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RAG that returns the right answer, not a confidently-wrong one.
An AI assistant that answers questions using your own documents — the approach people call RAG (retrieval-augmented generation). For mid-sized teams, we build the pipelines that pull clean text out of your documents, the part that lets the AI search them by meaning, the retrieval design, and a test set that checks whether the answers are actually right — so the assistant is something your team trusts, not something legal has to police.
What this means for you
If your team loses hours hunting through documents for an answer — or, worse, acts on a wrong one — this is about giving them an assistant that answers from your own files and shows its sources, so the answers are ones you can actually trust.
RAG over a SharePoint dump is not RAG.
It's a search box with a chatbot taped on, and it will confidently make things up. A real assistant is a whole system, not just a place to store your documents so the AI can search them by meaning.
We build it the way mid-sized teams actually need: clean text pulled out of your documents; sensible splitting of those documents into pieces, each tagged with labels that mean something (who owns it, when it took effect); version control so out-of-date documents are clearly marked as out of date; the search-by-meaning layer kept up to date as documents change; retrieval tuned to the kinds of questions people actually ask; and a test set your team can run on every change to confirm the answers are still right. The AI model you call at the very end is almost an afterthought.
The retrieval layer is the model.
If retrieval is wrong, no amount of prompting fixes it. These are the requirements that turn a RAG demo into a RAG system.
A real document collection, not a folder dump
Clean text pulled out of PDFs, Word files, web pages, scans, and tables. Labels on every document — who owns it, when it took effect, which area it applies to, what type it is. Out-of-date versions clearly marked as out of date. The collection of documents is the real product.
Splitting documents the right way
How you split documents into pieces depends on what they are: by page for reports, by clause for contracts, table-by-table for financials. We tune how big each piece is and how they overlap against your own test set — not against whatever the default settings happen to be.
Search-by-meaning storage that stays current
Pinecone, pgvector, Snowflake Cortex Search, Databricks Vector Search, or OpenSearch — the tool matters less than the discipline. The search-by-meaning index refreshes whenever a document changes, and you get an alert if it goes stale. No six-month-old data quietly serving answers in production.
Search by meaning and by keyword, together
Searching only by meaning misses exact matches (policy numbers, product codes, names). Searching only by keyword misses anything worded differently. We blend both — plus filters on the document labels and a re-ranking step that puts the best results first — and tune the mix against your own test set.
Permissions checked before the AI answers
Who's allowed to see what is checked before the AI ever pulls a document, not after it answers. The AI never sees anything the person asking isn't allowed to see. Every question is logged, and sensitive details are hidden automatically.
A test set your team owns
A hand-picked set of known questions and right answers, re-run on every change to catch anything that slips, plus scoring for whether each answer is actually backed by a real source. A dashboard shows you when the assistant is getting less accurate over time — before the CFO asks why it just made up a number.
Where RAG actually pays off.
We're skeptical-by-default. These are the use cases where the math works.
Policy & procedure assistants
Internal help desks for HR, IT, and finance. Handling 30–60% of routine questions on its own is realistic when the documents are well-kept and the test set is candid about what counts as 'right'.
Regulatory research assistants
Banking, insurance, healthcare. The assistant finds the relevant clause and shows you exactly where it came from; a person still makes the call. Cuts research time without taking the human out of the loop.
Contract and obligation Q&A
Search across all your contracts — master agreements, statements of work, vendor agreements — with answers tied to the right paragraph of the right version. Replaces 'email the legal team' for the 80% of routine questions.
Customer-facing knowledge assistants
Self-service support, product questions, onboarding help. The bar is higher here — wrong answers cost more — so we lean harder on safety checks, a thorough test set, and handing off to a person whenever the AI isn't sure.
Signals that RAG isn't the next move.
If two or more of these are true, the right work is corpus and foundation work first.
The documents have no owner
Nobody can tell you which version of a policy is current. Fix that — and the labels around it — before pointing an AI at it.
Documents are stored, not searchable
Image-only PDFs, scanned faxes, password-protected files, table-heavy financials with no readable text. Getting clean text out of them is the real project; adding AI on top doesn't change that.
No way to measure what counts as 'right'
Without a known set of questions and right answers, you can't measure whether the AI is accurate or catch it when it slips. Building that test set is a core part of the work.
Permissions are an afterthought
If 'who's allowed to see this document' isn't already settled in your file systems or data, the AI will leak information across those boundaries. We'll need to fix that first.
Scoped and quoted, with a real exit.
Same five reversible phases as every other engagement. Old systems stay live until the new one reaches parity.
See the full engagement modelReadiness assessment
Quoted by scopeWe look at your documents, your permissions setup, any search or AI work you've already done, and what you're trying to do. You leave with a clear go / fix-this-first / wait recommendation and an estimate for any groundwork.
Document & retrieval foundation
Quoted by scopeThe document pipeline, the labeling system, how documents get split, the search-by-meaning layer, retrieval design, and permission enforcement. The test set is built alongside it, not after.
Build & rollout
Quoted by scopeDesigning the prompts and the flow, choosing the AI model (a leading paid service or one you host yourself), safety checks, handing off to a person where it matters, monitoring, and a staged rollout you can reverse.
The questions we get most often.
Pinecone, pgvector, Cortex Search, Databricks Vector Search — which?
Depends on where your data already lives and how your security and permissions are set up. If your documents and access rules are in Snowflake, Cortex Search keeps everything inside the platform. If you're on Databricks, its built-in Vector Search has the same advantage. pgvector is the right answer when the job is small and your team already runs Postgres. Pinecone is the right answer when search scale and hands-off operations matter more than staying in one platform. The readiness check makes the call.
Do we have to fine-tune?
Almost never for this. ('Fine-tuning' means further training an AI model on your own data.) The wins here come from better document splitting, better retrieval, better prompts, and a better test set — not fine-tuning. Fine-tuning helps mainly with specialized language (medical, legal, niche industry terms) and with getting reliable, consistent output at high volume.
How do you keep answers up to date?
When a document changes, the affected pieces are re-indexed so the AI searches the latest version. Documents that don't send a change signal get refreshed on a schedule, and you get an alert if anything goes stale. We're explicit about what 'fresh enough' means for you — a policy assistant can tolerate a 24-hour lag; a pricing assistant probably can't.
What about the AI making things up?
Every answer is scored for whether it's actually backed by a real source, the assistant says 'I don't know' when it isn't confident, and low-confidence answers go to a person to review. We don't promise the AI will never confidently make something up — anyone who promises that is selling you something — but we do promise a test set that tells you when it's happening more often.
How this shows up in the work we ship.
Case studies and writing from engagements where this thinking actually meets production.
Adjacent topics in AI enablement.
Want to know if this is the right next move for your team?
Take the free AI & Data Readiness Assessment, or schedule 30 minutes to talk through your situation. Either way, you'll leave with a clearer picture of where you are and what the next steps should look like.