Foundry IQ: Document Intelligence
What is Foundry IQ?
Foundry IQ is Azure AI Foundry's unified knowledge layer that enables agents to access enterprise documents through intelligent retrieval.
Key Capabilities
| Capability | Description |
|---|---|
| Knowledge Bases | Automatic indexing and vectorization of documents |
| Agentic Retrieval | AI-driven search with planning, iteration, and reflection |
| Enterprise Security | Built-in Entra ID authentication and Purview integration |
| Multi-format Support | PDFs, Word, PowerPoint, and unstructured text |
How Agentic Retrieval Works
Unlike simple vector search (find similar text), agentic retrieval uses AI to:
User: "What's our policy for notifying customers during extended outages?"
┌─────────────────────────────────────────────────────────────┐
│ Step 1: PLAN │
│ Agent decomposes into sub-queries: │
│ • "customer notification policy" │
│ • "extended outage definition" │
│ • "communication requirements during incidents" │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Step 2: ITERATE │
│ For each sub-query: │
│ • Search knowledge base │
│ • Evaluate relevance of results │
│ • Refine search if needed │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Step 3: REFLECT │
│ Before responding: │
│ • Do I have enough information? │
│ • Are sources consistent? │
│ • Can I cite specific documents? │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Response with Citations │
│ "According to our Customer Service Policies (page 2), │
│ customers must be notified within 15 minutes of a │
│ confirmed outage. The Outage Management Policy (page 1) │
│ defines extended outages as those exceeding 4 hours..." │
└─────────────────────────────────────────────────────────────┘
Why This Matters for Customers
Problem: Simple RAG Fails on Complex Questions
Basic retrieval-augmented generation (RAG) does one search and uses whatever comes back. This fails when:
- Questions have multiple parts
- Information spans multiple documents
- The obvious search terms don't match the document language
Solution: Agentic Retrieval Reasons About the Search
The agent acts like a knowledgeable employee who:
- Understands what's really being asked
- Knows to check multiple sources
- Reconciles conflicting information
- Admits when it can't find an answer
Customer Talking Points
| Question | Response |
|---|---|
| "Why not just use search?" | "Search finds documents. Agentic retrieval finds answers — and knows when to look in multiple places." |
| "What about hallucination?" | "Every response cites specific documents. Users can click through to verify. The agent says 'I don't know' rather than guess." |
| "Can it handle our complex policies?" | "The Plan-Iterate-Reflect approach handles multi-part policies. Let me show you with this example..." |
Technical Details
Document Processing Pipeline
- Chunking: Preserves sentence boundaries, typically 500-1000 tokens
- Embedding: Azure OpenAI text-embedding-3-large (3072 dimensions)
- Index: Azure AI Search with hybrid (keyword + vector) retrieval
Search Configuration
# Hybrid search combines:
# 1. Vector similarity (semantic meaning)
# 2. Keyword matching (exact terms)
# 3. Semantic ranking (re-ranking for relevance)
query_type = "vectorSemanticHybrid"