An internal AI Q&A assistant for company and product knowledge. It used retrieval-augmented generation to answer questions from approved internal materials, rather than relying on open-ended model knowledge.
The system also captured feedback, unresolved questions, and weak answers, so the team could continuously improve the underlying knowledge base. Company-specific architecture details and performance metrics cannot be shared.
The company had a growing amount of product, operational, and support knowledge spread across internal documentation, team knowledge, and repeated Q&A.
That made it hard for people to get consistent answers quickly, and created repeated manual work for those who kept answering the same questions.
The real problem went beyond adding a chatbot. It meant building a reliable knowledge workflow:
- ▹retrieve the right source material
- ▹generate grounded answers
- ▹avoid hallucinating when context was missing
- ▹capture user feedback and recurring unanswered questions
- ▹turn those gaps into improvements to the knowledge base
The assistant needed to get better as the team used it.
- ▹RAG-based Q&A flow over curated company knowledge
- ▹Document ingestion process for approved internal content
- ▹Chunking and indexing workflow for retrieval
- ▹Semantic search matching questions to relevant source material
- ▹Grounded answer generation from retrieved context
- ▹Fallback behavior for low-confidence or missing-context queries
- ▹Feedback capture for answer quality and usefulness
- ▹Gap-identification workflow for unanswered or poorly answered questions
- ▹Review loop for the team to update source materials based on repeated gaps
- ▹Reporting view surfacing common questions, weak answers, and missing docs
The main decision was to treat the assistant as a knowledge system rather than a generic chatbot, designed around three principles:
Responses are constrained to retrieved source material rather than open-ended model knowledge, which is what makes it a tool people actually trust internally.
When context is absent, the system says so rather than papering over the gap with confident output; a known answer is worth more than a plausible one.
Failed or low-quality answers feed back into improving the source material, so the product helps the team see where documentation is unclear, incomplete, or outdated.
A standard RAG architecture, deliberately kept close to trusted source material rather than maximizing model autonomy:
The goal was a reliable, reviewable workflow: an assistant that stays close to trusted material and improves through operational feedback.
The assistant improved access to internal knowledge, reduced repeated manual Q&A, and created a structured feedback loop for improving company documentation.
It also gave the team visibility into what people were asking, where the knowledge base was weak, and which topics needed better source material. Specific usage, accuracy, and operational-impact metrics cannot be shared.