appear.\
    You can now see all imported sources listed in your datastore under the **Data Sources** section.

    <Frame>
      <img alt="Data Sources Added" />
    </Frame>
  </Step>
</Steps>

***

## Re-finetune the existing data

The edit feature allows you to quickly correct mistakes, update outdated information, or refine the quality of your AI agent's knowledge base — ensuring more accurate and reliable responses.

<Steps>
  <Step title="Step 1: Go to Data Sources Tab of a Data Store">
    Navigate to **AI Data Stores**, select your data store, and click on the **Data Sources** tab.

    <Frame>
      <img alt="Data Sources Tab" />
    </Frame>
  </Step>

  <Step title="Step 2: Open the Data Source">
    Find the data source you want to edit and click the **arrow icon** next to it to view its details.

    <Frame>
      <img alt="Open Data Source Details" />
    </Frame>
  </Step>

  <Step title="Step 3: Click Edit to Modify">
    Inside the detail view, click the **Edit** button to start modifying the content of the data chunk.

    <Frame>
      <img alt="Edit Data Source" />
    </Frame>
  </Step>

  <Step title="Step 4: Update Content and Save">
    Make your desired changes to the text and click **Save** to update the data source.

    <Frame>
      <img alt="Save Edited Data Source" />
    </Frame>
  </Step>
</Steps>

> Want to link this data to a agent?\
> Head over to the [AI Agents guide](/ai-bots-console/ai-bots) for connecting your agent to data stores.


# Managing Data Stores
Source: https://docs.aptlystar.ai/data-stores-console/overview



A **Data Store** is a curated collection of information—ranging from uploaded PDFs, webpages, and text files to entire knowledge corpora—that powers your AI Agents in AptlyStar.ai.

***

## Organization vs. Project Scope

* **Organization-Level Data Stores**:\
  Accessible globally across all projects and agents within your organization.

* **Project-Level Data Stores**:\
  Restricted to specific agents or configurations under that project.

> Reusable data stores eliminate duplication, reduce costs, and simplify multi-agent integrations. One setup, multiple uses!

***

## How to Access and Manage Data Store Settings

<Steps>
  <Step title="Open Data Store Overview">
    To manage a data store:

    * Navigate to the **AI Data Stores** console
    * Click on a specific data store to open its overview panel

    <Frame>
      <img alt="Open Data Store Overview" />
    </Frame>
  </Step>

  <Step title="Review Data Store Summary">
    In the **top section** of the overview tab, you will find key statistics:

    * Number of **AI Agents using this store**
    * Volume of **data converted into the AI knowledge corpus**
    * Total **data size** in bytes
    * **Linked sources** count
    * Storage usage against quota

    This area gives you an at-a-glance status of store usage and integrations.

    <Frame>
      <img alt="Data Usage, Agents, and Source Links" />
    </Frame>
  </Step>

  <Step title="Edit Data Store Details">
    In the **Data Store Details** section, you can view and modify:

    * Store name and description
    * Embedding model in use
    * Creator and creation date
    * Enable/disable status
    * Allow data reuse toggle (if data from this store can be linked elsewhere)

    <Frame>
      <img alt="Editable Data Store Fields and Toggles" />
    </Frame>
  </Step>

  <Step title="Adjust RAG Settings (Optional)">
    At the bottom of the overview section, you can configure **RAG (Retrieval-Augmented Generation) Settings**:

    * **Chunk Size** – Size of each text segment processed by the embedding model.
    * **Chunk Overlap** – Number of tokens that overlap between chunks for better continuity.
    * **Enhanced Website Loader** – Improves structure and readability when processing webpages.

    <Info>
      If you're unsure, keep the default RAG settings as-is for best performance.
    </Info>

    <Frame>
      <img alt="Overview Section Highlights" />
    </Frame>
  </Step>
</Steps>

Need to upload or connect new data sources? Visit [Data Sources](/data-stores-console/data-sources) for a step-by-step guide.


# Adding Webpage Data Sources
Source: https://docs.aptlystar.ai/data-stores-console/web-data-source



Webpages are one of the most powerful ways to enrich your Datastore.\
By connecting a webpage, your agent can directly reference and learn from the content of online resources like documentation, blogs, FAQs, or changelogs — ensuring answers stay **accurate and updated**.

This guide walks through how to add a **webpage as a data source**, configure **Auto-Sync schedules**, and monitor updates with **Sync History**.

***

## Step-by-Step Guide

<Steps>
  <Step title="Open a Datastore and navigate to Data Sources">
    Select the **Datastore** where you want to add external knowledge.\
    Inside, go to the **Data Sources** tab. Here you’ll see all linked sources that feed information into your Datastore.

    Click **Add new data source** to start adding content.

    <Frame>
      <img alt="Add new data source button inside a Datastore" />
    </Frame>
  </Step>

  <Step title="Choose Webpage as the data source type">
    The system supports multiple types of sources (Files, Text, GitHub, REST API, etc.).

    Select **Webpage**, paste the target URL, and click **Create**.\
    This will crawl the page content and add it into the Datastore.

    <Frame>
      <img alt="Select Webpage source option" />
    </Frame>
  </Step>

  <Step title="Confirm the Webpage is added successfully">
    Once processed, the webpage source will appear in the list of sources.

    Each entry shows:

    * **Source Type** (Webpage, File, etc.)
    * **Title of the page**
    * **Inclusion Status** (e.g., Included in knowledge corpus)
    * **Processing State** (FINISHED, PENDING, etc.)

    At this point, your agent can already use this content.

    <Frame>
      <img alt="Webpage successfully added to datastore" />
    </Frame>
  </Step>

  <Step title="Enable Auto-Sync to keep data fresh">
    Webpages often change — new docs, updated FAQs, or product details.\
    To avoid outdated answers, toggle **Auto-Resync** on the source card.

    This ensures the system automatically re-crawls the page at set intervals.

    <Frame>
      <img alt="Enable Auto-Resync for the webpage source" />
    </Frame>
  </Step>

  <Step title="Choose a sync frequency">
    When Auto-Sync is enabled, you can define how frequently the page should be re-processed.

    Options include:

    * Every hour / 3h / 6h / 12h / 24h
    * Specific weekdays (e.g., every Monday, every Friday)

    Pick a schedule that matches how often the page changes. For example, news sites may need hourly syncs, while product docs may only need daily updates.

    <Frame>
      <img alt="Configure sync schedule for webpage" />
    </Frame>
  </Step>

  <Step title="Use Sync Now for instant updates">
    If urgent changes happen on the webpage and you don’t want to wait for the next scheduled sync, click **Sync Now**.

    This immediately reprocesses the page and refreshes the Datastore with the latest version.

