Data integration for AI, so your teams find answers instead of searching

People spend less time hunting for information, and less data is copied by hand from one system to the next. Today the answer to a customer's question or a technician's problem often sits in a file share, an old ticket or the inbox of one experienced colleague. Once those sources are connected, an assistant finds it and shows where it came from. The agents described on our knowledge management and document processing use case pages then have the data they need.

Data protection tops the list of concerns, which is why permissions come first. In Bitkom's 2026 survey, 45% of companies that use AI name the concern that company data could fall into the wrong hands as a barrier. A search that ignores SharePoint rights would make that concern come true, so every answer and every agent action runs with the rights of the person it is for.

• Less time searching: answers from SharePoint, file shares and tickets, with the source shown
• Fewer repeated questions to the few experts who know where everything is
• New staff productive sooner, because they can ask the system instead of waiting for a colleague
• Customers answered faster, because service staff find the answer while the customer is still on the line
• Agents that look up and enter data in ERP, CRM and DMS, within the rights of the person they act for and with approval where it matters

In most companies the answers are spread over file shares, SharePoint sites, an ERP system, a ticket tool and people's inboxes. The same document exists in five versions, some folders are open to everyone by accident, and nobody knows which spreadsheet is current. A model on top of that answers confidently from the wrong source.

So we start with the data and leave the model for later: which sources answer which questions, which version counts, who may see what, and how changes reach the system.

Documents such as manuals, contracts, policies and past tickets usually go into a search index, split so that conditions and tables stay together, with the permissions of the original system stored alongside. Data that changes by the minute or must not be copied, such as stock levels, orders or customer records, is better read live through approved tools with their own access rules. Most systems need both.

• Permissions from SharePoint, file shares or your identity provider applied at search time, so nobody sees an answer from a document they cannot open.
• Changed documents re-indexed and deleted ones removed, so outdated answers do not survive.
• Personal data handled by agreed rules: excluded, pseudonymised or restricted to the people who may see it.
• Write access, where agents need it, only through tools with approvals and logs.

The index, the embedding model (the model that makes text searchable by meaning) and the connectors run where your data may go: in your own cloud account or on your own servers. Nothing is copied to a third-party service unless you decide it may be.

Before an assistant answers anyone, we test the retrieval on its own: for real questions from your teams, does the search return the passage that holds the answer, and does it hold back what the person may not see? Those tests stay with the system and run again whenever sources, splitting or models change.

We build this for companies that want assistants or agents on their own documents and systems but find their data scattered, duplicated or locked behind permissions, and for the IT teams that keep it running after the project ends.