AI how-to guides Knowledge and documents
How to build a company knowledge base with AI
Connect the repositories where knowledge already lives, index them with their permissions and let an AI assistant answer questions by retrieving the relevant passages and citing them. People own the content and fix what the assistant shows is missing or out of date. Start with one team and the sources it uses every day, then add more as trust in the citations grows.
What AI does
- Ingests documents, wikis and records from several repositories and keeps the index up to date.
- Finds the relevant passages for each question by meaning, not just by keywords.
- Writes a short answer grounded in those passages and links every claim to its source.
- Applies each user's permissions at query time, so nobody sees what they could not open.
- Flags questions with weak or no answers, showing where documentation needs work.
What stays with people
- Owning the content: deciding what is authoritative, what is retired and who maintains it.
- Deciding which sources go in, who may ask about what and which topics stay out.
- Reviewing the gaps and wrong answers the system surfaces and fixing the documents behind them.
How to put it in place
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Pick a team and the questions it asks
Choose a team that loses time looking for answers, such as support, sales or engineering. Collect the questions it actually asks and note where the answers live today and who knows them.
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Audit the sources before indexing anything
List the repositories, their owners and their permission models. Remove duplicates, mark outdated content and agree a rule for what happens when two documents disagree. Indexing a mess only makes the mess faster to find.
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Connect sources with their permissions
Use the official connectors or APIs of each repository and carry the access control lists into the index. Enforce them when the question is asked, not only when content is loaded, so permission changes take effect.
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Ground answers and require citations
Configure the assistant to answer only from retrieved passages, cite them and say so when it does not know. Test it with the real questions from the first step and compare answers with what experts would say.
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Pilot with the team and measure
Open it to the chosen team, log questions and answers and track usefulness, wrong answers and questions without an answer. Use those gaps to fix documentation, not only prompts.
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Add sources and channels as trust grows
Widen to more repositories, languages and the tools people already use, such as Teams, Slack or the service desk. Keep a named owner per source and review the logs regularly.
What you need
- Access to the repositories (SharePoint, Confluence, Drive, databases) through official connectors or APIs.
- A clear permission model that the index can read and enforce.
- Content owners per source who keep documents current.
- A set of real questions with known good answers to test against.
- Logging of queries and answers, with a retention policy agreed with data protection.
When it is not worth it
- Small teams with few documents where asking a colleague is faster.
- When most knowledge is not written down anywhere: capture it first.
- When nobody will own the content: the assistant will repeat whatever is out of date.
Risks and how to control them
- Leaking information across permission boundaries: enforce access at query time and test with users of different roles.
- Confident but wrong answers: answer only from retrieved passages, show citations and let users report errors.
- Stale or contradictory documents: set owners, review dates and rules for which source wins.
- Instructions hidden inside documents (prompt injection): treat content as data and keep the assistant read-only.
- Personal data in the corpus: classify sensitive content, minimise what is indexed and log who asked what.
How Thinkia does it
We audit the corpus first: repositories, permissions and stale content are mapped before anything is indexed. Retrieval, citations and personal data handling are then tuned, and red teams probe for leakage and drift before a function pilot in support, sales or engineering.
Users only see what they are authorised to see, enforced at query time; every answer links to its source, low-confidence queries surface documentation gaps, and a full log records who asked what and what the system returned. Expansion to more sources and channels follows trust in the citations.
The Thinkia solution behind this guide
- Enterprise Knowledge AIYour organisation knows more than it can find.
- AI document intelligenceTurn unstructured documents into structured decisions.
Thinkia products involved
Frequently asked questions
What is an AI knowledge base?
A layer on top of your existing documents and systems that lets people ask questions in plain language and get answers built from the relevant passages, with links to the sources. The documents stay where they are; the AI makes them findable and usable.
Do I need to move all my documents to a new tool?
No. The usual approach is to connect the repositories you already use through their connectors and index them with their permissions. Content keeps being maintained where it lives today.
Is this different from enterprise search?
Search returns a list of documents for you to read. An AI knowledge base retrieves the relevant passages and writes an answer with citations. Both depend on good content and permissions, and many setups combine them.
How do you stop it from showing confidential information?
By carrying each source's permissions into the index and checking them every time someone asks, so the assistant only retrieves what that person could open anyway. Test it with users of different roles before going live.
What happens when the documents are wrong or outdated?
The assistant will repeat them, which is why content ownership matters. A good setup flags low-confidence and unanswered questions so owners can see what to fix.
Keep exploring
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