    <Frame>
      <img alt="Manual sync now option on the data source card" />
    </Frame>
  </Step>

  <Step title="Monitor Sync History">
    To track all re-crawls, click **History** on the source card.\
    Here you can see:

    * Number of syncs performed
    * **Success rate** (were syncs successful or failed)
    * **Content changes** (if any new text was detected)
    * **Average processing time**

    This is helpful for auditing and ensuring data freshness.

    <Frame>
      <img alt="Sync history details of a webpage source" />
    </Frame>
  </Step>
</Steps>

***

## ✅ Key Takeaways

* Webpages are a simple way to continuously feed **dynamic, public content** into your Datastore.
* Use **Auto-Sync** for hands-free updates, and **Sync Now** for urgent refreshes.
* Track updates with **Sync History** to ensure your agent always has the most accurate knowledge.

<Check>
  Keeping your web data sources updated ensures your AI agents always provide **relevant, reliable, and up-to-date answers**.
</Check>


# Creating AI Agent
Source: https://docs.aptlystar.ai/howtocreateaiagent



Standard/Instant Access or Dashboard Path

<Steps>
  <Step title="Start Agent Creation">
    You can begin agent creation from multiple entry points:

    From the **Home** screen, click on **Start** under **Create AI Agents**.

    <Frame>
      <img alt="Org Home Start" />
    </Frame>

    Or navigate to **My Agents** and click on **Add new agent**.

    <Frame>
      <img alt="My Agents Start" />
    </Frame>

    This opens the guided creation wizard with 3 simple steps:

    <Frame>
      <img alt="3-Step Start Screen" />
    </Frame>
  </Step>

  <Step title="Step 1: Add LLM & Embedding Models">
    Start by adding the necessary AI models for your Agent.

    * Click **Add AI model** and choose a supported LLM (e.g., GPT-4o).

    <Frame>
      <img alt="Add AI Model" />
    </Frame>

    * Use the filters at the top of the selection modal to refine results:
      * **Provider Filter** – show models from a specific provider (e.g., OpenAI, Anthropic, Azure)
      * **Capability Filters** – such as *Image Analysis*, to quickly find multimodal models that support vision tasks
    * Select one or more models from the filtered list
    * Click **Add to org** to complete

    <Frame>
      <img alt="Select GPT-4o" />
    </Frame>

    * Switch to the **Embedding Models** tab and click **Add embedding model**.

    <Frame>
      <img alt="Add Embedding Model" />
    </Frame>

    * Choose a model like `text-embedding-3-large` and add it to your org.

    <Frame>
      <img alt="Select Embedding Model" />
    </Frame>

    * Ensure a model is selected before moving to the next step. Click **Next** to proceed to Step 2.

    <Frame>
      <img alt="Pick Embedding Model" />
    </Frame>
  </Step>

  <Step title="Step 2: Create and Upload Data Store">
    In this step, you’ll prepare the knowledge base for your agent.

    * Click **Go to data store to upload data**.

    <Frame>
      <img alt="Upload Prompt" />
    </Frame>

    * Click **Create new Data Store** and fill in the details including the embedding model.

    <Frame>
      <img alt="Create Data Store" />
    </Frame>

    * Once created, click **Upload data to this Data Store**.

    <Frame>
      <img alt="Upload Data Action" />
    </Frame>

    * Choose the data source (Webpage, Text, File, YouTube, or REST API).

    <Frame>
      <img alt="Select Data Source Type" />
    </Frame>

    * After uploading, select the data store to associate with your agent and click **Next**.

    <Frame>
      <img alt="Select Data Store" />
    </Frame>
  </Step>

  <Step title="Step 3: Configure & Create Agent">
    You can now finalize and deploy your agent using one of two options:

    **Custom Configuration**

    * Enter a name, choose chat model, embedding model, and link your data store.
    * Click **Create** to finish.

    <Frame>
      <img alt="Custom Config Agent Creation" />
    </Frame>

    **Default Configuration**

    * Uses preselected models for fast setup.
    * Simply click **Create AI Agent**.

    <Frame>
      <img alt="Default Config Agent Creation" />
    </Frame>
  </Step>
</Steps>

***

## Creating Agents with Ask Astra

Ask Astra provides a quick way to build agents using prompt based suggestions.\
Astra generates the agent configuration automatically based on the prompt you choose or write.

Before using Ask Astra, make sure:

* Your project already has at least one LLM model
* And at least one embedding model

Astra uses these existing models to create the new agent.

<Steps>
  <Step title="Open Ask Astra">
    Click the **Ask Astra** button to start the assisted agent creation flow.

    <Frame>
      <img alt="Ask Astra Entry" />
    </Frame>
  </Step>

  <Step title="Pick a Suggested Prompt">
    Astra shows a list of suggested agent ideas you can start from.\
    If none of them match what you need, click **Change Suggestions** to shuffle and see new options.

    <Frame>
      <img alt="Suggested Prompts" />
    </Frame>
  </Step>

  <Step title="Or Enter Your Own Custom Prompt">
    If you prefer, you can write your own description of the agent you want to build.\
    This is useful when your use case is more specific or not covered by the suggestions.

    <Frame>
      <img alt="Custom Prompt Field" />
    </Frame>
  </Step>

  <Step title="Review the Generated Agent">
    Astra uses your chosen or custom prompt to generate the initial agent setup.\
    You can review the configuration and adjust it before creating the agent.
  </Step>
</Steps>

<Note>
  Ask Astra makes agent creation fast by letting you start from guided suggestions or your own custom prompt, without manually configuring each step.
</Note>


# Introduction
Source: https://docs.aptlystar.ai/introduction

Welcome to the AptlyStar.ai Documentation Portal. 
Navigate through detailed guides and resources to make the most of our platform.

<div>
  <Frame>
    <img alt="Hero Light" />

    <img alt="Hero Dark" />
  </Frame>

  <h2>Get Started with AptlyStar.ai</h2>

  <p>
    Explore core areas of the AptlyStar platform — from building AI agents to customizing them with data and styling. Each guide walks you through setup and optimization.
  </p>
</div>

<div>
  <Card href="/howtocreateaiagent">
    <img alt="Create Agent" />

    <p>
      Quickstart to Create AI Agent
    </p>

    Deploy your first AI agent with a simple step-by-step guide.
  </Card>

  <Card href="/data-stores-console/data-sources">
    <img alt="Train Data" />

    <p>
      Train with Your Data
    </p>

    Feed your agent with PDFs, websites, and APIs to create context-aware conversations.
  </Card>

  <Card href="/ai-bots-console/settings">
    <img alt="Model Settings" />

    <p>
      AI Model Configuration
    </p>

    Set your chat model, embedding strategy, and tune precision.
  </Card>

  <Card href="/ai-bots-console/look">
    <img alt="Agent Styling" />

    <p>
      Agent Styling & Integration
    </p>

    Customize appearance and easily embed into your website.
  </Card>
</div>


# Deploying Pre-Built Agents from Agents Library
Source: https://docs.aptlystar.ai/marketplace

Use the AptlyStar.ai Agents Library to quickly deploy ready-to-use AI agents with minimal setup.

