AI company knowledge base
An employee asks in their own words and gets an answer with a link to the document and the clause it came from.
- Buildfrom $1,700
- Timeline2–4 weeks
- Supportfrom $570/mo
Who this suits: Companies whose rules sit in three places, where one person knows which version is current and a new hire asks whoever is busy.
What is in the first version
- search across documents
- answer with source
- access by role
- unanswered questions
The figures above are the package this sits on: AI agent or process automation. Anything past its edges the calculator adds before the work starts, not after.
How the path goes
- 01
A question in plain words
The search runs on meaning, so the employee’s wording and the document’s need not share a word.
- 02
The answer arrives with its source
Every answer carries the document, the section and the date, and an answer without a source is not given.
- 03
A missing answer is said plainly
The question is recorded instead, and that list is the plan for what the knowledge base is missing.
What decides whether this works or annoys people
A superseded document answers confidently too
Superseded versions leave the index rather than being labelled, and every document carries an owner and a date.
A knowledge base leaks easily
Rights are checked at the moment of the search: what a person may not open never appears inside a quotation.
Some knowledge is not a document
The first version takes what is written down, and its unanswered questions decide which rules are worth writing.
What to measure once it is live
- questions that find an answer
- questions moved off colleagues
- clicks through to the source
- documents with no owner or date
Why this comes out faster
The shape of this one is known: the states, the edge cases and the things that usually go wrong have been decided before. Nothing here is a template, and the saving is not in your half of the work. It goes into your process, your content and the systems this has to talk to, which is the part nobody can have solved in advance.
A likely stack for this
Picked against the task when we scope it, not decided in advance. This is the shelf it usually comes off.
- Search and modelsPythonFastAPIpgvectorOpenAIAnthropic
- Documents and storagePostgreSQLRedisS3n8n
- Entry points and operationsTypeScriptNext.jsTelegram Bot APIDockerGrafana
Price it yourself, right here
Five steps, and you can see the number without leaving a contact. The estimate accounts for the kind of work, what you already have and what it will need inside.
These bills do not come from us
- AI model calls, indexing the archive included
- Recognition of scans and photographs
- The plans on your drives and wiki
- Hosting and storage for the index
Asked before the first call
- Do our documents end up inside the model?
- No. The texts stay with you, only the fragment needed for an answer goes to the model, and the refusal to train on it is in the contract.
- What if it answers wrongly?
- Every answer has a link to the clause, so a wrong answer traces back to a document. Where the documents hold no answer, the system says so.
- Who keeps the base current?
- You do, and that is what an owner per document is for. An edit on your own drive re-indexes itself, without us.