Summarizing and sorting customer requests with AI

When customer requests come in by email, through web forms and as transcribed phone calls, someone spends part of their day reading them, guessing who should handle them and forwarding them. Urgent requests get lost among simple questions. Sorting and summarizing are tasks where AI is reliable and easy to check. Here is how I set them up.

What AI brings to request sorting

For each incoming message, AI can produce in a few seconds:

• a category: order, delivery, billing, technical, complaint, sales inquiry;
• an urgency level: based on criteria you define (customer blocked, deadline close, tone of the message);
• a two-line summary, useful when the message is long or contains a thread;
• the key details extracted: order number, product reference, customer name;
• routing to the right team or person;
• optionally a draft reply, which the agent reviews and completes.

All of this can be checked at a glance, and mistakes are easy to fix: this is exactly the kind of task where I recommend starting with AI.

In practice, the agent opens their queue and sees, for each request, the category, the urgency, the summary and the extracted details, with the original message one click away. They no longer read every message in full to decide what to do with it: they read the summary, check the category, and open the full message only when the request is theirs to handle. Requests the AI is unsure about go to a manual sorting queue instead of being guessed.

Sample sorting grid

An illustrative example, to adapt to your organization:

What it costs

These amounts exclude VAT and come from my pricing.

In the estimator, built-in AI counts for $2,800 (€2,500)excl. VAT, a dashboard for $1,700 (€1,500)excl. VAT and notifications for $1,200 (€1,000)excl. VAT, on top of the application’s core. AI API usage comes on top, billed by the provider. I do not promise response times improved by a specific percentage: we measure before and after, on your own requests.

The rules I apply

1. The reply stays human for anything that commits the company: goodwill gestures, deadlines, refunds.
2. Every categorization can be corrected in one click, and every correction is logged.
3. Data sent out is limited to the content of the request, with a provider whose business terms exclude training on your data. The precautions are covered in what data to share with AI.
4. Simple metrics: recategorization rate, time to first response, volume by category.
5. A fallback: if the AI is unavailable, requests land in the general queue as before.

When it is not the right solution

• Volume is low: someone reading twenty messages a day sorts them as fast as AI does.
• Requests already arrive structured: a well-designed form with a list of reasons sorts at the source. In a client portal, customers pick the category themselves and attach their order.
• Your support tool already offers automatic sorting that works on your messages: no need to rebuild it.
• The problem is handling time, not sorting: if the team is understaffed, AI will make the queue easier to read, not shorter.

For returns and after-sales service in online retail, the page on e-commerce returns management shows how to combine a structured form with automatic sorting.

Where to start

1. Export one month of requests, anonymized if needed.
2. Define your categories and urgency criteria with the teams that handle requests.
3. Run a one-week audit and a prototype: the AI sorts last month’s requests, and your teams compare with what they would have done.

To go further on customer relations, see what actually works with AI in customer service. The full approach is on the AI integration page.