## Why Use the AptlyStar.ai Agents Library?

<Note>
  The AptlyStar.ai Agents Library enables you to deploy ready-to-use AI agents with minimal setup, making it suitable for both technical and non-technical users. Focus on customization — not infrastructure.
</Note>

<CardGroup>
  <Card title="Quick Deployment" icon="rocket">
    Deploy ready-made agents instantly, with no prior technical expertise required.
  </Card>

  <Card title="Time Efficiency" icon="clock">
    Skip the setup — jump straight into customization and usage.
  </Card>

  <Card title="Scalable Solutions" icon="expand">
    Access agents for various use cases and scale your projects effortlessly.
  </Card>

  <Card title="Cost-Effective" icon="money-bill-trend-up">
    Cut down development time and costs with plug-and-play AI agents.
  </Card>

  <Card title="Simplified Integration" icon="plug">
    Seamlessly integrate AI agents into your existing workflow.
  </Card>
</CardGroup>

***

## Steps to Deploy Pre-Built Agents from the Agents Library

<Steps>
  <Step title="Visit the Agents Library">
    Navigate to the **Agents Library** section in the top menu of the application.\
    Browse and select a **pre-built agent** that suits your use case.

    <Frame>
      <img alt="Agents Library Selection" />
    </Frame>
  </Step>

  <Step title="Choose Configuration and Deploy">
    Once you select a agent, proceed to deploy it using either:

    * **Default Configuration**: Uses preselected models and settings
    * **Custom Configuration**: Lets you choose your own models and linked data stores

    Learn more here:\
    [How to Create a Agent →](https://docs.aptlystar.ai/howtocreateaiagent)

    <Frame>
      <img alt="Choose Agent Config" />
    </Frame>
  </Step>
</Steps>

<Check>
  Once you click **Create AI Agent**, the pre-built agent will be deployed with the selected prompt and configuration instantly.
</Check>


# null
Source: https://docs.aptlystar.ai/oraganization-console/Organization-config



## Steps to Configure Your AI Organization

Before creating any AI agents or projects, your organization must be **properly configured**.

Explore the sections below step by step to get started with a solid foundation for building GenAI applications.

<CardGroup>
  <Card title="Add LLM Model to Organization" icon="star" href="https://docs.aptlystar.ai/oraganization-console/ai-models">
    Integrate your preferred LLM model into the organization to start creating AI agents and agents.
  </Card>

  <Card title="Create a New Datastore" icon="database" href="https://docs.aptlystar.ai/oraganization-console/data-stores">
    Set up reusable datastores by uploading data from various sources such as PDFs, websites, or text.
  </Card>
</CardGroup>

<Info>
  These are **mandatory steps** before proceeding to agent creation. Make sure both the LLM model and datastore are added successfully in the console.
</Info>


# AI Catalogue
Source: https://docs.aptlystar.ai/oraganization-console/ai-models



## What is AI Catalogue?

AI Catalogue contains AI models that are algorithms trained on data to perform specific tasks such as pattern recognition, data analysis, content generation, and predictions. They range from classic machine learning models to advanced large language models (LLMs) like **GPT**, which specialize in understanding and generating human-like text.

These models are essential for powering AI-driven applications like agents, enabling:

* Natural, human-like conversations
* Personalized interactions
* Continuous learning and improvement

Choosing the **right LLM model** is critical to ensure optimal performance. Depending on your use case — whether it's **customer support**, **content creation**, or **workflow automation** — the appropriate model enhances:

* Accuracy
* Efficiency
* Scalability

***

## AI Catalogue in AptlyStar

The **AI Catalogue** section in AptlyStar displays a list of models added to your organization. You can check them in **Models in Org**.

<Frame>
  <img alt="Models in Organization" />
</Frame>

<Note>
  AptlyStar supports **49 LLM models** and **11 embedding models**, giving you flexibility to power a variety of AI-driven applications.
</Note>

These models allow you to integrate cutting-edge AI capabilities into your agents— all tailored to your needs.

### Supported Providers

<Info>
  The following providers are supported by AptlyStar for LLM integration:
</Info>

* OpenAI
* Azure OpenAI
* Groq
* Anthropic
* Cohere
* Google
* Fireworks
* Ollama

These providers enable you to harness best-in-class language models in your organization’s AI projects.Find a full list of supported models in **Catalogue**.

<Frame>
  <img alt="Models in Organization" />
</Frame>

## Add Models to Your Organization

<CardGroup>
  <Card title="Add LLM Model to the Organization" icon="star" href="/oraganization-console/ai-models#1-add-llm-models">
    Add your favorite LLM model to create AI agents.
  </Card>

  <Card title="Add Embedding Model to the Organization" icon="chart-simple" href="/oraganization-console/ai-models#2-add-embedding-models">
    Add an embedding model to enable your agents to process and retrieve knowledge effectively.
  </Card>
</CardGroup>

***

## 🛠️ How to Add LLM Models

### 1) Add LLM Models to Your Organization

To add LLM models:

* Navigate to the **AI Catalogue** page

* Click **Add Model**

* Use the filters at the top of the selection modal to refine results:

  * **Provider Filter** – lets you quickly narrow down models based on their source.\
    For example, you can view only **OpenAI**, **Anthropic**, **Azure OpenAI**, or **Groq** models depending on your preferred infrastructure and compliance needs.\
    This makes it easier to align your agent setup with organizational standards (e.g., Azure-hosted for enterprise security or Groq for cost optimization).

  * **Image Analysis Filter** – highlights only models that support **vision and multimodal tasks** (image + text).\
    By enabling this filter, you can instantly see which LLMs allow you to build agents that analyze and interpret images.\
    This is especially useful for use cases like **document OCR**, **screenshot QA**, **visual inspections**, or **content moderation involving images**.

* Select one or more models from the filtered list

* Click **Add to org** to complete

<Frame>
  <img alt="LLM Models Table" />
</Frame>

Once models are selected, confirm and complete the addition:

<Frame>
  <img alt="Select AI Models with Provider and Image Analysis Filters" />
</Frame>

<Note>
  The **Provider filter** helps you choose *where* your models come from, while the **Image Analysis filter** helps you choose *what they can do*. Using both together ensures you add the most relevant models to your organization.
</Note>

### 2) Add Embedding Models to Your Organization

To add embedding models:

* On the same **AI Catalogue** page, click on **Add Embedding Model**
* Select an embedding model from the list
* Click **Add to org** to add it

<Frame>
  <img alt="Select Embedding Model" />
</Frame>

<Check>
  Ensure both **LLM models** and **embedding models** are successfully added to your organization to enable full agent functionality and performance.
</Check>

## AI Catalogue Config

Beyond adding models, AptlyStar also provides a **config** option that allows you to configure **custom API keys** for supported providers.\
This is useful if your organization wants to use its own API keys instead of the system-provided ones for security, billing, or usage tracking purposes.

