Put your institutional knowledge to work.
Your company already knows the answer. It's just buried in a wiki page from 2022, a closed support ticket, a contract PDF, or the head of someone who hasn't looked at it in months. I build RAG systems over that knowledge — documents, wikis, tickets, contracts — and turn it into a queryable, source-cited system your team can actually trust. Without leaking it to people who shouldn't see it.
Most companies I meet don't have a shortage of knowledge — they have a shortage of access to it. The answer to a customer's question sits in a ticket from eighteen months ago. The right contract clause is buried in a PDF nobody indexed. The process someone figured out the hard way lives in their head, not in a doc. That's not a knowledge problem, it's a retrieval problem.
I build systems that fix the retrieval problem: document intelligence that actually understands your files, hybrid search that finds the right passage instead of the closest keyword, and answers that come back with the source attached. If an answer can't point to the document it came from, it doesn't ship.
What a knowledge system actually needs
Three things separate a system people trust from one they quietly stop using after the second wrong answer.
- Document intelligence — parsing, chunking, and structure-aware ingestion across PDFs, wikis, tickets, contracts, and spreadsheets, so the tables and hierarchy your data depends on survive the process instead of getting flattened into noise.
- Permissioned access — retrieval that respects the access controls you already have, so a system built to save time doesn't become the fastest way to leak something it shouldn't.
- Freshness and source citations — every answer traces back to the exact document and section it came from, and the index stays in sync as source material changes, so stale answers get flagged instead of served with confidence.
Where this earns its keep
The clearest wins are places where people currently ask a colleague instead of searching: support teams answering the same question a hundred different ways, new hires re-learning what the last person already figured out, compliance teams hunting through contracts for a clause. I look for the process where faster, cited answers move a real number — time-to-answer, ticket deflection, onboarding time — and I start there.
The knowledge is already inside your company. Most of it just isn't findable. That's the gap I close.
The four pillars of a knowledge system.
Eight weeks, corpus to live.
Map where the knowledge actually lives
We inventory the docs, wikis, tickets, and contracts scattered across your systems, and agree on the KPI — time-to-answer, ticket deflection, onboarding time — before a line of code is written.
Design ingestion, permissions, and retrieval
Chunking strategy, permission mapping, and hybrid retrieval are designed together — fixed scope and price so there are no surprises mid-build.
Ingest, index, and ship into production
We build in sprints with weekly demos, index the real corpus, and deploy into your environment — not a sandbox with a handful of sample documents.
Prove it against the KPI
We track the system against the number we set, tune retrieval quality and citation accuracy, and keep tuning until the value clears the cost.
Let's make your knowledge findable.
If your team burns hours hunting for answers that already exist somewhere in your docs, that's where we start. I'll tell you what it's worth before we build it.
