Handling email inquiries is one of those areas where response time has real business significance. A customer who waits several hours for an initial response may choose a competitor in the meantime. On the other hand — forcing employees to repetitively analyze very similar messages, extract the same parameters, and create very similar responses is an inefficient use of their time and skills.

The Problem: Time and repetitiveness

A B2B service company received dozens of email inquiries a week. Each of them required:

• reading the message and understanding the customer's intent,
• checking any attachments (specifications, technical drawings, forms),
• extracting key order parameters (quantity, deadline, specifics),
• comparing with the current price list and availability,
• writing a coherent response or preparing a quote.

Each such operation took from a dozen to several dozen minutes. With a large volume of inquiries, employees spent a significant part of the day on tasks that were almost identical in their structure — differing only in data.

Business Context

AI in correspondence handling is an area that many companies avoid out of fear of losing communication quality. This is a justified fear — but it often stems from the mistaken assumption that an AI assistant is meant to replace the employee, rather than support them.

The key design decision in this case was: AI prepares a draft, a human verifies and sends it. No message goes out without the employee's approval. This is not a compromise — it's the right model for working with AI in environments where communication quality matters.

The Solution: An assistant integrated with email

We designed an assistant operating directly in the team's work environment — integrated with the existing email client and internal databases:

• Message content analysis — the language model identifies the type of inquiry, extracts key parameters (product, quantity, deadline, delivery location), and classifies priority.
• Working with attachments — the system supports the most common document formats: PDF, XLSX, DOCX. It extracts tabular data, technical specifications, and order conditions.
• Response proposal — based on the extracted data and the history of similar inquiries, the model generates a draft response or a preliminary quote, which the employee can accept, modify, or reject.
• Customer history — the assistant has access to the history of correspondence with a given customer, which allows it to take into account previous arrangements and terms of cooperation.

What working with the assistant looks like in practice

The employee opens the inbox — next to new messages, they see a summary automatically generated by the assistant: type of inquiry, key parameters, suggested priority. For messages that require a response, they see an "Open draft" button. They click, review the proposal, make any corrections — and send.

In the case of more complex inquiries, the assistant highlights elements it wasn't sure about and leaves them blank or marked "to be completed". The employee sees exactly what the system did on its own and what requires their assessment.

What about data security and confidentiality?

This is an important issue that must be resolved before choosing the technology. In this project, customer data did not leave the company's infrastructure — the language model operated in a local environment or within a dedicated instance with access restrictions. For companies that can use external APIs (e.g., OpenAI), there are mechanisms for anonymization and controlling the scope of data transferred to the model.

Result

The handling time for a typical inquiry has been significantly reduced. Employees could handle more inquiries without increasing headcount. Communication quality — measured by terminology consistency and response completeness — improved because the assistant ensures no parameter is overlooked.

Importantly: the implementation did not require training employees in AI. The assistant is an interface that fits into their existing way of working — it doesn't fundamentally change it.

What can another company learn from this?

If your team spends a lot of time analyzing similar messages and creating similar responses — it's a good sign that AI can genuinely help here. You don't have to decide right away whether you want to fully automate communication. Start with the "AI as an assistant" model — it's a safe way to check how technology works in your company's conditions.

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