### AccessingConfig

To open the config, click the **Config** button on the top-right of the **AI Catalogue** page.

<Frame>
  <img alt="Navigate to AI Catalogue Config" />
</Frame>

***

### Select AI Provider

From the settings panel, select the **AI Provider** for which you want to configure a key (e.g., OpenAI, Azure OpenAI, Anthropic, etc.).

<Frame>
  <img alt="Select AI Provider" />
</Frame>

***

### Enable Custom API Key

Toggle the **Use Custom API Key** option. This allows you to provide your own key rather than relying on system defaults.

<Frame>
  <img alt="Enable Custom API Key Option" />
</Frame>

***

### Add and Validate Key

* Enter your **API Key**
* (Optional) Provide your **Organization ID** if the provider requires it
* Click **Validate API Key** to check correctness
* Save your settings to apply them globally

<Frame>
  <img alt="Enter API Key and Save Settings" />
</Frame>

***

### Notes

* Keys are **encrypted and stored securely**.
* **Organization-level keys** apply to all agents by default.
* Keys can still be overridden at the **agent level** if a specific agent requires a different configuration.

<Check>
  Using custom API keys ensures you have full control over billing, monitoring, and provider-specific compliance requirements for your organization.
</Check>


# AI Models with Image Analysis
Source: https://docs.aptlystar.ai/oraganization-console/ai-models-with-image-analysis



AI Models with **Image Analysis** and **Image Generation** capabilities allow your agents to process, understand, and create visual content.\
This unlocks powerful multimodal workflows — making your assistants capable of analyzing screenshots, generating visuals, and responding contextually.

***

## 🖼️ Why Image Analysis Matters

Traditional text-only AI agents are limited to what users type. With **Image Analysis enabled models**, you can:

* Upload documents or screenshots for instant analysis
* Run visual inspections (manufacturing, QA, product checks)
* Extract structured data from receipts, bills, or invoices
* Provide educational explanations from diagrams or charts
* Moderate user-submitted images in community platforms
* Automate HR and business workflows with scanned or photographed inputs

This makes your agents much more versatile and useful in real-world workflows.

***

## 📥 How to Use Image Analysis in Canvas

On the **Canvas playground**, when your selected model supports image analysis, you’ll see an option to **Add Photos** directly in the input bar.

<Frame>
  <img alt="Canvas Add Photo Option" />
</Frame>

Users can drag and drop images or select them, and the model will generate a response that combines **text understanding + visual reasoning**.

***

## 🎯 Selecting Image Analysis Models

Not all LLMs support image processing. To ensure your agent can use image features, you need to select a model with **Image Analysis support**.

On the **AI Models** page, use the filter options:

* **Provider Filter** – Choose the provider (e.g., OpenAI, Anthropic, Azure)
* **Image Analysis Filter** – Narrow down to only those models that support **vision + multimodal tasks**

<Frame>
  <img alt="Filter AI Models for Image Analysis" />
</Frame>

This ensures you only add models with visual reasoning capabilities into your organization.

***

## 🧠 Image Generation Models

Beyond analyzing visuals, AptlyStar also supports **AI models capable of generating images** from text prompts — opening new creative and automation possibilities.

<Frame>
  <img alt="AI Models with Image Generation capability" />
</Frame>

### ✨ How Image Generation Works

* Choose a model that supports **Image Generation** (e.g., *GPT Image 1 - OpenAI*).
* Use **Canvas** to enter a descriptive text prompt such as “Generate a world map” or “Create a futuristic city skyline.”
* The model produces a **generated image output** directly inside the Canvas conversation.

<Frame>
  <img alt="Generated image result in Canvas" />
</Frame>

### 🔍 Key Features

* Generate visuals using natural language descriptions.
* Ideal for creative workflows (marketing, education, storytelling, design).
* Works seamlessly inside the **Canvas interface** — no separate upload or setup needed.
* Supports iterative prompting: refine your image by continuing the conversation.

> 💡 Example prompt: “Generate a minimalist infographic showing AI workflow from data to decision.”

***

## 📚 Example Use Cases

### 🧾 Business: Document Parsing

* Upload receipts, invoices, contracts, or ID cards.
* The agent extracts structured data (amounts, parties, dates) for finance or legal systems.
* Example: Finance teams can upload monthly receipts for **automatic expense tracking**.

***

### 🏢 HR: Candidate Screening & Compliance

* Parse **resumes with embedded charts, certificates, or scanned copies**.
* Verify identity documents (passports, ID cards) for onboarding.
* Extract data from **employee forms** (tax forms, HR compliance scans) into HRIS systems.
* Example: HR uploads an employee’s scanned certificate → the agent validates the content and logs it automatically.

***

### 🏭 Industry: Quality Control

* Factory workers upload photos of product parts.
* The agent analyzes defects (scratches, misalignment, wear) and flags issues in real-time.
* Example: A car manufacturer uploads part images to ensure **paint quality and assembly alignment**.

***

### 📖 Education: Diagram Explainer

* Students upload math graphs or physics diagrams.
* The agent explains step-by-step interpretations (formulas, forces, trends).
* Example: A biology student uploads a photo of a cell diagram → the agent highlights organelles with explanations.

***

### 🌐 Customer Support: Screenshot Debugging

* Users share error screenshots.
* The agent interprets UI messages or codes and suggests troubleshooting steps.
* Example: A SaaS company receives screenshot-based queries → the agent automatically **guides users through fixes**.

***

### 🛍️ Retail & E-commerce

* Customers upload product photos for **catalog matching** or **AI image generation previews**.
* The agent can generate visuals of new variants or recommend similar items.
* Example: Upload a product photo → the agent creates a custom variation mockup.

***

## ✅ Key Takeaways

* Enable **Image Analysis** to make agents understand and reason over visuals.
* Use **Image Generation** models to create new visuals from text prompts.
* Both capabilities work seamlessly in **Canvas**, enhancing multimodal AI workflows across business, education, and creative domains.

<Check>
  By combining **Image Analysis** and **Image Generation**, your organization can build truly multimodal agents — capable of seeing, understanding, and creating.
</Check>


# Customer List
Source: https://docs.aptlystar.ai/oraganization-console/customerdetails



The **Customer List** section provides a centralized view of all end-users who have interacted with your agents. It’s designed to help you keep track of customers across projects and agents, complete with key identifiers and timestamps.

