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Azure AI Search & Foundry IQ Development
Reliable AI starts with finding the right information. We use Azure AI Search to help your agents find relevant content via hybrid, semantic, and agentic retrieval. The outcome is straightforward: better-grounded answers, clearer citations, and access based on user permissions.
Microsoft Solutions Partner (Data & AI) Hybrid + semantic + agentic retrieval Permission-aware & evaluated
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What is Azure AI Search – and how does it relate to Foundry IQ?
Azure AI Search is Microsoft’s enterprise search and retrieval service. It supports keyword, vector, hybrid, semantic, and agentic search, making it a core part of a majority of enterprise RAG systems.
In 2026, Azure AI Search became the foundation of Foundry IQ, Microsoft’s managed, permission-aware knowledge layer for AI agents in Microsoft Foundry. This gives agents a ready-to-use retrieval foundation rather than needing teams to develop RAG from scratch.
Prakash Software Solutions specializes in Azure AI Search development services, creating custom Azure AI Search pipelines and Foundry IQ knowledge bases.
Foundry IQ — One Knowledge Layer for Every Agent
AI agents are only useful when they can find the correct information. Foundry IQ provides agents with a shared way to access enterprise knowledge without explicitly needing to build a separate RAG setup for every project.
Reusable Knowledge Bases
Bring your business data into permission-aware knowledge bases that multiple agents can use. Set it up once and reuse it across projects. Connect your business data to knowledge bases that many agents can use at the same time. Set it up just once and continue to reuse it across different projects.
Retrieval that Understands Context
Foundry IQ doesn’t just return matching results. Agents can search across sources, establish a connection between related information if suitable, and create a more complete answer.
Managed Indexing
Foundry IQ handles ingestion, chunking, and vectorization for supported sources, reducing the search infrastructure your team needs to manage.
Part of the Intelligence Layer
Foundry IQ is known for managing organizational knowledge, while Fabric IQ handles business data and Work IQ delivers workplace context.
Built for Agent Frameworks
The Azure AI Search context provider connects Microsoft Agent Framework with Foundry IQ, making it easier for agents to retrieve the information they need for each task.
Governance Built In
Permission controls, source citations, and logging help keep knowledge access secure and make agent responses easier to trace.
Choosing The Ideal Retrieval Method for Your Data
“Just harness the power of RAG” is no longer enough. The quality of AI response depends on how information is being retrieved. Azure AI Search provides you with a variety of retrieval options. The right mix often depends on your data and the questions your users usually ask.
| Method | How it works | Best suited for |
|---|---|---|
| Keyword (BM25) | Finds and ranks exact or matching terms | Names, codes, acronyms, and specific terms |
| Vector search | Finds results on the basis of meaning and similarity | Concepts, paraphrased questions, and multilingual queries |
| Hybrid (RRF) | Combines keyword and vector search | A strong default for balancing precision and recall |
| Semantic ranker | Re-ranks results to identify the most relevant passages | Getting the best result at the top of the page |
| Agentic retrieval GA (2026-04-01 API) | Breaks complex questions into smaller searches and gives combined results | Multi-part and multi-step questions |
Our experts also tune metadata filters, strictness, and chunking. These details can have a huge impact on the quality of the final answer.
What Leads to RAG Hallucinations — And How to Prevent Them
Most enterprise RAG projects often fail to meet expectations not because of the model itself, but because of retrieval. Here are some of the most common issues we see and how we solve them.
| What usually goes wrong | How we address it |
|---|---|
| The model answers confidently but with wrong answers | We combine hybrid search, semantic ranking, and strictness controls, with groundedness checks to filter out all the weak results. |
| The answer is in the documents, but the chatbot fails to find the same | We use better chunking and vector coverage to make relevant content easier to retrieve. |
| Answers tend to rely on outdated information | Our experts ensure that your indexes are always up-to-date with incremental updates, recency filters, and source versioning. |
| Complete questions get incomplete answers | We utilize agentic retrieval to break down questions into smaller searches and combine the results. |
| Users can see content they shouldn't | Apply document-level security with Entra ID so users only retrieve content they're given access to. |
Connect Your Data, Wherever it Lives
Microsoft 365 and SharePoint
Documents, sites, and intranets, with existing permissions kept in place.
OneLake and Fabric IQ
Structured business data from the Microsoft ecosystem.
