A production-grade retrieval-augmented system on your SOPs, training materials, policies, contracts, and tribal knowledge. Hybrid retrieval, citation generation, eval suite, continuous improvement loop. Built in 8–12 weeks for established operations where institutional knowledge is the bottleneck.
If new hires take six months to ramp because nobody can find the answer to "how do we handle X?" — if your senior tech has the company on their shoulders and you're nervous about them taking PTO — if you have 200 SOPs in a Google Drive that nobody reads — this is the build.
Decades of accumulated knowledge in owners' heads, in long-tenured employees, in 1,000-page operations manuals from 2008. Knowledge that's invaluable and almost completely unsearchable.
Med spas, salons, fitness, restaurant groups, dental groups. Where the same questions get answered differently at different locations because nobody has a single source of truth.
Healthcare, behavioral health, dental groups, financial advisors, accounting firms. Where the cost of an inconsistent answer isn't just inefficiency — it's compliance risk.
Tribal knowledge is the most valuable asset most operations have — and the most fragile. Three failure modes show up over and over again.
Every new hire pings the same senior people for the same answers. Senior people stop being productive. New hires take forever to feel competent. The cost is hard to measure but enormous.
Same question, different answer depending on who you ask. Sometimes that's a feature (nuance!). Usually it's a bug — and at the customer-facing layer it shows up as a compliance issue or a customer-trust problem.
Senior tech retires. Office manager leaves. Founder takes a vacation. The operation degrades immediately, sometimes catastrophically. You can't sell the business at full multiple because too much sits in heads that aren't on the cap table.
"A chatbot on your documents" is what most people sell. Most of those break the second your data gets remotely interesting — wrong chunking strategy, brittle retrieval, hallucinated citations, no evals, no observability. This is the build that doesn't.
SOPs (Word, PDF, Google Docs), training videos (Whisper transcription), past communications, policies, contracts, training quizzes, vendor manuals. Each source has its own ingestion strategy — not a one-size-fits-all "throw it at an embedding model."
Semantic search (vector embeddings) + keyword search (BM25) + recency weighting + metadata filters. Hybrid retrieval consistently outperforms pure-semantic for operational knowledge — your SOPs use specific terminology that matters exactly.
Every answer includes the source documents and exact passages it drew from. Click through to verify. The agent is configured to refuse rather than invent — if the corpus doesn't have the answer, it says so.
Golden-question set built during scoping with your domain experts. Regression evals on every retrieval and generation change. We catch quality drift before your team does — and before your team starts ignoring the system because it stopped being useful.
Every interaction is logged with feedback signals. Bad answers trigger review. New SOPs get ingested on a schedule. The system gets better as your knowledge base does — not staler.
Meets your team where they already are. Slack and Teams native integrations. Web interface for outside-the-firewall use cases. Mobile-responsive. SSO from day one.
Not everyone should see everything. Document-level and role-level permissions. Audit logging on every query and every retrieved document. The compliance layer that production knowledge systems need.
Every query, every retrieval, every model call instrumented. Per-user, per-team, per-feature cost attribution. Latency and quality dashboards. Multi-LLM provider routing with fallback.
A lot of operations have tried "a chatbot on our docs" and been disappointed. The reasons are predictable, and they're all the work that templated solutions skip.
Splitting documents naively destroys context. Splitting them well requires understanding your specific corpus — section-aware for SOPs, time-aware for transcripts, table-aware for policy documents. We do this work during scoping.
The agent is configured to refuse out-of-corpus questions, not invent answers. Citations are verified against the source — not generated by the model. The eval suite catches drift in this behavior before it reaches users.
You can't fix retrieval quality you can't see. Every query is logged with retrieved documents, scores, generated answer, and (where available) user feedback. The system is debuggable when it gets something wrong — which is when debuggability matters most.
We map your knowledge sources — what's documented, what's tribal, where the documentation lives, and what the highest-value queries would be. By end of call we know whether a Knowledge Agent fits and what the scope looks like.
Stakeholder interviews, corpus audit (sources, quality, completeness), golden-question set built with your domain experts, retrieval architecture sketch, fixed-quote SOW. The golden-question set is the foundation of the eval suite — it gets reused throughout the build.
Weeks 1–3: ingestion pipeline + chunking strategy + initial retrieval. Weeks 4–6: generation layer + citation + refusal behavior. Weeks 7–8: Slack/Teams/web interface, permissions, SSO. Weeks 9–10: eval suite, observability, cost monitoring. Weeks 11–12 (if needed): hardening, edge cases, pre-launch testing with your team.
Daily check-ins for the first week. Monthly knowledge-base updates as part of the retainer. Quarterly business review and eval re-runs. Maintenance retainer covers hosting, monitoring, eval pipeline maintenance, ingestion of new corpus material, and LLM API baseline.
ChatGPT with file upload works for ad-hoc Q&A on a few documents. It's not a production system: no permissions, no audit log, no eval suite, no hallucination control, no cost attribution, no continuous ingestion. Knowledge Agent is the production version of that idea, scoped to operations where the answers actually matter.
The agent is configured to refuse rather than invent. Every answer cites its sources. If a wrong answer slips through anyway, the feedback loop captures it, the eval suite gets updated, and the underlying issue (chunking, retrieval, generation) gets fixed. Wrong answers should be rare — and when they happen, they should be debuggable.
BAAs in place with all vendors that touch PHI. Document-level and role-level permissions. Audit logging on every query and retrieval. Encryption at rest and in transit. Multi-tenant isolation if you have separate practices/locations. The compliance posture gets documented during scoping for healthcare and financial-services builds.
Two paths. Lightweight: a content interface where your team can add/update documents and they flow into the index on a daily refresh. Heavier: integration with your document management system (SharePoint, Google Drive, Confluence, etc.) for automatic ingestion. Both are scoped during paid scoping based on what you have.
Both, but internal first is almost always the right call. Customer-facing deployments need a much higher accuracy bar (and tighter refusal behavior) than internal. We scope internal first, build evals on real production usage, and only expose customer-facing if the eval data justifies it.
Hosting (pass-through), monitoring, eval suite maintenance, ongoing ingestion of new corpus material, retrieval quality monitoring, bug fixes, minor enhancements, quarterly review, LLM API costs up to a baseline. New features (new interface, new permission model) are change orders.
Owners record themselves doing tasks once; the agent transcribes, structures, and produces a step-by-step SOP. Pairs strongly with Knowledge Agent — the output of #12 becomes corpus for #10.
Discuss →Generate training modules with quizzes from the same corpus. Track completion, assign by role/location, refresh automatically. Pairs naturally with Knowledge Agent for total "your business as software" coverage.
Discuss →Once your knowledge layer is structured, generating documents from templates becomes the next obvious step — proposals, contracts, engagement letters drawing on the same source of truth.
See full details →A 30-minute discovery call confirms whether Knowledge Agent fits, what your corpus looks like, and what the next step is. Custom RAG done right is the technical moat — same engineer, same engineering rigor, paid scoping → fixed quote.