Start with source quality, permissions and freshness.
AI knowledge base (this service)OXIAE / AI AGENTS & CHATBOTS
AI knowledge base.
Make company knowledge searchable with answers that lead back to the source.
Discuss your projectManage knowledge, chat with documents or build RAG?
Staff need to ask questions about a bounded document collection.
Chat with business documents →You need custom source selection, integration and evaluation.
RAG system development →UNDERSTAND THE SERVICE
What makes an AI knowledge base reliable?
An AI knowledge base starts with governed sources: who may access a document, which version applies and who keeps it current? I organise ingestion, findability and source references around those agreements. A chat interface can sit on top, but does not replace source management. Answers should show their evidence or clearly state when knowledge is missing.
When it is a good fit
Staff search across scattered documents and someone can own source quality and access permissions.
When to take a different approach
If sources are outdated or contradictory, begin with cleanup and ownership. A model cannot reliably invent missing company policies.
PRACTICAL DECISION GUIDE
An AI knowledge base depends on source ownership
An AI knowledge base needs more than document storage. We establish which source is authoritative, who reviews changes and how obsolete versions are removed. Search and answering receive the metadata they need, such as document type, department, publication date and validity period. [1]
We then design updates. In a Microsoft environment, Graph incremental synchronization may be part of the solution; other sources need their own mechanism. Deletions and changed permissions need the same care as new documents. Users should be able to open the cited source. [2][3]
Compare knowledge search, document chat and access requirements in the AI search and business knowledge overview. Use that comparison to decide which part of the information process needs work first.
Does the task require new research rather than only retrieving existing company documents? An AI research agent has a different brief: gather sources, preserve uncertainty and make the findings reviewable.
Questions to settle first
- Which source wins when two versions of an instruction coexist?
- How are deletions and permission changes propagated?
Make acceptance measurable
- Tests using new, changed, revoked and deleted source material.
- A question set checking document versions and retrievable references.
Compare your options
Choose based on the task, the people involved and what you can maintain.
| Approach | Useful when | What to consider |
|---|---|---|
| Searchable knowledge base | Users want to find documents and judge them directly. | Easier to inspect; requires clear documents and titles. |
| Document chat | Users ask questions about a bounded collection. | Answers must refer to relevant passages. |
| RAG system | Source selection must integrate with several systems. | More retrieval control, with more maintenance and evaluation. |
What I need to start
- A source inventory with owners, freshness and permissions.
- Example questions with passages that support the answers.
- A process for updating, withdrawing and deleting documents.
How to assess the result
- Users cannot obtain information from sources they may not access.
- Updated or deleted sources are synchronised as agreed.
- Unsupported questions do not produce invented company policies.
SCOPE & INVESTMENT
What determines the cost?
A useful estimate starts with the scope. I distinguish development work from recurring usage and operating costs. These are the main drivers:
- Number of source locations and differences in document quality.
- Complexity of permissions and synchronisation.
- Evaluation questions, freshness agreements and content ownership.
APPLICATION AND SCOPE
Your challenge.
A practical application.
Illustrative scenario; not a published client case.
Valuable information is spread across documents and internal platforms.
A knowledge base centres on source management, freshness and access. Chat with business documents is one way to use that information. A RAG system handles the retrieval of relevant passages, while ownership and content review remain responsibilities of the organisation.
THE QUESTION I ASK FIRST
Who manages the knowledge, and when does a document expire?
Agree the first step
Use the prerequisites, acceptance checks and cost factors above to define your assignment. Agree who can approve the outcome and who will maintain the solution. The project approach explains discovery, delivery and handover.
Read the project approachQUESTIONS ABOUT THIS SERVICE
Your questions.
Clear answers.
Is an AI knowledge base the same as a chatbot?
No. The knowledge base manages sources, permissions and freshness. A chatbot is one possible interface on top of that information.
Sources and technical basis
These primary sources explain the technical principles behind this guide. The project recommendations are OXIAE’s interpretation; they do not imply endorsement by the publishers. Product documentation applies to the named platform.
- Retrieval augmented generation in Azure AI SearchMicrosoft Learn
Explanation of searchable source content for generated answers.
Link and relevance checked: - Use delta query to track changes in Microsoft Graph dataMicrosoft Learn
Example of incremental updates in Microsoft Graph.
Link and relevance checked: - Security filter pattern in Azure AI SearchMicrosoft Learn
Example of permission-scoped filtering during search.
Link and relevance checked:
YOUR NEXT STEP
Prepare your project conversation.
Exploring AI knowledge base? Use the project checklist to describe your task, systems and intended outcome. Collect open questions and download a brief for your conversation.
Create your project brief