***

## 👥 What You Can Track

This table helps you manage and review the following fields:

| Column         | Description                                             |
| -------------- | ------------------------------------------------------- |
| **Name**       | Customer’s full name (if available)                     |
| **Email**      | Email used to start the agent interaction               |
| **Mobile**     | Contact number, if captured                             |
| **Project ID** | Associated project under which the agent was created    |
| **Agent Name** | The name of the agent the user interacted with          |
| **Agent ID**   | Internal unique identifier for the agent                |
| **Created At** | Timestamp showing when the user was added to the system |

<Frame>
  <img alt="Customer Table Overview" />
</Frame>

***

## 📤 Export Customer Details

You can now **export the entire customer list** for analysis, reporting, or CRM import.

Click the **Export** button at the top of the page to download all customer records in `.csv` format.

<Frame>
  <img alt="Export Customer Details button" />
</Frame>

### Key Highlights

* Exports all visible customer details (Name, Email, Mobile, Agent, Project, Created At).
* Downloadable in `.csv` format for quick sharing or integration.
* Useful for marketing teams, CRM updates, and performance analysis.

> 💡 Export helps you maintain offline records and simplifies syncing customer data with other internal tools.

***

## 🔍 Why Use the Customer List?

Use this page to:

* **Audit interactions** across agents and projects
* **Support ticket resolution** with user-level detail
* **Review usage per customer** for personalization or CRM sync
* **Cross-check users** tied to specific agent sessions

> Note: The customer list auto-populates as users engage with agents. No manual entry is required.

***

## 🔐 Data Privacy Considerations

This section respects the privacy rules and security protocols in place:

* Only owners and admins can access this data
* Sensitive data (e.g., email, phone) is never shown publicly or stored without consent
* You can integrate this list with your internal systems using API access (coming soon)

***


# Data Stores of Organization
Source: https://docs.aptlystar.ai/oraganization-console/data-stores



## 🗂️ What is a Data Store?

A **Datastore** is a centralized collection of various data sources—such as PDFs, webpages, or internal knowledge bases—managed through the **Organization Console**.

It acts as a unified repository for your data, enabling seamless access and integration across:

* Multiple AI agents
* AI-powered workflows
* Organizational applications

Datastores allow you to reuse data across projects, eliminating the need to recreate sources repeatedly.

<Note>
  This reusability improves **consistency**, reduces **manual effort**, and saves **time and costs** when scaling your AI initiatives.
</Note>

Whether you're working with structured or unstructured data, datastores offer a scalable way to manage your organization’s knowledge corpus.

```mermaid theme={null}
flowchart TB
    DS[("DATA STORES")] --> SRC[("DATA SOURCES")]

    SRC --> FILES([Files])
    SRC --> WEBPAGES([Webpages])
    SRC --> TEXT([Text])

    FILES -->|Embeddings| VECTOR[(Vector DB)]
    WEBPAGES -->|Embeddings| VECTOR
    TEXT -->|Embeddings| VECTOR
```

***

## 🛠️ Steps to Add a Datastore

<Steps>
  <Step title="Step 1: Start Creating a New Datastore">
    * Navigate to the **AI Data Stores** section in the organization console
    * Click on **Create New Data Store**

    <Frame>
      <img alt="Add New Datastore Button" />
    </Frame>
  </Step>

  <Step title="Step 2: Add Datastore Details">
    * Fill in the following:
      * **Datastore Name**
      * **Description**
      * **Embedding Model**
    * Click **Add Datastore** to finalize

    <Frame>
      <img alt="Datastore Details Form" />
    </Frame>
  </Step>

  <Step title="Step 3: View Datastore in List">
    Once created, your datastore will appear in the organization’s list with embedding info and data stats.

    <Frame>
      <img alt="Datastore View List" />
    </Frame>
  </Step>
</Steps>

<Info>
  📌 **Note:**\
  While multiple datastores within the same project can use different **embedding models**, to **link a datastore with a agent**, both must use the **same embedding model**.
</Info>


# Home
Source: https://docs.aptlystar.ai/oraganization-console/home

Understand and navigate the AptlyStar organization homepage to manage teams, users, and AI resources effectively.

## What is the Home Page?

<Note>
  The **Home Page** is your central hub for managing users, teams, projects, and agents in AptlyStar.ai. Each organization acts as a collaborative workspace for building and deploying AI solutions.
</Note>

### Why Use an Organization?

<CardGroup>
  <Card title="Structure Your Team" icon="building">
    Customize your organization to reflect your team or company structure.
  </Card>

  <Card title="Invite & Manage Members" icon="users">
    Add users, assign roles, and collaborate seamlessly across teams.
  </Card>

  <Card title="Organize Projects & Agents" icon="diagram-project">
    Create and manage AI projects under one centralized workspace.
  </Card>

  <Card title="Streamlined Workflows" icon="shuffle">
    Keep resources, roles, and projects in sync for efficient development.
  </Card>
</CardGroup>

***

## What You See on the Home Dashboard

<Steps>
  <Step title="Access Your Organization Overview">
    Logging into the AptlyStar portal takes you to a dashboard that summarizes your organization's key details, such as:

    * Number of **teams**, **projects**, **users**, and **AI Agents**
    * Your organization’s name and metadata
    * Admin and ownership information

    <Frame>
      <img alt="Organization Home Dashboard" />
    </Frame>
  </Step>

  <Step title="Quick Actions from the Sidebar">
    On the right side of the home dashboard, you can:

    * Click **Create AI Agents** to launch new agents instantly
    * View a list of your **favorite agents**
    * Navigate easily to other sections like Projects, AI Models, or Users

    Clicking on **Home** anytime will always bring you back to this overview.
  </Step>
</Steps>


# Projects
Source: https://docs.aptlystar.ai/oraganization-console/projects



## What is an AI Project in AptlyStar?

In AptlyStar, an **AI Project** is a centralized workspace designed to organize tasks, workflows, and AI agents under a shared objective.

Projects provide key details such as:

* Project Name
* Description
* Status
* Creation Date

They serve as a foundation for:

* Centralizing organizational efforts
* Fostering team collaboration
* Enhancing transparency with progress tracking
* Aligning initiatives with strategic goals
* Streamlining operations and resource allocation

<Note>
  Projects help you scale AI efforts across teams by creating structured, trackable, and collaborative environments.
</Note>

***

## 📁 Projects Section Overview

The **Projects** section displays a list of all projects created within your organization. Each entry includes:

* Project Name
* Description
* Status
* Date Created

## Creating a New Project

To set up a new project in AptlyStar:

1. Navigate to the **Projects** section from the sidebar
2. Click the **Create new project** button at the top

<Frame>
  <img alt="Create New Project Button" />
</Frame>

A project creation form will appear on the right.

