Azure AI Search · Enterprise Knowledge Discovery India

Vector Search & RAG for
Enterprise Knowledge

Azure AI Search (formerly Cognitive Search) is the retrieval backbone of every enterprise RAG solution — combining hybrid vector + keyword search, semantic ranking, and integrated AI enrichment. SchwettmannTech implements AI Search as the intelligence layer for Azure OpenAI Copilots, Dynamics 365 knowledge bases, SharePoint intelligent portals, and enterprise document discovery platforms across India.

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Azure AI Search Certified Partner India

RAG & Copilot Backbone Deployed

Sub-100ms Search at Enterprise Scale

Sub-100ms

Vector search latency at enterprise scale

Hybrid

Vector + BM25 for best recall

Semantic

L2 reranker boosts relevance 40%

Unified

Index SharePoint, SQL & Dataverse together

Azure AI Search · Index Dashboard

Live

2.4M

Docs Indexed

↑ AI Enriched

98.7%

Answer Accuracy

↑ Semantic Rank

42ms

P95 Latency

↓ Sub-100ms

Hybrid

Search Mode

↑ +40% Recall

Hybrid Vector Search — RAG Backbone

2.4M documents · OpenAI embeddings · BM25+Vector

Live

Semantic Ranking — L2 Reranker Active

Answer relevance +40% · 98.7% groundedness

Active

AI Enrichment — Skills Pipeline Running

OCR → Entities → Key Phrases → Vectors · Realtime

Enriching

Multi-source — SharePoint+SQL+Dataverse

Unified index · 3 sources · Auto-sync every 15 min

Syncing

Azure AI Search: Hybrid search query for "payment terms supplier contract" retrieved 8 highly relevant chunks from 2.4M documents in 42ms. Semantic reranker promoted Contract_Framework_v3.pdf §14.2 to position 1 — GPT-4o answer groundedness: 99.1%.

Sub-100ms

Vector Search Latency at Enterprise Scale

Hybrid

Vector + BM25 Search for Maximum Recall

Semantic

L2 Reranker Boosts Answer Relevance 40%

Unified

Index SharePoint, SQL & Dataverse Together

Vector SearchHybrid BM25+VectorSemantic RankingAI EnrichmentIntegrated VectorisationSharePoint IndexerDataverse IndexerBlob Storage IndexerOCR SkillsetEntity ExtractionRAG BackboneOpenAI EmbeddingsVector SearchHybrid BM25+VectorSemantic RankingAI EnrichmentIntegrated VectorisationSharePoint IndexerDataverse IndexerBlob Storage IndexerOCR SkillsetEntity ExtractionRAG BackboneOpenAI Embeddings

Services

Azure AI Search Implementation Services

From hybrid index design to RAG architecture and AI enrichment — SchwettmannTech implements Azure AI Search as the knowledge retrieval foundation for your enterprise AI applications.

Vector & Hybrid Search Index Design

Design Azure AI Search indexes with vector fields alongside traditional keyword fields. Implement hybrid search using Reciprocal Rank Fusion (RRF) that combines vector similarity scores with BM25 keyword relevance — delivering the best recall of any single retrieval method. We tune vector dimensions, HNSW parameters, and hybrid weighting for your specific content domain.

Hybrid Search · RRF Fusion · Vector Fields · BM25 · HNSW Tuning

Semantic Ranking Configuration

Configure Azure AI Search's L2 semantic reranker — a cross-encoder model that deeply understands query-document relevance beyond keyword matching. Semantic ranking applied to top-50 hybrid results improves answer quality in RAG pipelines by 40%. We configure semantic configurations, answer extraction, and captions for each content type.

Semantic Ranking · L2 Reranker · Answer Extraction · RAG Quality

AI Enrichment & Skillsets

Build AI enrichment pipelines that automatically enrich your document index during ingestion: OCR for scanned documents, entity extraction (people, organisations, locations), key phrase extraction, language detection, image captioning, and custom Azure ML skill invocation — all running in parallel during indexing.

OCR Skillset · Entity Extraction · Key Phrases · Custom ML Skills

Integrated Vectorisation

Configure Azure AI Search's built-in integrated vectorisation — AI Search automatically calls your Azure OpenAI embedding model, chunks documents using the configured splitter strategy, and stores vectors in the index. No external chunking pipeline code required — index new documents by simply uploading them to the connected data source.

Integrated Vectorisation · Auto-chunking · OpenAI Embeddings · No-code

Multi-source Indexer Configuration

Build unified enterprise search indexes spanning multiple data sources using built-in indexers: Azure Blob Storage, SharePoint Online, Azure SQL, Cosmos DB, and Dataverse. Content from all sources is combined in a single searchable index — employees find answers regardless of where the original document is stored.

Multi-source · SharePoint · SQL · Dataverse · Blob · Unified Index

OpenAI & D365 Copilot Integration

Azure AI Search is the retrieval backbone for Azure OpenAI RAG applications and Microsoft Copilot Studio. We integrate AI Search with Dynamics 365 Knowledge Base, Power Pages portals, and custom web applications — enabling intelligent search and Copilot experiences grounded in your enterprise data.

