Telegram automated AI customer service intent recognition configuration guide: keywords, command process and manual transfer rules

Teams that use Telegram Bot for customer service may face hundreds or even thousands of repeated questions every day - “How much does it cost?” “How to ship it?” “Can I get a refund?” Manual responses one by one are slow and expensive. Configuring Automated AI Customer Service Intent Recognition so that the Bot can automatically understand what the user wants to do and give an accurate answer, and only switch to manual work when necessary, is a key step to improve efficiency.

This article takes TG-Staff as an example to help you build a complete intent recognition process from scratch: define keywords → build a visual command process → configure diversion rules → monitor optimization. The whole process is zero-code, suitable for those in charge of operations and customer service.

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Why does Telegram’s automated AI customer service require intent recognition?

Intent Recognition is the Bot’s ability to automatically understand the user’s intention to ask questions. Without it, Bot can only reply to fixed content mechanically. If the user says “I want to check the logistics” and “Where is the express delivery?”, Bot may trigger different processes respectively, or even be unable to recognize them.

The core value of configuration intent recognition:

• Reduce manual repeated answers: FAQ, order inquiries, price inquiries and other high-frequency questions are automatically answered, and agents only handle complex conversations.
• Improve response speed: Users will reply within seconds after sending a message, no need to wait in line.
• Reduced agent costs: One agent quota can cover more sessions because most of the traffic is automatically digested.
• Improve conversion rate: User questions are answered quickly and are less likely to be lost.

In the Telegram scenario, intent recognition is usually implemented through keyword matching + command process: the user input contains specific keywords (such as “price” and “return”), and the Bot automatically jumps to the corresponding conversation process.

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Step 1: Define common user intentions and keywords

The basis of intent recognition is the intent list. Spend 30 minutes sorting out the 5–10 most frequent questions in your business and configure trigger keywords for each intent.

Intent List Examples and Keyword Design Tips

Intent Category	Business Scenario Example	Trigger Keywords (Chinese)	Trigger Keywords (English/Others)
Price consultation	Pre-sale price inquiry	Price, how much, quotation, fee	price, cost, quote
Order inquiry	After-sales inquiry	Order, logistics, express delivery, where is it	order, tracking, delivery
Returns and refunds	After-sales complaints	Returns, refunds, exchanges, dissatisfaction	return, refund, exchange
Technical support	Usage problems	How to use, error reporting, unable to log in, tutorials	help, guide, error, how to
Human customer service	Transfer to manual	Human, customer service, transfer to manual, complaint	agent, human, support, complaint

Keyword design skills:

• Use phrase matching instead of exact matching: For example, “price” can match “how much does this product cost”, but “price list” may be hit by mistake. It is recommended to set the “include” matching mode in the TG-Staff visualization process.
• Covering synonyms and common expressions: Users will not just say “order”, but may also say “order checking”, “logistics” and “express delivery”.
• Multi-language coverage: If your user group has English users, configure English keywords at the same time. TG-Staff supports multi-language keywords and can cover a wider area with automatic translation.
• Utilize Telegram’s native commands: Use built-in commands such as /start, /help, /menu as intention trigger points, allowing users to enter the corresponding process through menu selection.

Strategies to avoid keyword conflicts and false triggers

The more keywords there are, the greater the risk of accidental hits. The following methods can reduce the false touch rate:

1. Set negative keywords: For example, if you configure the keyword “return” for “return and refund”, but the user says “I want to know about the return policy instead of actually returning the product,” you can first set a confirmation step in the process: “Do you want to apply for a return? Yes/No.”
2. Keyword priority sorting: In the TG-Staff command process, you can drag and drop to adjust the process order. Put precise intentions (such as “return process”) in the front and general intentions (such as “help”) in the back to avoid being intercepted by the general process.
3. Test Verification: After the configuration is completed, test the Bot response using different expressions (complete sentences, colloquialisms, typos). For example, enter “Why haven’t the things I bought arrived yet?” to see if the order inquiry process will be triggered correctly.
4. Regular review of unmatched conversations: In TG-Staff user portraits and statistics, view “unidentified” conversation records and add keywords to these new expressions.

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Step 2: Use visual command process to build Bot automatic reply

After defining the intentions, you need to use command flow to convert each intention into specific dialogue steps. TG-Staff provides a drag-and-drop visual editor to build multiple rounds of interactions without programming.

Build welcome and menu branches

The first time the user talks to the Bot, send the /start command. The process design is as follows:

1. Trigger node: keyword “/start” or “start”.
2. Welcome message: Output a message containing brand introduction and main function buttons, for example:

   您好！欢迎使用 [品牌名] 客服。请选择您需要的服务：
   [产品咨询] [订单查询] [技术支持] [转人工]

3. Button Jump: The user clicks “Product Consultation” → enters the corresponding price FAQ process; clicks “Convert to Manual” → enters the queuing to manual process.

Best Practice: Keep your welcome message no longer than 3 lines + 4 buttons to avoid information overload. The button text should be consistent with the intended keyword (for example, the button should say “Product Consultation”, and “product”, “consultation” and “price” should be set as the trigger keywords for the process).

Conditional branching and dynamic data collection

More complex scenarios require dynamic adjustments of the process based on user input. For example, order inquiry process:

1. Ask for order number: Bot replies “Please provide your order number (starting with ORD):”.
2. User input: User input such as “ORD20241001”.
3. Conditional judgment: If the input matches the ORD\d+ format, then go to the next step (such as automatically querying the order status and replying); if the input does not match, it will prompt “Format error, please re-enter”, and it will automatically switch to manual after repeating 3 times.
4. Data Saving: Save the order number entered by the user to the user portrait field (such as last_order_id) to facilitate subsequent viewing by agents.

