AI knowledge assistants
Answers from your own documents, with sources.
AI knowledge assistant development is for companies whose answers are buried in manuals, procedures, wikis and shared folders. Staff ask in plain words and get an answer from your own documents, with a link to the passage behind it. We build it with retrieval-augmented generation (RAG), respect who may see what and check answer quality regularly.
What AI knowledge assistant development includes
Sorting the documents
We review document types, duplicates and versions, pick the official sources and agree who keeps each collection up to date.
Search and sources
We set up document loading and search around real staff questions. Every answer links to the passage it is based on.
Access rights
The assistant follows your permissions: people see answers only from documents they are allowed to open. We connect it to your document storage and the tool staff already use.
Answer checks
We prepare a set of questions with the expected sources and check answers for accuracy, missing sources and the effect of document updates.
What your team gets
- A list of sources and access rules for the knowledge base.
- A working assistant that answers with links to the source documents.
- A set of test questions and a simple process for updating and checking documents.
How a RAG chatbot for company documents finds its answers
The assistant does not memorise your documents. For each question it searches your approved collections for the most relevant passages, asks a language model to answer only from them and shows the links.
So answer quality depends mostly on the documents and the search, not the model: outdated files give outdated answers. Updating knowledge means updating the document. And because every answer cites a source, staff can check it in seconds.
Preparing an internal knowledge base AI can rely on
Most of the quality is decided before the first question. Together with your team we:
- choose the official source for each topic and leave drafts and old versions out;
- name an owner for each collection who confirms changes;
- check access rights in your storage, because the assistant follows them;
- collect the real questions staff ask, to tune search to their wording.
What this can look like
A salesperson needs the current terms of a product package. The assistant searches the approved documents, quotes the relevant passages and says clearly when the detail is missing.
Who a knowledge assistant is for
A good fit
- Companies where answers live in manuals, procedures, wikis and shared folders, and staff spend time looking for them.
- Teams that onboard new people often, so the same questions come up again and again.
- Businesses with documents that have an owner and are kept reasonably up to date.
Not the right fit
- Knowledge that exists only in people’s heads. First it has to be written down; we can help plan that, but an assistant cannot replace it.
- Answers to customers in their own channels: that is a separate service, AI chatbot development.
What affects the cost of an AI knowledge assistant
We quote cost and timing in EUR after looking at your documents and access rules.
Documents
A tidy wiki is quicker to connect than years of shared folders with duplicates and scans.
Access rules
One shared collection is simple; department or document-level permissions need careful testing.
Where staff use it
A web page is simplest; Teams, Slack, Telegram or an intranet adds integration work.
Quality checks
The size of the test question set and how often answers are reviewed.
We’ll quote cost and timing in EUR after a short brief.Discuss a knowledge assistant
Knowledge assistant, ordinary search or a better wiki?
If staff know which document they need and cannot find it, better structure and search may be enough, and cheaper. An assistant earns its place when answers are spread across several documents, when people do not know the right terms, or when new staff need explanations rather than file names. If a simpler fix will do, we say so at the first review.
How a knowledge assistant reaches its first team
Each stage ends with something you can check before we continue.
Brief and examples
We collect the main document collections, typical staff questions and the access rules, and choose the first team to use the assistant.
Test on real cases
We load a test set of documents and check the answers against the expected sources, without opening the assistant to staff yet.
Pilot in your tools
One team uses the assistant in daily work and marks wrong or incomplete answers.
Review and next step
We fix the sources and settings, agree who updates the documents and decide which collections to add next.
What we need from you
Access to the document collections (or copies), a list of questions staff ask often, the current access rules and one person from each team who can confirm whether an answer is correct.
We also need someone who owns the company documents and can say which version is current. The assistant’s answers are checked against their judgement.
An assistant is only as accurate as the documents behind it, so we don’t promise an answer to every question. Each answer shows its source so staff can check it, and confidential data goes only to services your company has approved.
AI knowledge assistants: common questions
How do you measure the quality of answers?
We prepare real questions with the expected source and check that the assistant finds the right passage, answers correctly and admits when information is missing. The same set is rerun after document or setting changes.
Does RAG require training a new model?
Usually not. The assistant finds the right passages in your documents and passes them to an existing model. We consider extra training only if search quality is not enough.
What happens when documents disagree?
We agree which source wins and who owns each version. The assistant shows the conflict instead of choosing silently, and the document goes to review.
Is our data used to train AI models?
We choose providers and settings that keep your data out of model training where the provider allows it, and we agree in advance what data the system may see.
What if our documents are out of date?
The assistant answers only from the sources it is given, so we agree who owns each source and how it is updated. Every answer shows its source, so outdated content is easy to spot.
Where are our documents stored?
In storage your company approves. We agree in advance which documents are loaded, where the search index lives and who has access to it.
Read before you start
Short practical guides on the same subject.
- Since 2006
- Written scope before work starts
- One contact person
- Reply within 24 hours
Which questions does your team keep asking?
Tell us where your documents live and who needs the answers. We’ll reply with questions and a suggested first step.
The first conversation is free, with no obligation.