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An AI-assisted tool that drafts support responses from a company's knowledge base for human agents to review and send.
A growing support team was spending significant time searching internal documentation to answer repetitive customer questions accurately.
Dvexo built a retrieval-augmented AI assistant that surfaces relevant knowledge-base content and drafts suggested responses for agents to review before sending.
Support agents reach for internal documentation less often, using AI-drafted responses as a starting point that they review and personalize.
A growing support team answered the same questions repeatedly, each time hunting through internal documentation to get the details right. The obvious idea — let an AI answer customers directly — was the wrong one: a confidently incorrect answer about billing or account access costs far more than a slow one.
The requirement was therefore not automation but acceleration: draft the response, cite where it came from, and keep a human in the loop before anything reaches a customer.
The system retrieves passages from the knowledge base and generates only from what it retrieved. A model answering from its own weights will invent plausible policy details; one constrained to retrieved passages will not.
Every draft displays the passages it was built from. Agents verify in seconds rather than trusting blindly, and a wrong draft becomes visibly wrong instead of quietly wrong.
When retrieval returns nothing sufficiently relevant, the assistant declines to draft instead of producing something generic. Knowing when the tool has nothing useful to say is what makes agents trust it the rest of the time.
We built an evaluation set from historical tickets with known-good answers and measured changes against it, so prompt and retrieval tuning was driven by measurement rather than impression.
Agents start from a drafted, sourced answer rather than a blank box and a documentation search, and they retain full editorial control over what is sent.
Because sources are shown inline, the tool surfaces gaps in the documentation itself — repeated low-confidence retrievals point straight at what needs writing.
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