Integrations · Azure AI Foundry
Azure AI Search indexes SharePoint Online natively — crawling documents, applying vector embeddings, and making 10 years of company knowledge instantly queryable by AI agents. Entra ID permissions are enforced at query time so agents never surface documents users aren't authorised to see.
What's Possible
Employees ask questions in plain English and receive precise, cited answers drawn from SharePoint documents — policies, project archives, product specs. The agent retrieves the most relevant chunks and surfaces the source document link.
Legal, HR, and compliance teams query the agent for the current version of any policy, regulatory requirement, or procedure. Azure AI Search indexes policy libraries with real-time delta crawls so answers always reflect the latest documents.
Project managers retrieve past deliverables, lessons learned, and technical specs from completed projects without manual SharePoint navigation. Semantic search surfaces relevant content even when exact terminology differs.
Teams meeting recordings and transcripts stored in SharePoint become fully searchable. Ask 'what was decided about the Q3 pricing change in the June leadership meeting?' and get the specific passage with a timestamp link.
New employees and managers ask the HR bot questions about leave policies, benefits, performance processes, and office procedures. The agent answers from the authoritative HR SharePoint site — with escalation paths for edge cases.
IT engineers query the runbook agent for step-by-step procedures during incidents. The agent retrieves the correct runbook version, adapts the steps to the specific environment described, and logs the retrieval for audit purposes.
How We Connect It
Azure AI Search uses the SharePoint Online indexer to crawl selected site collections. Entra ID permission inheritance is enforced at query time — users only retrieve documents their account is authorised to access in SharePoint.
Documents are chunked, embedded using text-embedding-ada-002, and stored as vectors in the Azure AI Search index. A hybrid retrieval strategy combines BM25 keyword search with vector similarity — maximising recall and precision.
The RAG pipeline is exposed via Semantic Kernel as a plugin that any AI agent can call. Copilot Studio wraps it into a conversational bot deployed to Teams, SharePoint embedded web part, or standalone portal.
Use Cases
Scenario: In-house counsel asks the agent for all indemnification clauses in vendor contracts signed in the last two years. The agent queries the indexed contract library and returns the relevant clauses with document references.
Outcome: Contract review preparation time reduced from half a day to under 30 minutes. Legal team queries the agent rather than asking junior staff to manually search SharePoint.
Scenario: Engineer asks 'what is the approved cable specification for outdoor installations in our UK sites?' The agent retrieves the current version of the relevant technical standard from the SharePoint engineering library.
Outcome: Engineers access authoritative standards in seconds rather than navigating multi-level SharePoint hierarchies. Version confusion eliminated as the index always reflects the current approved document.
Scenario: An on-call SRE receives a P1 alert at 2am. They ask the IT agent for the runbook for that specific alert type. The agent retrieves the runbook, confirms the current on-call rotation from SharePoint, and opens a ticket.
Outcome: MTTR reduced as responders spend time executing the recovery procedure rather than finding it. Runbook version accuracy guaranteed by real-time index updates.
Scenario: A new employee asks the HR bot about the company's remote work policy, equipment allowance, and performance review cycle. The agent answers from the current HR handbook sections and links to the full documents.
Outcome: HR team spends less time answering repetitive onboarding queries. Employees get consistent, policy-accurate answers rather than relying on colleague word-of-mouth.
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