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Microsoft Foundry

From prototype
to production AI

Azure AI Foundry is now Microsoft Foundry, rebranded to reflect its scope: 80,000+ enterprises, 80% of Fortune 500, and 1,400+ business system integrations. Foundry REST API is now GA. GPT-5.4 generally available March 2026. We build on what's current, not what launched at Ignite.

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What we build

Six capabilities.
Production AI architecture.

AI Application Architecture
Model selection, RAG strategy, agent orchestration, integration patterns. A blueprint that scales from POC to production without rebuilding.
Foundry IQ: Agentic RAG
Foundry IQ (powered by Azure AI Search) connects agents to multiple data sources via one entry point (Fabric, SharePoint, custom APIs) with built-in user access permissions. Multi-hop retrieval grounded in your governance model.
Durable Agents (2026)
New HITL pattern: agents using Azure Durable Functions + Agent Framework + SignalR that survive restarts and wait days for human approval. Agent-to-Agent (A2A) calling for complex orchestration. Production-grade, not demo-grade.
Fine-Tuning
When foundation models need domain-specific accuracy. Industry terminology, specific output formats, specialized tasks. Better results for your use case.
Fabric Integration
Connect AI Foundry to your Microsoft Fabric data. Agents that query your Lakehouse. RAG applications grounded in governed datasets. AI and data, unified.
Responsible AI
Content safety, bias evaluation, transparency documentation. Configured as standard on every implementation. Your AI applications meet governance requirements from day one.

Delivery timeline

14 weeks to production AI

Four phases from use case definition through live deployment. Each phase has a defined output.

Weeks 1–2
Use Case Definition
Define objectives and success metrics. Select models. Design RAG strategy and data integration. Establish responsible AI requirements.
Weeks 3–6
Proof of Concept
Deploy AI Foundry environment. Implement RAG with sample data. Build agent with core capabilities. Validate accuracy against success metrics.
Weeks 7–12
Production Development
Expand data integration. Add tool integrations. Implement observability and monitoring. Configure security and private networking.
Weeks 13–14
Delivery Model
Deploy to production. Tune performance and cost. Train operators. Establish improvement and monitoring process.

Ready to ship
production AI?

We'll help you define use cases, architect solutions, and deploy AI that creates actual business value, not demo-ware.

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