Telegram Bot Image Recognition & Multimodal Ticket Routing: Automatic Pre-classification and Agent Assignment for User Screenshots and Product Images

A user sends a screenshot of a fault, and the bot automatically identifies it as an “after-sales” issue, directly assigning it to the after-sales agent group. Another user sends a product image, and the bot determines it as “electronics inquiry,” routing it to the sales agent. This isn’t science fiction—through Telegram Bot image recognition and multimodal ticket routing, your customer support team can complete pre-classification in seconds, significantly boosting response speed.

This article will guide you step by step from scenarios, technical pipelines to TG-Staff operational configuration, enabling you to implement this solution.

Why Do Telegram Bots Need Image Recognition and Ticket Routing?

The limitations of pure-text bots are obvious: when a user sends an image, the bot either forwards it directly to all agents (causing chaos) or can only reply with “Please describe your issue” (increasing user effort). Agents have to manually view each image, determine the category, and decide who handles it—this process slows down first response time and is prone to errors.

By introducing multimodal image recognition (such as GPT-4V, Claude Vision), the bot can:

• Automatically extract image content: Identify error codes in screenshots, product styles, credential information, etc.
• Tag: Generate classification labels like “after-sales,” “sales,” “verification,” etc.
• Trigger routing: Assign conversations to designated agents or agent groups based on tags.

The value of this mechanism lies in: reducing manual judgment steps, letting the right person handle the right issue at the first moment. For customer support teams processing hundreds of images daily, this can save over 30% of response time.

Common Application Scenarios for Multimodal Image Recognition

After-Sales & Fault Screenshot Recognition

When a user sends an error screenshot (e.g., payment failure, system crash) or a fault photo (e.g., product damage), the bot recognizes it and automatically tags it as an “after-sales” ticket, routing it to the after-sales agent group. When the agent opens the conversation, they already see the recognition tag and image content, enabling immediate processing.

Product Image & Demand Matching

In e-commerce scenarios, when a user sends an image of a dress or digital product, the bot identifies the category, color, approximate model, and automatically assigns it to the corresponding operations or sales agent. If the user sends multiple images simultaneously, tags can be aggregated to generate a “multi-product inquiry” marker.

Identity Verification & Credential Screenshots

In Web3 or financial projects, users send payment receipts, membership card screenshots, or KYC materials. The bot identifies key information (such as transaction hash, amount, date) and automatically creates a verification ticket, routing it to the verification agent group. Agents no longer need to repeatedly ask “Please provide a screenshot of the credential,” and can start verification based on the already extracted information.

Solution Design: Bot Image Recognition + Ticket Routing Technical Chain

The complete chain consists of 6 steps:

1. User sends an image to the Telegram Bot: Telegram Bot API natively supports image reception, no extra configuration needed.
2. Bot receives the image and calls a multimodal recognition API: Send the image to GPT-4V, Claude Vision, or other vision models to obtain recognition results (labels, intent, key information).
3. Extract recognition results: Parse classification labels (e.g., after_sale, sales, verification) and additional information (e.g., error codes, product descriptions) from the API response.
4. Assign agents via routing rules: Map recognition labels to agent group IDs in the TG-Staff project. TG-Staff’s session routing rules support a “designated agent” mode, allowing automatic assignment based on labels.
5. Create a session and notify agents: Agents receive a new session notification on the TG-Staff web interface, along with the user’s image and recognition labels.
6. Agent responds: Based on labels and image content, the agent starts processing.

The key here is how “recognition results” integrate with TG-Staff’s routing links, session labels, and agent permissions. For example, you can carry the ?tag=after_sale parameter in a routing link; when users click it, they directly enter the after-sales queue.

How to Configure Image Pre-classification and Agent Routing in TG-Staff

The following steps are based on the TG-Staff console (https://app.tg-staff.com/），你需要先完成注册并绑定 Telegram Bot.

Step 1: Configure Image Reception and Recognition Trigger in the Bot Backend

There are two approaches:

• Method A (Visual command flow): In TG-Staff’s drag-and-drop flow editor, add a “Receive Image” node, then call an external API (via an HTTP request node) to send the image to the multimodal recognition service. After receiving the recognition result, use a “Set Label” node to save the label.
• Method B (External API callback): If your Bot backend is independently deployed, you can call the recognition API after receiving the image, then create a session with labels via the TG-Staff API.

Step 2: Create Multiple Agent Groups

In the TG-Staff project, navigate to the “Agent Management” page:

• Create an “After-Sales Group” and add after-sales agent accounts.
• Create a “Sales Group” and add sales agent accounts.
• Create a “Review Group” and add review agent accounts.

Each agent group can have different project permissions (e.g., only view sessions assigned to that group).

Step 3: Set Up Session Routing Rules

Go to “Project Settings” → “Session Routing”:

• Select the “Designated Agent” mode.
• Map recognition labels (e.g., after_sale) to corresponding agent group IDs.
• Save the rules.