3. Enter the **Project Name** and an optional **Description**
4. Click **Add project** to finalize creation

<Frame>
  <img alt="Project Name Form" />
</Frame>

***

## Coming Soon: AI Agents for Projects

<Info>
  We’re excited to announce that **AI Agents for Projects** are coming soon!
</Info>

These intelligent agents will enable:

* Enhanced automation of workflows
* Smarter task management
* Increased efficiency within project-based collaboration

<Check>
  Empower your team with intelligent agents to get even more done, faster — right inside AptlyStar Projects.
</Check>


# Settings
Source: https://docs.aptlystar.ai/oraganization-console/settings



## Settings Overview

The Settings section lets organization owners and admins manage workspace configuration, access control, domain restrictions, feature availability, and branding.

This page includes the following tabs:

* **Overview**
* **Domain Restrictions**
* **Feature Flags**
* **Brand Setting**

<Frame>
  <img alt="Settings overview screen" />
</Frame>

***

## Overview

The Overview tab provides a quick snapshot of your organization.

You can view:

* Active projects
* Active teams
* Active users
* Active bots

Along with the ability to update:

* Organization name
* Organization description

<Frame>
  <img alt="Organization details edit screen" />
</Frame>

***

## Domain Restrictions

Use Domain Restrictions to control who can join your organization.

When enabled:

* Only users with your company’s email domain can be invited
* External or mistyped domains cannot be added
* You can toggle this setting on or off at any time

<Frame>
  <img alt="Domain restriction toggle" />
</Frame>

***

## Feature Flags

The Feature Flags tab allows you to manage early access or restricted features inside your organization.\
This content is based entirely on the new feature flags interface.

You can:

* View all available feature flags
* Enable or disable a feature
* Assign access to specific users
* Remove user access
* Disable an entire feature flag for everyone

### Feature Flags List

All flags appear in the left panel. Selecting a flag shows its details and user access options.

<Frame>
  <img alt="Feature flags list" />
</Frame>

### No feature flags enabled yet

If a feature has no enabled users, you will see an empty state with an option to activate it.

<Frame>
  <img alt="Enable feature flag empty view" />
</Frame>

### Managing user access

You can:

* Add users to enable the feature for them
* Remove users to revoke access
* Disable the feature entirely with the **Disable Feature** button

<Frame>
  <img alt="Manage users for a feature flag" />
</Frame>

***

## Brand Setting

Brand Setting allows you to upload your organization's logo.\
This logo is displayed across the platform wherever branding is supported.

Supported formats:

* JPEG
* PNG
* File size under 500 KB

You can upload, preview, and save your logo.

<Frame>
  <img alt="Brand setting and logo upload" />
</Frame>

***


# Telemetry
Source: https://docs.aptlystar.ai/oraganization-console/telemetry



## Telemetry Overview

The Telemetry dashboard gives you a clear picture of how your workspace is being used.\
It helps you understand user activity, feature interaction patterns, and overall system health.\
Teams use this data to track adoption, identify friction points, investigate errors, and make informed product or workflow decisions.

<Frame>
  <img alt="Telemetry dashboard" />
</Frame>

***

## Key Metrics Summary

At the top of the telemetry page, you’ll find a quick snapshot of your system’s current activity.\
This includes how many users interacted with the platform, how many sessions took place, how many events succeeded or failed, and whether usage increased or decreased compared to previous periods.\
This section provides an instant health check, helping you gauge engagement and stability at a glance.

***

## Filters and Controls

Telemetry includes several filters that let you narrow down the data you’re viewing.\
Instead of scrolling through thousands of events, you can focus on specific time ranges, specific types of interactions, or even certain user groups.\
These controls make it easy to answer questions like:\
“Which features were used yesterday?”\
“Who encountered an error?”\
“What actions are happening most in a given project?”

Each filter helps you isolate a slice of behavior so you can better understand how users are interacting with the system.

***

## Status Overview and Event Trends

The status overview graph shows how activity changes over time.\
You can see whether interactions are increasing or decreasing, when peak usage occurs, and whether certain types of events (like applied actions or preview interactions) are happening more often.\
This view is helpful for identifying patterns, planning rollouts, and understanding user rhythm — especially after a release or major update.

The events over time chart makes it simple to spot spikes, dips, or unusual behavior that may require investigation.

***

## Interaction Insights

Below the main chart, you’ll find several panels that break down activity by different perspectives.

### Categories

This section shows which parts of the platform users are interacting with most.\
It helps you understand which areas are drawing attention and which might require deeper engagement efforts.

### Actions

This section highlights what users are doing — whether browsing, applying templates, navigating workflows, or performing setup actions.\
It gives you more context on *how* users move through the product.

### Feature Areas

This view surfaces which product modules are used most often.\
It’s useful for understanding what features drive value and which ones may need redesign or discovery improvements.

### Roles

Here you can see how different user roles engage with the system.\
This helps you confirm whether team members with different responsibilities are using features as expected.

### Sources

This section shows where events originate — for example, from the main UI or other entry points.\
It helps you understand how people interact with the system across different access paths.

### Client Versions

This view helps you track which client or widget versions are active.\
It’s valuable during phased rollouts or troubleshooting version-specific issues.

Each of these panels gives you a different angle on the same activity, helping you build a complete picture of how people use your product.

***

## Actor Metric

This section highlights the users or accounts contributing the most events or encountering the most issues.\
Instead of searching through logs manually, you immediately see which users generate the highest activity or where problems are concentrated.\
It’s especially helpful for debugging — for example, noticing that a specific user or integration triggers unusual behavior.

***

## Recent Event Logs

At the bottom of the page, telemetry displays a chronological list of individual events.\
Each event includes details like what action occurred, who performed it, what part of the system it came from, and whether it succeeded or failed.\
This log is particularly helpful when investigating user reports, validating new features, or confirming that background actions executed correctly.

<Note>
  Telemetry provides complete visibility into user behavior and system activity, helping teams make confident product decisions, diagnose issues quickly, and understand how features are performing in real time.
</Note>


# Usage Analytics
Source: https://docs.aptlystar.ai/oraganization-console/usage-analytics



The **Usage Analytics** page offers a centralized dashboard to monitor how AI agents are being used across the organization. It provides granular visibility into conversations, agent activity, and channel distribution — all of which help drive smarter decisions and operational insights.

***

<Frame>
  <img alt="Organization Usage Analytics view" />
</Frame>

## What You Can Track

This view gives you channel-wise and agent-wise breakdowns of:

* Total number of questions and answers (Q\&A) exchanged
* Channel-specific traffic (e.g., Playground, Web, WhatsApp, Discord, API)
* Which agents were most active
* How usage has trended over time

> 📈 This data helps identify engagement hotspots, uncover underused channels, and optimize where your AI is delivering the most value.

***

## Filter Options

The left panel includes interactive filters to customize your analytics view.

