How It Works
We audit your document sources — SharePoint, Confluence, Blob Storage, SQL, or third-party systems — and design the Azure AI Search index schema, chunking strategy, and embedding configuration.
We build the end-to-end RAG pipeline — ingestion, chunking, embedding, indexing, retrieval, and generation — connecting Azure AI Search with Azure OpenAI inside an Azure AI Foundry Prompt Flow.
We evaluate retrieval accuracy, groundedness, and answer relevance against a curated test set — tuning semantic ranking, chunk overlap, and reranking until the pipeline meets your quality bar.
What's Included
Combine vector similarity search with BM25 keyword search in a single Azure AI Search query — ensuring high recall for semantic queries while maintaining precision for exact-match lookups.
Apply Azure AI Search's semantic ranker to reorder results by language understanding — dramatically improving answer relevance for ambiguous or conversational queries.
Index content from SharePoint, Confluence, Azure Blob Storage, SQL databases, and APIs — creating a unified knowledge index accessible to any Azure AI agent or application.
Preserve document-level security by filtering search results against the querying user's Entra ID group memberships — ensuring users only retrieve content they are authorised to see.
Configure Azure AI Search indexers with incremental indexing policies — automatically detecting and processing new or updated documents so the knowledge base stays current without manual re-indexing.
Implement groundedness checks in the Prompt Flow pipeline — verifying that every generated answer cites retrieved passages, and blocking or flagging responses unsupported by the index.
Who It's For
Organisations with thousands of internal documents, policies, runbooks, or product specifications — employees can't find what they need, and AI search changes that.
Teams building AI knowledge agents grounded in SharePoint or Confluence — you need a retrieval pipeline that respects permissions, handles large corpora, and returns accurate answers.
Engineering teams building any Azure AI agent that requires accurate, grounded responses — a well-tuned RAG pipeline is the difference between a demo and a production system.