SYSTEM · 3–5 WEEKS · FIXED FEE
RAG consulting for an AI knowledge base your team can trust.
I build a retrieval-augmented AI knowledge system over your existing documentation and internal knowledge — company wiki, SOPs, product specs, support history. Your team asks questions in plain language. The system answers from your actual content.
WHAT IT IS
Your company's knowledge. Suddenly searchable by anyone.
Most companies have more knowledge than they can use. SOPs that live in someone's head. Product specs buried in a folder no one remembers. Support answers that get rewritten from scratch every week because the good version is in a Slack thread from 2021.
A knowledge base AI layers a retrieval system over the documents you already have. Staff ask natural-language questions. The system finds the relevant sections, synthesizes an answer, and cites its sources. It doesn't hallucinate what it doesn't know — it says it doesn't know. Reliability is a design constraint, not an afterthought.
The output is a system your team can query through Slack, a web interface, or directly via API — whichever fits your workflow. I build it, connect it to your doc sources, train the indexing pipeline, and hand it off. You own and operate it from day one.
New to the category? Start with the AI workflow automation guide.
WHO IT'S FOR
Teams where knowledge is scattered but real.
Right fit if…
- Your team spends 30+ minutes a day hunting for internal information
- Onboarding new staff requires weeks of shadowing — the knowledge isn't written down
- Your support team keeps answering the same questions from scratch
- You have documentation but it's spread across Notion, Drive, Confluence, SharePoint
Probably not a fit if…
- You have almost no documentation yet — document first, then build the search layer
- Your knowledge is highly sensitive and can't be sent to any cloud service (discuss on discovery call — on-premise options exist)
- You need a broad multi-system knowledge graph — too large for a single Sprint
WHAT'S INCLUDED
Data ingestion to production interface.
Source connector setup
Ingestion pipelines for your existing document stores — Notion, Google Drive, Confluence, SharePoint, or local file sources.
Vector database & indexing pipeline
Chunking strategy, embedding model selection, and vector store (Pinecone, Weaviate, or pgvector depending on your infra preference).
RAG pipeline engineering
Retrieval-augmented generation pipeline with source citation, confidence handling, and "I don't know" behavior when coverage is insufficient.
Query interface
Slack bot, web UI, or API endpoint — whichever fits how your team already works. Not a new tool to learn, a layer on what you use.
Refresh scheduling
Automated re-indexing pipeline so the system stays current as your documentation evolves. No manual re-runs required.
Documentation & handoff
Full technical runbook, admin guide, and team training session. Your staff can add doc sources, retrain the index, and manage the system without me.
Source boundaries
Which documents get indexed, and for whom, is decided in Week 1 — before anything is embedded. If part of your documentation is restricted, that boundary is a scoping decision, not something discovered later when someone gets an answer they should not have had.
Evaluation before production
A question set drawn from your team's real queries, run against the system and scored on whether the answer is right and the citation supports it. You see that scoring before handoff, so "good enough to ship" is a number you agreed to rather than my opinion.
PRODUCTION READINESS
How the RAG system stays accurate, permission-aware, and observable.
A retrieval demo answers questions. A system you can put in front of staff has to answer the five below first, and each one is settled during the build rather than after it.
- Who can see what
- Which sources are indexed, and for whom, is decided in Week 1 — before anything is embedded. There are three ways to draw that line and the choice is made with you at scoping: index only what everyone on the system may already see; keep a separate index per group, so retrieval cannot cross a boundary it does not know exists; or carry the source system's own permissions through to query time, which is possible where the connector exposes them — SharePoint and Confluence do, a shared folder of PDFs does not. Whichever line is drawn, the last step before handoff is the same: queries are run as a restricted user against content that user should not reach, and the system has to come back with nothing.
- What it does when it does not know
- The retrieval pipeline is built to say so. Answers carry citations to the passages they came from, and where coverage is insufficient the system returns that rather than composing something plausible. I test this behaviour explicitly before handoff, because it is the failure mode that costs trust fastest and the one a demo never shows.
- How you know it is good enough
- A question set drawn from your team's real queries, scored on whether the answer is right and whether the citation supports it. You see that scoring before go-live, so the decision to ship is made against a number you agreed to rather than a demo that went well.
