01
Knowledge in silos
SharePoint, network drive, wiki, email. The same rule sits in three places, the current one often in only one.
AI knowledge base
AI knowledge base for companies
People ask in natural language and get answers from SharePoint, files or selected emails. The AI uses only approved company sources and shows the link to the original file. Internal knowledge becomes searchable without putting content into public chatbots.
Orientation
What is an AI knowledge base?
An AI knowledge base connects existing company knowledge with a natural question interface. People do not need to know which SharePoint folder holds a piece of information. They ask the question and get an answer based on the sources you connected.
Unlike a general chatbot, it works with defined data sources and access rights. SharePoint documents, files or selected internal systems. Instead of building a second wiki, existing knowledge becomes usable.
The problem
The knowledge is there. It is just hard to find.
Policies sit in SharePoint, old contracts on the drive, agreements in mailboxes. Anyone new or outside the specialty searches. Anyone searching interrupts others. ChatGPT with confidential company data is not an option in many organisations.
02
Public AI is off limits
Contracts, customer data and internal prices do not belong in a public model. That is why search often stays manual.
03
Search eats working time
Onboarding drags, support repeats the same questions, specialists become a human search engine.
The solution
AI knowledge base with RAG
I connect the sources you already use: Microsoft Graph for SharePoint, file stores, selected mailboxes or internal APIs. A retrieval step finds the matching passages. The language model writes the answer based on that content.
The model runs in an EU cloud, for example Azure, or locally if data must not leave the building. The architecture stays model-agnostic. OpenAI, local LLMs or a later model can be swapped. The connection to your systems stays.
What happens technically
- Index sources: SharePoint via Microsoft Graph, files, email if needed
- Search via embeddings and RAG, plus classic search where it helps
- Answer based on found sources, with a link to the original file
- Permissions feed into search: content a user cannot access is not used as a source
- Operations with Docker, APIs and documentation your team can continue
Your company data is not used to train a public model. The application is built so answers are based on the connected sources. If no sufficient source is found, the application can say so instead of inventing an unsupported answer.
SharePoint
AI knowledge base for SharePoint
SharePoint does not need replacing. People keep working in the environment they know. The AI adds a natural question interface to SharePoint.
The connection runs via Microsoft Graph. Retrieval augmented generation (RAG) finds matching passages in the indexed sources. The language model writes the answer from that. The model is swappable: Azure in the EU or local, depending on data risk.
If the problem is not knowledge but a recurring workflow, AI process automation is often the better start.
In practice
Where an AI knowledge base helps in daily work
01
AI knowledge base for support and back office
Which rule applies to case X? The answer comes from the current policy, with a link to the source. Colleagues no longer have to play internal search help.
02
AI knowledge base for onboarding
New hires find processes, templates and contacts in the folders you already have. Without a second wiki that nobody maintains.
03
AI knowledge base for sales
Find product facts, old proposals and service descriptions instead of waiting on replies and access.
04
AI knowledge management in regulated companies
Insurance, logistics, audit: traceable which source backs the answer. No unsupported text without a source.
Questions
Common questions about an AI knowledge base
Can an AI knowledge base connect to SharePoint?
Yes. I connect SharePoint as a source via Microsoft Graph. Further stores or selected mailboxes can be added depending on the use case.
Does the AI see documents a person cannot access?
Search respects existing permissions. Content a user cannot access is not used as an answer source, provided retrieval and rights checks are built that way.
Are company documents used to train a public model?
No. The architecture is chosen so company data stays in the intended environment, for example in an EU cloud or locally.
Does an AI knowledge base also work locally?
Yes. Depending on data risk, the model and other components can run in Azure in the EU or in your own infrastructure.
What happens if no matching information is found?
The application can be configured so it does not produce an unsupported answer when the available sources are not enough. Instead the user gets a clear notice.
In 15 minutes, see whether your sources are enough
Which store, which data risk, local or cloud. Non-binding. After that it is clear whether a first conversation about the setup makes sense.