Azure AI Search & Enterprise Knowledge Graphs
Unlock corporate intelligence. Deploy vector search and Retrieval-Augmented Generation (RAG) over millions of internal documents, PDFs, and database records.
Core Service Capabilities
Designed and delivered by experienced Australian Microsoft solution architects.
Vector & Hybrid Search
Combine keyword matching with dense vector embeddings to deliver hyper-accurate semantic search results across corporate files.
Vector Search • AIMulti-Format Document Ingestion
Index PDFs, Word docs, PowerPoint presentations, and SharePoint libraries with automated OCR and chunking strategies.
RAG • Document AISecurity-Trimming & Permissions
Ensure enterprise search respects Azure AD / M365 security permissions so users only retrieve documents they are authorized to view.
Security Trimming • Entra IDHybrid Search, Vector Embeddings & Semantic Reranking
Keyword search alone fails on complex questions, while pure vector search often struggles with exact part numbers, contract IDs, and domain-specific acronyms. We architect high-performance Hybrid Search pipelines in Azure AI Search combining lexical BM25 matching, dense vector embeddings, and deep learning cross-encoder reranking.
Reciprocal Rank Fusion (RRF)
Every user query executes parallel retrieval streams: an inverted index search using Lucene BM25 scoring, and a k-nearest neighbor (k-NN / HNSW) vector search powered by Azure OpenAI embedding models (`text-embedding-3-large`). Reciprocal Rank Fusion blends both result sets into an optimal unified list.
- Exact Match Integrity: Preserving precision for policy numbers, Australian Standards (AS/NZS), and client IDs.
- Conceptual Intent: Capturing synonyms, intent nuances, and natural language questions across repositories.
- HNSW Index Tuning: Calibrating vector graph parameters (`m`, `efConstruction`, `efSearch`) for millisecond query latency.
Microsoft Semantic Ranker (L2)
Once candidate documents are retrieved, Microsoft's Semantic Ranker applies cross-encoder machine learning models adapted from Bing. It evaluates the semantic contextual alignment between the search query and document excerpts, promoting the most authoritative answer to position one.
- Semantic Captions: Automatically extracting the exact paragraph answering the user's inquiry.
- Semantic Answers: Surfacing direct snippet answers with highlighted keyword evidence.
- Hallucination Defense: Feeding precisely ranked factual excerpts into downstream LLM RAG pipelines.
Document Chunking, AI Skillsets & Knowledge Projections
Enterprise knowledge is trapped in complex 200-page PDF reports, scanned engineering blueprints, Excel workbooks, and SharePoint intranet portals. We build automated ingestion skillsets that parse, normalize, and project structured knowledge.
Semantic Boundary Chunking
Arbitrary token slicing chops sentences in half, ruining embedding accuracy. We implement structure-aware chunking preserving Markdown headers, table rows, and paragraph boundaries with sliding-window overlap.
Multimodal OCR & Layout Analysis
Using Azure AI Document Intelligence skillsets, we extract complex financial tables, scanned signatures, and image captions into structured JSON, embedding both visual layout and tabular hierarchy.
Knowledge Store Projections
Index enrichments project directly into Azure Table Storage and Cosmos DB graph nodes, creating an auditable enterprise knowledge store supporting cross-document entity relationships.
Entra ID Security Trimming & Zero-Leakage Filters
The greatest risk in enterprise generative AI is information leakage—an AI assistant providing an employee with executive salary data or unannounced merger documents because access permissions were stripped during ingestion. We engineer watertight security trimming.
Access Control List (ACL) Ingestion
During index crawling, our indexers capture document-level Access Control Lists (ACLs) directly from SharePoint Online and Azure file storage. Each indexed chunk stores authorized Entra ID User Principal Names (UPNs) and Security Group Object IDs.
Security metadata synchronizes continuously as SharePoint permissions update.
Pre-Query Filter Enforcement
When a user executes a search or asks a Copilot agent a question, the application injects the caller's Entra ID group membership token into the mandatory search filter. The search engine automatically prunes unauthorized documents before vector or keyword matching begins.
Eliminates unauthorized data leakage at the database engine level.
Four-Stage Enterprise Search Implementation
Our Australian cloud AI specialists deploy enterprise Azure AI Search architectures through a rigorous, audit-tested four-stage delivery process:
Corpus Discovery & Schema Design
Audit enterprise document repositories, define metadata fields, select embedding models, and design semantic ranker configurations.
Ingestion Pipelines & Skillset Development
Build automated Azure AI indexers, custom OCR skillsets, boundary chunking algorithms, and vector embedding generators.
Security-Trimming & Relevance Calibration
Configure Entra ID ACL filtering, conduct benchmark precision/recall testing, and tune BM25 and vector weights using test query datasets.
RAG Integration & Production Handover
Connect search endpoints to Azure OpenAI / Copilot Studio agents, establish re-indexing schedules, and deliver operational runbooks.
Technical Prerequisites & Sizing
Before provisioning enterprise AI search instances, organizations should evaluate service capacity and cloud boundaries:
Acceptance Deliverables Checklist
- Azure AI Search index schema JSON definitions and skillset configs
- Entra ID security-trimming verification test suite report
- Benchmark relevance report (Mean Reciprocal Rank & NDCG metrics)
- Operational runbook with automated re-indexing and cost monitoring
Related AI & Data Modernization Services
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