### 🗓️ Date Range

Select the timeframe you want to analyze:

* All Time
* Yesterday
* Last 7 Days
* Last 30 Days
* Custom Range (using start and end dates)

> Useful for comparing usage trends across months or analyzing spikes post-feature launches.

***

### AI Agents

Toggle visibility for each AI agent in your org:

* Check or uncheck individual agents
* Filter for **active** or **offline** agents
* Use "Select All" or "Clear All" for convenience

> Great for seeing which agents are driving the most engagement or are currently inactive.

***

### Channels

Analyze performance across different communication channels:

* Website
* Telegram
* Discord
* WhatsApp
* API
* Playground

You can include or exclude channels depending on your use case — for example, isolating API usage or comparing live chat vs web widget behavior.

***

## Visual Insights

In addition to summary cards, the dashboard now includes **graphical breakdowns** to make insights more intuitive:

* **Bar graphs** to compare agent activity and channel performance
* **Pie charts** to visualize distribution of queries across LLM models
* **Trend visualizations** to quickly spot spikes or drops

<Frame>
  <img alt="Usage Analytics with graphical breakdowns" />
</Frame>

These charts make it easier to not only track numbers but also **see patterns at a glance** — helping teams interpret data faster and make informed decisions.

***

## Interpreting the Results

Once filters are applied, the right panel will display visual summary cards for each channel. Each card shows:

* **Total Q\&A count** for that channel
* A unified metric across all selected agents and date range

## Why It Matters

This page helps your organization:

* **Monitor real-time usage** across all AI integrations
* **Diagnose drops** or spikes in channel traffic
* **Audit agent activity** across internal and public touchpoints
* **Allocate resources** to channels with highest user interaction
* **Optimize content** or responses where engagement is low

Whether you're scaling support agents, tracking usage by department, or reporting engagement to stakeholders — this dashboard is essential for **AI ops**, **QA teams**, and **product managers** alike.

***


# Users & Roles
Source: https://docs.aptlystar.ai/oraganization-console/users



## Users in Your Organization

The **Users** section displays a list of all members within your organization. You can assign them roles with varying permissions based on their responsibilities.

This enables clear role-based access control for managing projects, agents, and settings.

## Roles and Responsibilities

There are **three types of user roles** in the organization, each with distinct levels of access and responsibility:

### 1. Owner of Organization

* Full access to all organizational settings, users, and permissions
* Can add, edit, delete, or activate/deactivate users
* Can invite users as **Admin** or **Owner**

### 2. Admin of Organization

* Can manage users, data, and organization settings
* May have limited access to owner-level features

### 3. User / Agent Manager

* Can access assigned projects and resources
* Cannot manage other users or modify organizational settings

***

## User Management Actions

<CardGroup>
  <Card title="Add Users in Organization" icon="star" href="/oraganization-console/users#add-users-to-your-organization">
    Add and invite users to your organization and assign them roles.
  </Card>

  <Card title="User Console" icon="chart-simple" href="/oraganization-console/users#user-console">
    View and manage user details in the User Console.
  </Card>
</CardGroup>

***

## Add Users to Your Organization

To add a new user:

* Navigate to the **Users** section (1)
* Click on **Add User** (2)
* Fill out the following:
  * **Email address** (3)
  * **Role selection** (4)
  * **Assign to project(s)** (5)
* Click **Send Invitation** (6)

Once the invitation is accepted, the user will be successfully added.

<Frame>
  <img alt="Users list view" />
</Frame>

Below is the modal where you send the invite and define the user role:

<Frame>
  <img alt="Send invitation popup" />
</Frame>

## Bulk User Upload via CSV

In addition to adding users one by one, you can also **invite multiple users at once** using a **CSV or Excel file**.

This is useful for onboarding larger teams quickly — simply upload a file containing multiple email addresses.

<Frame>
  <img alt="Invite multiple users via CSV" />
</Frame>

#### CSV Upload Requirements

* **Accepted formats:** `.csv`, `.xls`, `.xlsx`
* **Limit:** Up to **50 email addresses per upload**
* **File rule:** Each email address must appear on a **separate row**
* **Required columns:**
  * `fullname`
  * `username`
  * `email` *(ensure all emails are listed in this column)*

You can use the **Choose File** option in the “Invite User” modal to upload your CSV.\
Once uploaded, the system validates the file format and displays any errors if the structure doesn’t match the required fields.

> 💡 **Tip:** Double-check that the CSV includes the column headers exactly as shown (`fullname`, `username`, `email`) to ensure smooth import.

## User Console

The **User Console** provides a complete view of your profile and access details within the organization.\
Here, you can view and update your account information such as:

* **Full Name**
* **Email ID**
* **Role** (e.g., Owner, Admin, or User)
* **User ID**
* **Time Zone**
* **Account Creation Date**
* **Status** (Enabled/Disabled)

<Frame>
  <img alt="User Console overview page" />
</Frame>

***

### Tabs in the User Console

The top navigation tabs allow you to explore different views:

* **All Users** – View a list of all members in the organization
* **Overview** – Manage your personal details and settings
* **AI Agents** – See agents created or managed by you
* **Teams** – Check which teams or projects you belong to
* **Login History** – Review all your login activities

This makes it easy to access teammate information, assigned projects, and your own workspace from one central view.

***

### Login History

The **Login History** tab displays detailed records of your login activity.\
It helps you track your access sessions and ensure account security.

<Frame>
  <img alt="Login history tab view" />
</Frame>

#### Information Shown in Login History

* **Login Date & Time** – When you last accessed your account
* **Username** – The name used during login
* **IP Address** – The network location of your session
* **User Agent** – The browser or device used to log in
* **Login Status** – Whether the attempt was successful or failed

> 💡 Use this tab to review your recent sessions and identify any unfamiliar login attempts.


# AI Agents of the Project
Source: https://docs.aptlystar.ai/project-console/ai-bots



An **AI Agent** is an intelligent agent designed to perform tasks, interact with users, and answer questions instantly based on your organizational or project-level data.

In AptlyStar, you can create agents **directly within a project**, allowing them to be scoped specifically to that project's datasets and goals.

***

## 🛠️ Steps to Create an AI Agent within a Project

<Steps>
  <Step title="Navigate to AI Agents inside Project">
    From the AptlyStar Dashboard:

    * Click on the **Projects** tab (1)
    * Select your project and go to the **AI Agents** section (2)
    * Click on **Create New Agent** (3)

    <Frame>
      <img alt="Open Project Agents" />
    </Frame>
  </Step>

  <Step title="Configure Your Agent">
    Fill out the agent creation form:

    * Enter the **Agent Name** (1)
    * Select the **Chat Model** and **Embedding Model** (2)
    * Link relevant **Data Stores** (3)
    * Click **Create** (4)

    <Frame>
      <img alt="Agent Configuration Screen" />
    </Frame>
  </Step>

  <Step title="Agent Successfully Created">
    Once created, your AI Agent will appear under the **AI Agents** tab of the selected project.