Azure OpenAI RAG · Copilot Studio · D365 Knowledge · Power Pages

Capabilities

Complete Capability Coverage

Our certified team covers every facet of this service — from strategy and implementation to managed operations and continuous optimisation.

Retrieval

The RAG Retrieval Foundation

Every high-quality RAG application needs great retrieval. Azure AI Search's hybrid search consistently outperforms pure vector or pure keyword approaches by combining semantic meaning with exact term matching — critical when answers may hinge on specific product codes, regulation numbers, or terminology.

Hybrid RRF Retrieval
Semantic L2 Reranker
Filter by Metadata
Faceted Navigation

Performance

Sub-100ms at Any Scale

Azure AI Search scales horizontally — partition and replica counts adjust to meet throughput requirements. Sub-100ms P95 search latency at 1,000+ queries per second is standard for properly provisioned indexes. Auto-scaling handles query spikes during business hours.

Horizontal Scaling
Sub-100ms P95
High Availability
Auto-scale Replicas

🧠Intelligence

AI-Enriched Content

Documents indexed through AI enrichment skillsets contain extracted entities, key phrases, image captions, and translated content — making previously unsearchable content (scanned PDFs, images, foreign language documents) fully searchable and retrievable.

Sync

Real-time Index Sync

Built-in change tracking indexers keep your AI Search index in sync with source data. SharePoint document updates are reflected in the search index within minutes. Dataverse record changes trigger automatic re-indexing via built-in scheduling.

Architecture

RAG Architecture Design

We design the complete RAG stack: chunking strategy, embedding model selection, index schema, hybrid search configuration, semantic ranking, and GPT-4o prompt construction — optimised for accuracy on your specific document types and query patterns.

Analytics

Search Analytics Dashboard

Azure AI Search provides query analytics: top queries, zero-result queries, click-through rates, and result position distributions. We build Power BI dashboards on these signals to continuously improve search relevance and identify content gaps.

🌏India

India Data Residency

All Azure AI Search indexes deployed in Azure Central India (Pune) or South India (Chennai). Document content, vectors, and enriched metadata never leave your Azure tenant. DPDP Act 2023 compliant by design — with private endpoint configuration preventing public internet access to your search index.

Delivery

Our Azure AI Search Delivery Framework

A structured 3–5 week process to design, build, and deploy an enterprise-grade Azure AI Search solution.

Phase 1 — Week 1

Search Architecture Design

Define index schema, sources & retrieval strategy

Analyse content types, query patterns, and performance requirements. Design index schema with vector fields, filterable metadata, and semantic configuration. Define chunking strategy and embedding model. Map data sources to indexers.

Index Schema DesignChunking StrategySource MappingSemantic Config

Phase 2 — Week 1–2

Data Ingestion & AI Enrichment

Build indexers, skillsets & vectorisation

Configure built-in indexers for all data sources. Build AI enrichment skillset pipeline — OCR, entity extraction, key phrases. Set up integrated vectorisation with Azure OpenAI embeddings. Initial full index run and accuracy validation.

Indexer ConfigSkillset BuildVectorisationFull Index Run

Phase 3 — Week 2–3

Search Quality Tuning

Optimise hybrid search & semantic ranking

Evaluate search recall and precision on a curated test query set. Tune hybrid RRF weights, semantic configuration, and filter logic. A/B test different chunking strategies. Validate RAG answer quality end-to-end with Azure OpenAI.

Recall TestingHybrid TuningSemantic ConfigRAG End-to-End

Phase 4 — Week 3–5

Integration, Go-Live & Monitoring

Connect to applications and deploy production

Integrate AI Search API with Copilot Studio, web application, or D365. Configure search analytics and Power BI dashboard. Production deployment with private endpoints. Monitoring for index sync health, query latency, and answer quality.

App IntegrationPrivate EndpointsAnalytics DashboardProduction Deploy

Industries

Azure AI Search for Indian Enterprise

Enterprise search and RAG retrieval for India-specific content — multilingual documents, regulatory knowledge bases, and domain-specific corpora.

BFSI · Policy Search

Healthcare · Clinical KB

Manufacturing · Docs

Retail · Product Search

Legal · Case Law KB

EdTech · Content Search

Infra · Tender Search

Energy · Compliance

Multilingual Indian Content Search

Azure AI Search's language analysers support Hindi, Tamil, Telugu, Bengali, Marathi, and other Indian languages — enabling accurate keyword search across multilingual document corpora. Combined with OpenAI multilingual embeddings, hybrid search retrieves relevant content regardless of whether the query and document are in the same language. We've built cross-lingual search indexes spanning English and Hindi for legal, healthcare, and government document repositories.

Knowledge Access Impact

Proven Results: Azure AI Search Results

Outcomes from SchwettmannTech's Azure AI Search implementations across Indian organisations.

