Before building a company AI assistant, define who will use it, which information it may access, what private means for your organization, how answers will be tested, and who will keep the knowledge current. Start with an approved knowledge area and measurable use cases.
1. Name the questions it should answer
Choose a narrow job, such as helping service staff find current operating procedures. Gather realistic questions and define what a useful answer includes. “Answer anything about the business” creates a scope that is difficult to validate.
2. Identify authoritative sources
List the documents, repositories, and records that may be used. Assign an owner to each source, remove obsolete versions, and define what happens when documents conflict. Retrieval over inconsistent knowledge can produce inconsistent answers.
3. Specify permissions before ingestion
A user should not gain access to restricted information simply because it entered an AI index. Access enforcement belongs in the retrieval and application architecture, rather than relying on instructions to the model.
Microsoft’s Azure AI Search documentation describes document-level filtering and authorization approaches for retrieval. The available mechanisms depend on the selected data sources and implementation, and some provider features may be in preview. Confirm the exact permission behavior before relying on it.
4. Define the privacy boundary
Ask where documents, indexes, prompts, responses, and logs will be processed and stored. Review provider terms, data retention, model-training treatment, network requirements, and administrator access. A private deployment requirement needs more detail than a login screen.
5. Test missing information and failure modes
A test set should include valid questions, outdated sources, ambiguous wording, conflicting documents, and requests for unauthorized information. Check whether the answer is supported by the cited source and whether the assistant declines or escalates when evidence is insufficient.
A correct answer to a few familiar questions is not enough to establish production readiness. Record the kinds of mistakes that would matter to the team and assign acceptance criteria accordingly.
6. Separate answers from actions
An assistant that finds a policy has a different risk profile from one that changes a customer record or approves a purchase. Give actions their own permission checks, review steps, and traceability. Start with retrieval and drafting before adding broader authority.
7. Assign ongoing ownership
Someone needs to maintain source documents, handle feedback, review access changes, and monitor use. Define how quickly revised documents become available and how the team will know when a source is missing. Treat the assistant as an operating system component with a lifecycle.
Common questions
Can a knowledge assistant guarantee correct answers?
No. Source grounding, evaluation, and access controls support reliability, but the assistant still needs an explicit uncertainty and escalation process.
Should we upload all company documents first?
Begin with an approved and maintained collection. Broad ingestion before permission and quality checks can create avoidable problems.
Sources & further reading
Microsoft Learn: Document-level access control in Azure AI Search ↗
Technical reference for document authorization approaches; confirm feature availability for the selected architecture.