    <Frame>
      <img alt="Agent Successfully Created" />
    </Frame>
  </Step>
</Steps>

Looking to connect project-level agents to a data store? Check out our guide on [Linking Data Stores to Agents](/ai-datastores/ai-bots).


# Project Overview
Source: https://docs.aptlystar.ai/project-console/overview



The **Overview** tab in the Project section provides a quick summary of your project’s key elements, including:

* Total number of **AI Agents** created under the project
* General **project metadata** such as name and description
* Quick access to update project details

This is your starting point for managing AI initiatives tied to a specific project.

<Frame>
  <img alt="Project Overview Tab" />
</Frame>


# Project Settings - ObstraLog
Source: https://docs.aptlystar.ai/project-console/settings



The **Settings** tab in the Project Console displays **ObstraLog** instance information specific to your project.

ObstraLog allows you to:

* 📊 Monitor **LLM usage**
* 💰 Track **cost analytics**
* 📝 Capture **logs** and request-level metadata
* 🔍 Enhance visibility into AI performance per project

***

## 🔐 Access ObstraLog for a Project

To view the ObstraLog instance:

* Navigate to your **Project Console**
* Click on the **Settings** tab

You’ll see:

* An **ObstraLog Access Key**
* A **Unique Instance ID**

These credentials are automatically generated when your project is created.

<Frame>
  <img alt="ObstraLog Settings in Project" />
</Frame>

<Note>
  Each project has its own ObstraLog instance, scoped independently for accurate cost and usage tracking
</Note>


# Teams in Project Console
Source: https://docs.aptlystar.ai/project-console/teams



## Teams in the Project Console

Teams let you organize how different people collaborate inside a project.\
Instead of giving every user access to everything, Teams help you structure access based on roles, workflows, and responsibilities.

Teams are useful when you want to:

* Give only specific users access to a project
* Group members working on the same agent or dataset
* Restrict access to sensitive data stores or internal agents
* Maintain clear separation between multiple clients or departments

The Teams tab provides a clear view of each team, the number of members, and the resources assigned to them.

<Frame>
  <img alt="Teams overview screen" />
</Frame>

***

## Adding Users to a Team

You can add existing organization members to the team.\
This helps you control who can work inside the project without exposing it to everyone in the company.

Typical use cases:

* Adding developers/testers to a specific agent
* Allowing project-specific analysts to view data stores
* Giving clients access only to their assigned team

<Frame>
  <img alt="Add existing user modal" />
</Frame>

***

## Inviting New Users to the Team

If someone is not yet a member of your organization, you can invite them directly.\
This is useful for:

* Onboarding new employees
* Giving clients limited access to their project
* Adding contractors or temporary collaborators

You simply enter an email, choose a role, and send the invitation.

<Frame>
  <img alt="Invite user modal" />
</Frame>

*Users can also be removed at any time if they no longer require access.*

***

## Team Permissions Overview

Team Permissions give you fine control over what each team can access within the project.\
This includes:

* Which **data stores** the team can read from
* Which **AI agents** they can use or manage

This section provides a complete overview of the team's assigned resources, making it easy to manage access as your project grows.

<Frame>
  <img alt="Team permissions overview" />
</Frame>

***

## Adding Data Stores to a Team

Teams may need access to specific data stores for agent training, retrieval, or analysis.\
From this panel, you can assign any available data store to the team.

Typical use cases:

* Granting analysts access to only one knowledge base
* Allowing a client team to use their own dataset
* Keeping internal and external data stores separate

<Frame>
  <img alt="Add datastore modal" />
</Frame>

*You can revoke data store access at any point if requirements change.*

***

## Adding AI Agents to a Team

You can also assign one or more AI agents to a team.\
Members of the team will be able to view, test, or configure only the agents assigned to them.

This is useful when:

* Each team manages different agents
* You want isolation between client projects
* You need to limit access to experimental or internal agents

<Frame>
  <img alt="Add AI agent modal" />
</Frame>

*AI agent access can also be removed as needed.*

***

<Note>
  Teams help ensure every project remains organized, secure, and scoped correctly, giving each collaborator access only to what they need.
</Note>


# Use Cases
Source: https://docs.aptlystar.ai/usecase



Explore how AptlyStar's AI Agent can be tailored for different real-world scenarios. These use cases highlight how the agent improves productivity, automates tasks, and enhances user experience—while being customizable with your own data and embedding strategies.

<AccordionGroup>
  <Accordion title="🧑‍💼 HR Agent">
    Use AptlyAgent as an internal HR assistant for your organization. It can:

    * Answer FAQs about **policies, benefits, leave, holidays**
    * Automate **onboarding instructions** for new employees
    * Help users locate **forms, guides, and contacts**
    * Reduce repetitive queries to HR personnel

    ### Customization Suggestions:

    * Upload internal policy docs, PDFs, leave forms
    * Use **text-embedding** models for secure document comprehension
    * Use **starter questions** like:
      * *What’s the PTO policy?*
      * *How do I apply for medical leave?*
  </Accordion>

  <Accordion title="📚 AI Student Library">
    Deploy the agent as a **virtual study assistant** on campus portals or LMS platforms. It can:

    * Answer questions about **syllabi, textbooks, deadlines, courses**
    * Provide summaries of **reading material or lecture notes**
    * Help with **academic resource navigation**

    ### Customization Suggestions:

    * Upload class syllabi, past exam papers, course PDFs
    * Enable **web search fallback** for external references
    * Add starter questions like:
      * *What is the syllabus for Data Structures?*
      * *Show me previous year papers for Algorithms.*
  </Accordion>

  <Accordion title="💬 Customer Support for SaaS Product">
    Improve user support by embedding the agent in your SaaS platform. It can:

    * Handle **common troubleshooting steps** (e.g. login issues, billing)
    * Answer **product usage queries**
    * Deflect support tickets with **instant replies**
    * Route high-priority cases to human support

    ### Customization Suggestions:

    * Train on support documentation, FAQs, API guides
    * Embed links to video tutorials or live chat for escalation
    * Suggested prompts:
      * *How do I reset my password?*
      * *What’s the rate limit for API calls?*
  </Accordion>
</AccordionGroup>

***

## 🧩 Embedding the Agent

You can integrate any of these agents via a simple HTML snippet on your website or product dashboard. This makes the agent accessible as a floating widget on the bottom-right corner.

Refer to our [Embedding Guide](https://docs.aptlystar.ai/ai-bots-console/channels) for details.

<Frame>
  <img alt="Embedded Widget" />
</Frame>

***

By tailoring your agent with the right training data and interface, you can create a powerful assistant for your team, customers, or students—without needing to build from scratch.