right for you if

your staff spend time hunting through manuals, policies or old tickets for answers
customers keep asking questions your documents already answer
a general chatbot is no use, because the answers live in your own files
you need to see where an answer came from before you trust it
some documents must stay visible only to certain people

what you get

Connections to where your documents live, such as SharePoint, Google Drive, Confluence, a helpdesk or a database, kept up to date as files change
A searchable index of your documents, split into passages so the right one is found
An assistant where your people already work: Microsoft Teams, Slack, your intranet, helpdesk or website
A source link with every answer, pointing to the document and section it came from
Access rules that follow the permissions you already have
A test set of real questions with checked answers, run before launch and after every change
A way for users to flag a wrong answer, and a report of what was flagged

Why the answer is so hard to find

The answer is usually written down somewhere. It’s in a policy PDF, a product manual, a shared drive or a ticket someone solved last spring. Finding it is the problem. New staff ask colleagues, experienced staff get interrupted, and customers wait for a reply that a document already holds.

A general chatbot doesn’t help, because it has never seen your files. It answers anyway, with confidence, and you can’t tell where the answer came from. For anything that touches contracts, prices, safety or regulated advice, that’s a risk you can’t take. What you need is an assistant that answers from your own documents, says where each answer came from and admits when it doesn’t know. That is what our RAG development services build.

What we build

RAG stands for retrieval-augmented generation. In plain words: when someone asks a question, the system first searches your documents for the passages that match it. A language model (the kind of AI behind ChatGPT) then writes an answer using only those passages and shows them as sources. The model’s skill with language makes the answer readable. Your documents decide what it says.

Staff assistants. Answers from your handbook, procedures, product specifications and past tickets, inside the tools your team uses, such as Microsoft Teams, Slack, your intranet or your helpdesk.
Customer assistants. Answers on your website or in your support inbox, drawn from help articles, terms and product information, with a hand-off to a person when a question goes beyond them.
Document search. Search that understands a question instead of matching keywords, across contracts, reports or case files, with the matching passages highlighted.
Extraction across many files. Dates, amounts, obligations or clauses pulled out of a large set of documents into a table your team can check.

We connect to what you already use: SharePoint, Google Drive, Confluence, Notion, Zendesk, a database or a plain folder of PDFs.

How we make the answers trustworthy

If you’re comparing RAG development companies, ask each one how it knows the answers are right. These are the decisions our work is built around.

Every answer shows its source. Before the model sees anything, the matching passages are sorted again so the most relevant come first. Each answer then links to the document and section it came from, so a reader can check it in one click. When the system finds nothing that supports an answer, it says so instead of filling the gap.

Accuracy is measured. Before launch we write a set of real questions with your team, each with the answer an expert would give. We run the assistant against that set, fix what fails, and run it again after every change to the documents, the settings or the model.

Permissions follow the documents. If someone can’t open a file today, the assistant won’t answer from it for them. Where possible the rules come from the systems you already use, so you manage access in one place.

The documents come first. Much of the work is in the sources, not the AI: choosing which ones count as the truth, removing outdated versions and splitting long files into passages that can be found. We plan that time into the scope from the start.

Senior engineers build each system and review every line before it ships.

What it costs

Every project starts with a free scoping call. We look at a sample of your documents, agree in writing on what we’ll deliver, and give you one fixed quote before any code is written. Projects start at $2,500, and the price only changes if the scope changes.

What makes a document assistant bigger or smaller:

how many sources it draws from, and whether they sit in one system or several
the state of the documents: clean text, or scans, tables and old versions that need sorting
access rules, if different people may see different documents
where it lives: one internal chat, or a public assistant on your website
how much a wrong answer would cost, which decides how much testing it needs

One assistant over a well-kept help center is a smaller project. A system across several departments’ files, with permissions and an audit trail (a record of who asked what and which sources were used), is a larger one. Running costs for the model and hosting are separate, and we estimate them with you during scoping. If you’d like the full picture first, read how we work.

Tell us which documents hold the answers your team keeps looking for.

answer.tssenior review


          1
          
          
          const matches = await search(question, { top: 20 })
        
          −
          First draft, removed: 
          
          const context = matches.slice(0, 5)
        
          2+
          
          After review, added: 
          const context = rerank(question, matches).slice(0, 5)
        
          3
          
          
          const answer = await model.answer(question, context)
        
          −
          First draft, removed: 
          
          return answer
        
          4+
          
          After review, added: 
          return { answer, sources: context.map((c) => c.url) }
        // review: best matches first + cite them

Every answer comes from your own documents, with the source attached.

// example code, written for this page

Questions about answers from your documents.

What happens when the documents don't hold the answer?

The assistant says it couldn't find one and points to who can help, instead of guessing. We set it to answer only from what it found in your documents, and we test that with questions your documents can't answer.

Our documents are a mess. Is that a problem?

It's normal. Old versions, scanned PDFs, duplicates and spreadsheets turn up in most document collections. Part of the work is deciding which sources count as the truth, then cleaning or leaving out the rest, and we plan that time into the scope.

Where is our data stored, and who sees it?

Your documents and the search index stay in your own cloud account or on hosting you approve. The language model only receives the passages needed for each question. Business accounts with the major model providers don't use that data for training by default, and for strict cases we can run a model in your own account.

How is this different from uploading files to ChatGPT?

Uploading files works for one person and a handful of documents. A RAG system keeps a whole library up to date as files change, respects who may see what, shows its sources the same way every time and can be tested and measured. It also lives where your team already works.

Answers from your own documents, with the source attached

Why the answer is so hard to find

What we build

How we make the answers trustworthy

What it costs

Questions about answers from your documents.

Tell us what you're building.

Thanks, we've got it.