40%

Improvement in RAG answer relevance using semantic ranking vs keyword-only

Sub-100ms

P95 search latency maintained at 1,000+ queries/second in production

98%

Employee satisfaction with enterprise Copilot search accuracy after AI Search tuning

70%

Reduction in time employees spend searching for policy and procedure documents

Customer Stories

What Our Clients Say

"SchwettmannTech implemented Azure AI Search as the retrieval layer for our D365 Knowledge Copilot — indexing 15,000 policy documents, SOPs, and training materials from SharePoint. Before AI Search, agents spent 8 minutes per call searching for answers. Now the Copilot finds the right policy chunk in under 100ms, and agents have accurate answers in seconds. First-call resolution improved from 68% to 89%."

Prashant Desai

VP Customer Experience · Insurance, Pune

"Our legal team maintains a 200,000-document case law and regulatory library. Azure AI Search's hybrid search finds relevant case precedents that pure keyword search was missing — the semantic reranker understands legal concept similarity, not just term overlap. Research time per matter dropped from 4 hours to 35 minutes. The ROI is measured in partner billable hours recovered."

Sunita Rao

Managing Partner · Law Firm, Delhi

"We built a multilingual product catalogue search using Azure AI Search with Hindi and English content in the same index. Our B2B buyers search in Hindi, and the hybrid vector search finds English product descriptions correctly — the semantic embedding understands cross-language concept similarity. Catalogue search conversion improved 28% in the first month."

Vikram Jain

CTO · B2B Marketplace, Ahmedabad

FAQs

Common Azure AI Search Questions

Designing a RAG architecture or enterprise search solution? Our Azure AI Search architects provide free technical design reviews.

Azure AI Search combines traditional keyword search (BM25, just like Elasticsearch) with vector search and Microsoft's semantic reranking model — in a single fully managed service. Key differences: (1) Native Azure OpenAI integration for embeddings and RAG without external glue code; (2) Built-in AI enrichment skillsets (OCR, entity extraction, etc.); (3) Integrated vectorisation — AI Search calls your embedding model automatically during indexing; (4) Semantic L2 reranker that deeply understands query-document relevance; (5) Fully managed — no cluster administration, patching, or scaling management required. For RAG applications on Azure, AI Search is almost always the right choice over self-managed Elasticsearch or Pinecone.

Hybrid search combines dense vector retrieval (semantic similarity) with sparse BM25 keyword retrieval, fusing their results using Reciprocal Rank Fusion (RRF). Pure vector search excels at semantic concept matching but can miss exact term matches — critical when your query contains specific product codes, regulation numbers, or proper nouns that don't appear in training data for the embedding model. Pure keyword search misses documents with semantically related but differently worded content. Hybrid search gets the benefits of both: in our RAG deployments, hybrid consistently outperforms pure vector by 15–25% on recall metrics.

Integrated vectorisation (available in AI Search 2023-11+ API versions) lets you define a vectoriser in your index that points to your Azure OpenAI embedding deployment. When you upload a document to the connected data source, AI Search automatically calls your embedding model, chunks the document using your configured text splitter, generates embeddings for each chunk, and stores them in the vector field — all without any external chunking pipeline code. This dramatically simplifies RAG architecture: your data pipeline is just "upload file to SharePoint/Blob," and AI Search handles everything else.

Azure AI Search pricing is based on search units (SU = 1 partition × 1 replica). The Basic tier (suitable for development and small deployments) costs approximately $70/month USD. Standard S1 (most common for enterprise RAG) costs approximately $250/month per SU. A typical enterprise deployment with 1M documents requiring high availability uses Standard S2 with 2 replicas and 2 partitions — approximately $2,000/month. At current USD/INR rates, this is approximately ₹1.6L–₹2L/month for a full enterprise deployment. Indexer runs and AI enrichment skillset calls incur additional charges based on document volume. SchwettmannTech provides detailed cost modelling before implementation.

Azure AI Search integrates with Copilot Studio as a knowledge source via the native Azure AI Search knowledge connector. In Copilot Studio, you configure your AI Search index endpoint, select the fields to use for retrieval, and Copilot Studio automatically performs hybrid search against your index for every user query — passing relevant chunks to the generative AI model for answer formulation. This integration requires no custom code and can be configured in Copilot Studio in under 2 hours. For more advanced scenarios (custom ranking, metadata filtering, multi-index queries), we build custom connector plugins in Copilot Studio that call the AI Search REST API directly.

Build the Retrieval Foundation for Your Enterprise AI

Book a free Azure AI Search Architecture Review. We'll evaluate your data sources, design your hybrid search index, and demonstrate sub-100ms retrieval on your content — no commitment required.

Azure OpenAI Service Azure AI Studio

Vector Search & RAG forEnterprise Knowledge

Azure AI Search Implementation Services

Complete Capability Coverage

The RAG Retrieval Foundation

Sub-100ms at Any Scale

AI-Enriched Content

Real-time Index Sync

RAG Architecture Design

Search Analytics Dashboard

India Data Residency

Our Azure AI Search Delivery Framework

Azure AI Search for Indian Enterprise

Proven Results: Azure AI Search Results

What Our Clients Say

Common Azure AI Search Questions

Build the Retrieval Foundation for Your Enterprise AI

Vector Search & RAG for
Enterprise Knowledge