Short answer
A company AI assistant is a knowledge system that searches your company documents, gives answers together with their sources and does not show content the user is not authorised to see. When there is not enough information, it should say so instead of guessing; this behaviour is tested with an evaluation question set.
Suitable use cases
A knowledge assistant may help teams find procedures, product documentation and project information. Content ownership and permissions must be clear before indexing.
Ground answers in sources
Relevant documents are retrieved to support the response. Users should see source links. When information is missing or contradictory, the system should not invent a confident answer.
Keep access and freshness
Search must filter sources by user permission. Updates, deletions and index refresh are designed together so restricted or outdated content is not silently reused.
Pilot evaluation
Deliverables may include a source map, working query flow, evaluation questions and operating plan. Test answer quality, source relevance, abstention and unauthorised-access scenarios separately.
When an assistant is not suitable
If documents are out of date, who may see what is not defined, or questions can be answered with a single structured database query, the information structure or reporting should be improved before building an assistant. A general chat tool also does not meet the need for company-specific accuracy.
How we work
Data sources, update frequency and access rights are mapped. A retrieval and answer flow is built for the selected sources and an evaluation question set is prepared. Tests cover accuracy, source fit, declining to answer and permission violations; then the assistant goes live with a pilot user group.
Frequently asked questions
Questions people ask when considering an AI assistant that works with company data.
Which document types can it work with?
It can work with text-bearing sources such as PDF, Word, spreadsheets and wikis. For scanned documents we first measure text-extraction quality and state in the pilot scope if it is insufficient.
Can the assistant give wrong information?
Language models can make mistakes. Our design goal is that the assistant cites its sources and says so when it lacks enough information, and we test this with an evaluation question set. We do not promise zero errors.
Can everyone access every document?
No. Access rights are applied at the retrieval stage; content a user is not allowed to see never enters the answer as context.
Is the data kept in the cloud or on premises?
Both are considered. The decision depends on data sensitivity, legal requirements and budget, and is confirmed in writing at the quotation stage.
