Cinimex unveils AI coach for enterprise sales that fact-checks claims against documentation

Cinimex has detailed a prototype AI coach for B2B sales. The tool trains sales reps to run negotiations, handle objections, and avoid overstating what a product can do. The system is already in testing, and a change of architecture made it noticeably more stable. The team expects regular training sessions to increase requests for commercial proposals by at least 15%.

From failure to simulator

The idea grew out of a common B2B sales problem. A new rep can confidently handle the first part of a meeting, then get lost once the client starts asking tough questions about the product, the documentation, and the benefits of switching. In consumer sales, a mistake like that is survivable. In the enterprise segment, every failed meeting costs a lead, team time, and money. For companies with long deal cycles, these failures are especially painful and expensive.

So Cinimex set out to build a negotiation simulator rather than a simple chatbot. The coach has to take a rep through the entire conversation, from the greeting to agreeing on next steps, push back realistically, and give feedback afterward. The team trained it on two main types of data: visit scenarios with goals and typical objections, and product documentation in PDF form, so the agent could rely on facts rather than "gut feeling."

Keeping the architecture under control

The team tried three approaches. It rejected a single-agent design because of context overflow: in long conversations, the model started losing the thread and following instructions less reliably. A classic multi-agent approach didn't work either. It was flexible but behaved too much like a black box, making it hard to see why one agent handed a task to another or where an error originated. The team settled on sequential prompting: a chain of narrow steps with controlled JSON output and separate prompts for each stage of the conversation.

• selecting a prompt based on the conversation phase
• generating several candidate replies
• having a separate agent choose the best reply
• sending the final reply to the sales rep

The first version still failed: only 2 dialogues out of 8 were judged successful. The team then added a second "editor" agent that picks the best of several generated replies. This two-stage pipeline took load off the main prompt and gave the model more variety without sacrificing control. In the second iteration, the result rose to 7 successful dialogues out of 8, and the replies themselves sounded more natural. That became the project's turning point.

Catching fabrications

Next, the team made the simulation harder. The coach gained difficulty levels: on easy, the virtual client is more agreeable; on medium, it demands arguments; on hard, it scrutinizes every claim the rep makes. This is where the team added RAG, so the agent could check claims against the documentation and catch unsupported promises. Without it, the coach would either believe any promise a rep made or argue without any grounding in facts.

Here's how it works: the rep makes a claim about the product, the system extracts the specific assertion, finds a relevant passage of documentation via vector search, and then passes both the claim and the retrieved text to the model for a verdict. If a rep says, for example, that the cloud version of the product supports 500 concurrent users, but the documentation only covers a test with two, the agent doesn't simply agree and instead gently points out the discrepancy. According to the team, each prompt was rewritten about 12 times on average to achieve this behavior.

What it means

The Cinimex project shows that enterprise AI agents are increasingly moving away from "smart chatbots" toward practical training tools with verifiable logic. If the format delivers on its projected growth in proposals and pilot conversions, AI could take on the role of an always-available coach in B2B sales, one that relies on documentation rather than improvisation. For in-house sales teams, this is no longer an experiment for its own sake but a potential tool for scaling training.