Not a toy demo — a deployed assistant your users can hit from day one. Vector index built from your corpus, retrieval evaluated against a real question set, hosted with auth and rate limits. Provenance preserved on every answer.
An LLM that makes up answers is not a chatbot — it's a liability.
Most "AI chatbot" builds glue an LLM onto a website and call it done. The bot sounds confident. The bot is also making things up — half the time about topics where being wrong matters.
The fix isn't a smarter model. The fix is grounding every answer in a real document the bot can cite — and showing that citation to the user.
Retrieval-augmented generation is the fix. The bot searches your real corpus first, retrieves the relevant chunks, and the LLM writes its answer from that — not from training data the model half-remembers.
Every response ships with the source chunks attached. Users can verify. Your support team can audit. Hallucinations don't go to zero, but they drop hard.
Two human gates. Four production stages. Retrieval gets evaluated against a real question set before anything goes live to users.
Scoping call. What documents, what user questions, what hosting target. We agree on the test question set before building — so we measure what matters.
Document ingestion built to re-run. Chunking strategy tuned to your content type. Provenance metadata preserved on every chunk.
Embedding model selected (Claude / OpenAI / open-source — your call). Vector store wired. Retrieval-and-generation chain with citation rendering.
Web UI or API endpoint depending on your scope. Auth + rate limits. Deployment to your infrastructure (or ours for Entry tier).
The test question set runs against the bot. Retrieval thresholds and re-rank logic tuned against actual failure modes — not hypothetical ones.
You hit it with real questions. One revision pass. Maintenance docs, evaluation report, support window. Re-index when you add documents.
An LLM is only as good as what you feed it. Most "bad bot" complaints trace to retrieval, not generation — the model couldn't see the right chunk, so it improvised.
We evaluate retrieval against your real questions before tuning generation. Recall, rank, citation accuracy — measured per question, reported per build. The bot you ship is the bot we measured.
Pick by document count, evaluation rigor, and hosting model. Up-scope at any time before kickoff.
A 4-person product team using Claude for code review, ticket triage, and project planning — but every Claude interaction required manually copy-pasting context from Notion (specs), GitHub Issues (tickets), and an internal wiki. Three context-switch roundtrips per task, every task.
We built a custom MCP server exposing all three internal systems as native Claude tools: notion_read, github_issues, wiki_search with semantic top-3 retrieval.
Same architectural primitive as a RAG chatbot — retrieval-grounded responses, citation-preserving, deployed to a team's actual workflow. Built on the same retrieval stack we'd ship for any customer-facing bot.
Submit the form. Scoping call within 24 hours. We'll review the corpus type, target users, and test questions before scoping the tier.