- What you can see
- The system is instrumented and you own the instruments. Per document: how many chunks it was split into and how many vectors each chunk became — which is how a file that failed to index properly shows up, as a count that does not match the document in front of you. Per answer: the confidence attached to each of the top twenty or thirty passages retrieved, and which answers went out on weak retrieval. That last list is the one worth reading first when somebody says it gave a bad answer, because it usually already contains the example they mean. And the index knows when a source document has changed underneath it, so chunks and vectors that no longer match their source are visible as stale rather than quietly wrong.
- What happens after launch
- Re-indexing runs on a schedule, so the system follows your documentation as it changes without anyone re-running anything. The runbook covers what to check, how to add a source, and how to re-score the question set when your content moves. Day-to-day operation sits with your team — this is a handoff, not a managed service.
PROCESS
Three to five weeks, depending on source complexity.
Week 1
Audit & scope lock
Inventory your doc sources, assess quality and coverage, agree on connectors and interface type.
Weeks 2–3
Ingestion & indexing
Build connectors, run initial ingestion, tune chunking and embedding strategy. Validate retrieval quality on real questions.
Week 4
Interface & integration
Build and wire the query interface. Set up refresh pipeline. Run end-to-end testing with your team's real questions.
Week 5
Handoff & training
Go live, train admins and daily users, transfer all assets and documentation.
OUTCOME
Questions answered in seconds, not hours.
In the deployment I can speak to, the material was ten training manuals, a video archive nobody had watched end to end, support tickets, and meeting transcripts. The answer to most questions already existed somewhere in that pile. Finding it was the problem.
Support and operations teams stop rewriting answers they've written before. The system finds the existing good answer, cites where it came from, and presents it. If the answer doesn't exist yet, the system says so instead of fabricating something plausible-sounding.
The one measured case is a knowledge base I built and ran inside an operating company. Answering a question there used to mean asking colleagues, searching several systems separately, and sometimes waiting for a meeting — about an hour of effort. Afterwards, answers came back in under five minutes, cited to the source document, across roughly 1,000 queries a month from about 100 internal users. What made that pace possible was twenty years of knowing those documents and that client taxonomy, not the tooling.
FAQ
Common questions.
What if our docs are a mess?
Common situation. I'll document the coverage gaps and quality issues in the Week 1 audit, and scope only what makes sense to index. Garbage in, garbage out — but I won't pretend otherwise.
Will it hallucinate?
Designed not to. RAG systems with source attribution and explicit "I don't know" behavior hallucinate far less than vanilla LLM chat. I test this explicitly before handoff.
What about data privacy?
I sign NDAs before any engagement. I scope the data handling approach in Week 1. For regulated industries or sensitive content, we can discuss on-premise or private cloud deployment.
Which LLM do you use?
Depends on your content, budget, and data sensitivity requirements. I'll recommend during scoping — usually OpenAI or Anthropic for cloud, Mistral or Llama for on-premise.
How do we keep it updated?
I build a scheduled re-indexing pipeline as part of delivery. You update the source documents as you normally would — the system picks up changes automatically.
What's the cost?
Flat fee, scoped after the discovery call based on number of source connectors and complexity of the interface. Plus ongoing LLM API costs (billed directly to you, typically modest).
How do you know it's accurate enough to ship?
A question set drawn from your team's real queries is run against the system and scored on two things: whether the answer is right, and whether the citation actually supports it. You see that scoring before handoff, so the decision to go live is made against a number you agreed to rather than a demo that went well.
How do you know if it stops answering well?
By looking, with instruments that come with the system. Every answer records the confidence of the top twenty or thirty passages it retrieved, so the answers that went out on weak retrieval are a list you can read rather than a complaint you wait for. Every document records how many chunks it became and how many vectors each chunk became, so a file that did not index properly is a number that does not add up rather than a gap nobody notices. And when a source document changes, the chunks and vectors built from it are marked stale instead of continuing to answer from the old version.
Can it respect who is allowed to see what?
That boundary is set in Week 1, before anything is indexed. Which sources go in — and for whom — is a scoping decision made with you. Restricted material stays out of the index unless the design accounts for it explicitly, rather than being discovered later when someone gets an answer they should not have had.