TG-Staff’s visual process supports variable extraction (such as {{user_input}}) and conditional branching (such as “matching format” → yes/no), which can easily implement the above logic.

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Step 3: Configure diversion rules to realize intelligent conversion to manual

No matter how intelligent the Bot is, there are always scenarios that require manual intervention - complex complaints, personalized needs, and multiple unrecognized situations. Configure distribution rules to automatically transfer these sessions to idle agents.

The core of the diversion rules is “when to transfer to manual” and “to whom”.

When to switch to manual:

• Users enter keywords such as “manual” and “customer service” to be converted to manual.
• Set a timeout in the process (such as the user not replying for 60 seconds) or multiple incorrect inputs (such as the order number format being wrong three times).
• Set the “Do you need manual assistance?” option at the end of the process, and the user selects “Yes”.

To whom: Configure the diversion rules in the TG-Staff project settings:

• Allocation in turns (default): Allocate new sessions to authorized agents in order, suitable for scenarios with a fixed number of agents and balanced workload.
• Online Priority: priority will be given to the currently online agents, and will be assigned in turn when all agents are offline. Suitable for scenarios where agents work in shifts or some agents are not online all day.

Diversion links (standard version and above) are another important tool: you can use TG-Staff official short links (such as https://app.tg-staff.com/{code}) in advertisements and social media posts. After users click, they will automatically jump to your Bot and carry source parameters (such as utm_source=facebook). In conjunction with the diversion rules, a complete conversion link of “advertising → diversion link → Bot automatic reply → manual agent acceptance” can be achieved.

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Step 4: Monitor and optimize intent recognition effects

Once the configuration is complete, don’t forget about it. Monitor data regularly and continuously optimize.

Key Indicators:

• Intent hit rate: The proportion of sessions that are successfully matched by keywords and trigger the process. Ideal value > 80%.
• Manual transfer rate: The proportion of final transfers to manual agents. If > 30%, it means that the automatic reply coverage is insufficient and the intent needs to be added or the process needs to be optimized.
• Unmatched Sessions: User messages that the Bot does not recognize. This is the focus of optimization – check it once a week to add keywords to these new expressions.

Optimization method:

1. Add high-frequency unmatched: Add the high-frequency phrases in the “unmatched” list to the keywords corresponding to the intent.
2. Adjust keyword matching mode: If the false hit rate is high, change from “contains” to “exact match” or add negative keywords.
3. Optimize the number of process steps: If the automatic reply process for an intention is too long (more than 5 steps), users may give up midway. Consider simplifying or adding a quick entry for “transfer to manual”.

TG-Staff Professional Edition provides user portraits and data statistics. You can view the number of triggers for each intention, average processing time, transfer to manual nodes, etc. to help with precise optimization.

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Step 5: Advanced techniques - combining automatic translation and content risk control

For cross-border or multilingual teams, the following two features can be combined with intent recognition to improve coverage and compliance.

Automatic translation: Enable automatic translation in TG-Staff (the standard version includes AI translation, and the professional version supports DeepL/Google professional translation). User messages seen by the agent are automatically translated into the set language, and the agent’s reply is automatically translated back to the user language. This will not affect the intent recognition logic - keywords are still matched in the original text (for example, if the user posts “price” in English, the Bot can still trigger the price consultation process). It is recommended to add both Chinese and English versions when configuring keywords.

Content Risk Control: If your business involves cryptocurrency, finance, or sensitive information, it is recommended to create content risk control rules in the professional version. For example, if the payment address (TRC20/ERC20 address fragment) is set as a risk word, if the agent includes this address when replying, the system will pop up a secondary confirmation window or prevent the sending. Detection can also be added to the intent recognition process: when the user mentions “payment”, “address” and “recharge”, the session is automatically marked as high risk, and the agent receives a reminder after switching to manual.

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FAQ

**Q: Does automated AI customer service intent recognition require programming skills? ** Answer: No need. Taking TG-Staff as an example, keywords and command processes are configured through the visual drag-and-drop editor, without the need to write code; just sort out the business intentions and corresponding answers in advance.

**Q: How to prevent Bot from mistaking user input as keyword trigger? ** Answer: It is recommended to use phrase matching instead of exact matching, and set negative keywords (such as excluding words such as “test” and “example”). In addition, a confirmation step (such as “Do you want to check the order status? Yes/No”) can be added to the process to reduce the rate of false touches.

**Q: Does the manual transfer rule support allocation according to agent skills? ** Answer: Currently, TG-Staff supports configuring the customer service range (all customer service or designated customer service) by project, but does not currently support automatic allocation by skill tags. You can use diversion links to achieve similar effects with different Bot projects, or combine user portraits to manually transfer sessions.

**Q: Will automatic translation affect the accuracy of intent recognition? ** Answer: Automatic translation is performed when the message is sent/received and does not directly participate in the intent recognition logic. It is recommended to use the multi-language version (such as English keywords + Chinese keywords) when configuring keywords, or enable the professional version multi-language support to cover a wider range of scenarios.

**Q: Can all features be tested during the free trial? ** Answer: Yes. After registration, you can enjoy a 3-day free trial, which supports testing the core functions of the standard and professional versions (such as intent recognition, offloading, and command processes); after the trial expires, you need to subscribe to a package to continue using it.

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You can start building your Telegram automated AI customer service now: register [TG-Staff console] (https://app.tg-staff.com/) → import your Bot → configure keywords and command processes → experience automated customer service. If you need help, contact @tgstaff_robot or check out the official documentation.