Azure Blob and Databases
Files and data from SQL and NoSQL databases.
Existing Azure AI Search Indexes
Reuse and build on your current search indexes.
The Web
Curated external sources for latest information.
MCP Source Preview
Connect data and tools via Model Context Protocol connectors.
CRM, ERP, and Ticketing
Connect systems like Dynamics 365, Salesforce, and support platforms.
Scanned and Multimodal Content
Prepare PDFs, images, and other types of documents or media for retrieval with Content Understanding.
How We Test RAG Quality Before and Post Launch?
We don’t launch RAG without extensively testing it. We use real user questions to build an evaluation set, measure response quality, and keep checking performance post-launch.
Grounded Answers
Check whether claims are supported by retrieved sources and flag answers that aren’t.
Recall & Precision
Make sure retrieval finds the right information without returning too much irrelevant content.
Relevant Answers
Check whether the response answers the question clearly and includes the right source.
Production Monitoring
Track quality, response time, and cost post-launch, and rectify any kinds of problems before they become bigger nuisances.
Always Keep Agent Access Secure
Permission Aware Retrieval
Leverage Entra ID and document-level permissions to ensure that agents only access information that the user is allowed to see.
Protect Your Data
Protect sensitive data with PII detection, private networking, encryption, Key Vault, logging, and retention controls that fit your security and compliance needs.
Azure AI Search or Foundry IQ: Which Fits?
| Option | Best when you need… |
|---|---|
| Azure AI Search | Complete control over indexing, ranking, custom skills, and the search pipeline |
| Foundry IQ | A managed knowledge layer that multiple agents can share |
| Both | Foundry IQ for shared knowledge and custom Azure AI Search indexes for specialized retrieval |
Our Azure AI Search & RAG Services
Our Azure AI Search development services help businesses design, build, and optimize safe retrieval pipelines for enterprise AI apps.
Develop RAG Solutions
Design and develop RAG solutions that give AI responses the right context, with grounding and citations.
Set up Foundry IQ
Create permission-aware knowledge bases that your agents can reuse across projects.
Tune Retrieval
Fine-tune chunking, hybrid, semantic, and agentic retrieval, along with filters and strictness.
Ground Your Agents
Connect retrieval to your agents via the Microsoft Agent Framework.
Test and Monitor Quality
Measure groundedness and response quality, then monitor performance post-launch.
Fix or Migrate Current RAG
Identify what’s holding an existing search or RAG system back, make necessary fixes, or move it to a better setup.
Where Grounded AI Makes a Difference
Policy and Compliance
Provide agents access to approved, permission-aware information with citations for review and audits.
Customer and Field Support
Assists agents in finding answers and resolving issues with the help of the latest knowledge and documentation.
Contracts and Documents
Search large document collections and answer complex questions across multiple sources.
Product and Engineering
Give teams grounded answers from product specs, runbooks, and internal wikis.
Sales and RFPs
Identify relevant information from previous proposals and product data, with sources to back it up.
Healthcare and Finance
Support high-stakes use cases with tighter permissions, reliable retrieval, and up-to-date information.
Build RAG Your Agents
Share your data sources and the questions your agents need to answer. We’ll help you design the right retrieval setup, test its quality, and keep access secure.
Client Success Stories
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Frequently Asked Questions
No. Azure AI Search enhances Foundry IQ. You can leverage it directly when it’s necessary and when you want far better control. Foundry IQ is often used for shared, permission-aware knowledge bases. They can also be used together.
Agentic retrieval breaks down complex questions into smaller searches, runs them side by side, and combines the results. It is designed for RAG and AI agent workflows and is generally available in the 2026-04-01 REST API.
Keyword search matches terms; vector search matches meaning. Hybrid search is a combination of both, and semantic ranking re-ranks the results to find the most relevant ones. Hybrid with semantic ranking is a common production setup.
There are plenty of reasons as to why they deliver such responses. Some of the most common are usually poor chunking, weak retrieval, outdated content, and missing filters. Our experts address them by better chunking, hybrid and semantic search, filtering, freshness controls, and groundedness checks.
We keep retrieval permission-aware by using Entrance ID, document-level permissions, and security trimming. This allows both users and agents to only retrieve content they are given access to. Foundry IQ knowledge bases support permission-aware retrieval by design.