When a user sends an image and triggers recognition, TG-Staff automatically assigns the session to the corresponding agent group. If multiple agents are in the group, the system further assigns based on “round-robin” or “online priority” rules.

Step 4: Use Routing Links to Carry Recognition Parameters

TG-Staff’s routing links (magic links) support capturing URL parameters. In the recognition flow, you can encode results as ?tag=after_sale or ?category=electronics; when users click the link, they automatically enter the corresponding ticket queue.

This is particularly useful for ad traffic scenarios: when a user clicks a routing link from an ad channel, the system automatically identifies the source and assigns an agent, eliminating the need for users to manually select the issue type.

Step 5: Agent View and Response

After logging into the TG-Staff web interface, agents see new sessions in the “Live Chat” panel. The session card displays recognition labels (e.g., “After-sales - Fault Screenshot”). Clicking reveals the user’s image. Agents can reply directly or use the auto-translate feature to handle multilingual users.

Precautions and Common Pitfalls

Image Recognition Models Have Misjudgment Risks

Multimodal models are not 100% accurate. For example, an image containing a product and background might be misjudged as a “scene image” instead of a “product image.” It is recommended to set up a manual review mechanism: agents can manually correct the labels during conversations and provide feedback to the model for future optimization.

Large Volumes of Image Requests May Trigger API Rate Limits

If your team processes thousands of images daily, you need to monitor the call limits of the recognition API. It is recommended to implement queues and caching: for similar images sent by the same user within a short period, reuse the previous recognition results. TG-Staff’s visual workflow supports conditional logic, allowing you to add deduplication rules.

User Privacy: Images Should Not Be Stored Long-Term

Image data may contain sensitive information (e.g., ID cards, payment receipts). It is recommended to promptly delete image files from the Bot server after recognition. TG-Staff’s session messages are stored in the cloud, and you can configure data retention periods for automatic deletion.

Routing Rules Should Not Be Too Granular

Overly detailed agent group divisions (e.g., “After-Sales-Refund Group,” “After-Sales-Exchange Group,” “After-Sales-Repair Group”) may lead to some groups being idle for long periods while others are overloaded. It is recommended to start with broad categories like “After-Sales,” “Sales,” and “Review,” then refine based on data.

Checklist: 5 Key Steps to Implement Image Recognition Ticket Routing

1. Ensure the Bot Can Receive Images: Telegram Bot API natively supports this—no additional development required. If your Bot is already running, just make sure the message handling logic includes image types.
2. Integrate a Multimodal Recognition API and Define Classification Labels: Choose GPT-4V, Claude Vision, or an open-source model (e.g., LLaVA), and define 3–5 labels (e.g., 售后, 销售, 审核, 其他). Test recognition accuracy and adjust prompts as needed.
3. Create Corresponding Agent Groups and Routing Rules in TG-Staff: In the console, create agent groups, set up “Assign Agent” routing rules, and map labels to group IDs.
4. Test the Full Workflow: Send an image as a test user and verify: Bot receives → recognizes → labels → routes to the correct agent → agent receives notification and views the image.
5. Monitor Continuously After Launch: Check agent response times, recognition accuracy, and user satisfaction. If some labels are inaccurate, adjust model prompts or add training samples.

Frequently Asked Questions

Q: Does TG-Staff come with built-in image recognition?

A: TG-Staff itself does not include a multimodal recognition model, but you can send images to a recognition service (e.g., GPT-4V, Claude Vision) via the visual command flow or external API callbacks, and use the recognition results as labels or routing criteria. TG-Staff handles ticket assignment and agent management after recognition.

Q: How can images be automatically assigned to different agents after recognition?

A: In TG-Staff, you can create multiple agent groups (e.g., “After-Sales Group,” “Sales Group”) and set the routing rule to “Assign Agent” mode. Map the recognition results to the corresponding agent group IDs to achieve automatic assignment based on labels.

Q: Can routing links carry image recognition parameters?

A: Yes. TG-Staff’s routing links (magic links) support capturing URL parameters. You can encode the recognition results as parameters (e.g., ?tag=after_sale), and when users click the link, they will automatically enter the corresponding ticket queue.

Q: Which industries are suitable for image recognition ticket routing?

A: It is suitable for industries where users frequently send images and require multi-agent division, such as e-commerce (product image inquiries), after-sales (fault screenshots), Web3 (credential verification), and education (homework screenshots).

Q: Can agents see the images sent by users?

A: Yes. TG-Staff’s real-time two-way chat supports multimedia messages like images and files. Agents can view user-sent images directly on the web interface and respond quickly based on recognition labels.

---

Next Steps

• Sign up for a free 3-day trial of TG-Staff to experience agent management and routing features: https://app.tg-staff.com/
• Check the official documentation for details on routing links and content moderation configuration: https://docs.tg-staff.com/
• Contact the @tgstaff_robot customer service Bot for one-on-one configuration guidance