Answers pulled from your own contracts and history, not the open internet.
Retrieval-augmented generation: the system finds the actual passage in your documents and grounds the answer in it, with a pointer back to where it came from.
What this is
A general model answers from what it learned during training, which does not include your contracts, your internal wiki, or the decision your team made in a meeting three years ago. RAG, retrieval-augmented generation, closes that gap: a question triggers a search over your own documents first, the relevant passages get pulled, and the model answers from what was actually found, with a citation back to the source paragraph.
The hard part is retrieval, not generation. Documents get split into chunks before they are indexed, and a naive fixed-size split can cut a clause in half or separate a table's header from its rows. Once the chunking is wrong, feeding a good model good instructions does not fix it, the model will answer fluently and confidently from a chunk that is missing the part that mattered.
Company documents also do not sit still. A policy gets updated, a contract renews, an old decision gets superseded by a newer one. A pipeline that indexes once and never rechecks produces answers that are confidently out of date, which is worse than no answer, because it looks exactly as trustworthy as a current one. Keeping the index current as the source changes matters more than a marginally cleverer embedding model.
If the job is pulling specific fields out of a flow of new documents, invoices, applications, claims, rather than answering open questions across an existing archive, that is document and email processing, not this. If the job is a chat interface embedded into your product that takes actions rather than answering questions, that is AI assistants inside your product; this service is often the retrieval layer sitting behind one.
What you get
Ingestion pipeline
Documents pulled from wherever they actually live, a shared drive, Confluence, SharePoint, a document management system, and chunked in a way that respects the document's own structure.
Embedding and vector index
Chosen and tuned for your document types, since a long contract and a short internal wiki page do not retrieve well the same way.
Retrieval and re-ranking
So the passages that reach the model are the right ones, not just the ones with the highest raw similarity score.
Answers with citations
Every answer carries a pointer to the specific document and passage it came from, so a person can check it rather than just trust it.
Re-indexing pipeline
Keeps the index current as documents are added, edited or deleted, instead of reflecting a snapshot from launch day.
Access control matched to your permissions
A search tool that surfaces a document someone should not see is a bigger problem than one that misses a document.
Evaluation set
A fixed list of real questions with known correct answers, so a change to the pipeline is checked against a number, not a gut feeling.
When this fits, and when it does not
A good fit
- The answer to a question genuinely lives inside your own documents, contracts, policies, internal wikis, past project history, and not on the open internet.
- The archive is large enough, or growing fast enough, that nobody can hold it in their head, and "ask the person who has been here twelve years" is a bus-factor problem rather than a plan.
- Wrong answers are expensive enough that you need citations back to the source document, not just fluent-sounding prose.
- The documents already exist in digital, mostly-text form, PDFs, Word documents, wiki pages, not boxes of paper still waiting to be scanned.
Not a good fit
- The whole corpus is small enough to paste into a single prompt, a few hundred pages. A well-organised document and a model with a large context window does the job without a retrieval pipeline at all.
- What you actually need is to pull structured fields out of new documents landing every day, invoices, applications, claims. That is document and email processing, not search.
- You want the system to take actions on the answer, book something, update a record, send a reply, not just surface information. That is AI assistants inside your product; retrieval is usually one part of it.
- The documents are still on paper, unscanned, or scattered across systems nobody has inventoried yet. Start with an AI feasibility study to find out what is actually there before indexing it.
- You want this to replace having anyone on staff who understands the material. It answers questions from what is written down; it does not catch what never got written down.
How it runs
- 01
Inventory the real corpus
Where the documents actually live, who can see what, and a close read of a sample so we know what "correct" looks like.
- 02
Build ingestion and chunking
Against real documents, not a clean export, because the messy PDF with a scanned signature page is the one that breaks a generic splitter.
- 03
Wire up retrieval and generation
And build the evaluation set of real questions before touting a demo, so quality is a number rather than an impression.
- 04
Add access control and citations
Then run it against the evaluation set until answers are actually grounded, not just fluent.
- 05
Hand over with re-indexing running
So day four hundred's documents are searchable, not just day one's.
Questions we get
How is this different from just uploading files to a chat tool?
An uploaded file sits inside one conversation's context and disappears when it ends. This indexes your whole archive, keeps it current as documents change, and cites a source for every answer instead of relying on one chat session's memory.
What if the model does not find the right document?
It either says so, or answers from the wrong one, and either way you need to know which. That is what the evaluation set is for: retrieval quality gets measured against real questions, not assumed from a demo.
Can this stay on our own infrastructure?
Yes, when that is a requirement. We also build the on-premise hardware side of this when data cannot leave the building.
Does it work across languages?
Generally yes, but it should be tested on your actual documents and the languages your questions are asked in, since retrieval quality varies by language pair and is worth checking rather than assuming.
Have an archive nobody can search properly?
Describe what it is and roughly how much of it there is. An engineer reads it and gives you a straight answer on whether retrieval is the right tool for it.