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Chat With Your Data: SharePoint
SharePoint's built-in search is keyword-based. Type the wrong word and you get nothing. Type the right word and you get forty documents to read through. Neither outcome helps an employee who needs a specific policy answer in the next thirty seconds.
We build a custom RAG layer that indexes your SharePoint documents (pages, files, and document libraries) into a vector store, then routes natural language questions to the most relevant chunks. Answers cite the source document and section. Permission-aware retrieval means employees only search documents they have access to.
Deploy as a Teams bot, a web widget, or a standalone app. Works with SharePoint Online and Microsoft 365.
Tell us about your SharePoint environment.
Six components that make natural language search work on your SharePoint environment, from document ingestion through to permission-checked answers.
The ingestion pipeline connects to SharePoint through the Microsoft Graph API. It indexes pages, files, and document libraries, extracting text, metadata, and structural context from each item. New and updated documents are picked up on a configurable sync schedule.
Indexed content is stored in a vector database with metadata attached: site name, library name, document type, author, and last modified date. This lets users filter their questions by site or library when they need precision, or search across everything when they want breadth.
The retrieval layer uses delegated authentication, meaning the AI only returns documents the querying user has permission to read in SharePoint. If a document is in a restricted library, users without access do not get it in their answers.
Questions are answered in plain language with a reference to the source document and section. Users can ask follow-up questions within the same session. The interface is deployed as a Teams bot, embedded web widget, or standalone web app, your choice.
Every answer includes the document title, library, and a direct link to the SharePoint page or file. Employees can verify the answer immediately and follow the link if they need the full document.
Documents not updated within a configurable window are flagged in the answer, so users know whether the answer is based on a policy updated last week or a process document from three years ago.
From API connection to live Teams bot, the build sequence.
Connect via Microsoft Graph API
We register an Azure AD application with appropriate Graph API permissions. The ingestion pipeline authenticates and begins pulling documents from the SharePoint sites and libraries you specify.
Index documents into a vector store
Each document is chunked, embedded, and stored in the vector database with metadata tags. The chunking strategy preserves section context, answers reference the correct part of a long document, not just the document title.
Configure permission mapping
SharePoint permission groups are mapped to the retrieval layer. When a user queries the system, their identity is verified and retrieval is scoped to documents they have read access to.
Deploy the query interface
The chat interface is deployed in the channel your team uses. For most clients this is a Microsoft Teams bot, installed in the relevant Teams channels so employees can ask questions without leaving their workflow.
Test with real queries
Before go-live, we test with a sample of actual questions your employees ask most frequently. Retrieval quality is tuned and any gaps in document coverage are identified and resolved.
Monitor and improve
A dashboard shows which queries are being asked, which get confident answers, and which fall back to "I couldn't find a reliable answer." You can use this to identify documentation gaps and improve coverage over time.
The architecture uses Microsoft Graph API and SharePoint connectors for document ingestion. The LLM layer runs on Azure OpenAI or Anthropic depending on your data residency and compliance requirements. The vector store is Azure AI Search or Pinecone, both support metadata filtering and permission-scoped retrieval.
Authentication uses delegated OAuth flows so the system acts as the querying user for permission checks. We can also implement a service account approach with an explicit document-level permission lookup at query time, whichever fits your IT security policy.
All document content stays within your chosen cloud region. We do not store SharePoint content on third-party infrastructure outside your selected vector store provider.
The right fit is an organisation using SharePoint Online as its primary document store, with maintained content employees actually need to find.
When SharePoint has thousands of documents across dozens of sites, employees stop using it because they can't find anything. A natural language search layer changes the dynamic. People can describe what they need in their own words.
Employees ask HR the same questions repeatedly because they can't find the policy document. A custom search layer routes those questions to the right policy page automatically, reducing the load on HR and giving employees instant answers.
Support staff and engineers need fast access to runbooks, system documentation, and known-issue logs. A SharePoint search layer means asking a question in Teams and getting the relevant runbook section immediately.
We’d rather decline than take a project that won’t deliver value.
SharePoint as a file graveyard with no maintained content
RAG is only as good as the documents it searches. If your SharePoint has years of outdated policies, conflicting procedure documents, and files nobody maintains, the AI will return confusing or incorrect answers. The right first step is a content audit and cleanup, then we build the search layer.
Teams that need real-time data from SharePoint lists
RAG search works well on document content. If your use case is querying live data from SharePoint lists or Power Apps data sources in real time, a different integration pattern is more appropriate. We can scope that separately.
Tell us how many sites, the approximate number of documents, and the most common types of questions your employees currently search for. We'll reply within one business day.