Azure AIAzure AI Foundry

From prototype to production. The platform for building AI applications that actually ship.

The Challenge

When the Numbers Don't Line Up.

The prototype graveyard.

01

Demo Day Was Amazing. Then What?

Your data science team built a proof-of-concept in a Jupyter notebook. The demo impressed everyone. But turning that notebook into a production application requires different infrastructure, different security review, different skills. The prototype sits on a shelf while the business problem remains unsolved.

02

Model Chaos

Teams are experimenting with GPT-4, Claude, Llama, and fine-tuned models. Nobody knows what's deployed where. There's no central registry, no responsible AI review, no cost visibility. You're not building an AI capability — you're accumulating AI experiments.

03

AI That Doesn't Know Your Business

Foundation models know general knowledge. They don't know your customers, your products, or your procedures. When you ask about your specific situation, you get generic answers. The gap between AI capability and business utility is filled with custom engineering your team doesn't have time to build.

Core Benefits

What AI Foundry Provides

Eliminate data silos, lower licensing costs, and empower executive teams with one source of truth.

Fabric Advantage

Every Model, One Platform

Foundry Models includes Azure OpenAI (GPT-4, o1), Anthropic Claude, Meta Llama, Mistral, DeepSeek, and thousands more. Explore, compare, deploy — without separate vendor relationships. One catalog, one API, one billing.

fabric-workspace.microsoft.com

OneLake Storage

Sync: Active
AWS S324.5 TB
Azure Blob18.2 TB
GCS9.4 TB
Unified Fabric Workspace52.1 TB

What You Actually Get

End-to-end Microsoft Fabric implementation from architecture blueprinting to governance.

AI Application Architecture

We design your AI application — model selection, RAG strategy, agent orchestration, integration patterns. A blueprint that scales from POC to production without rebuilding.

Fabric Tenant
PROD (F64)
DEV (F32)

RAG Implementation

We build retrieval-augmented generation applications grounded in your data. Document indexing, embedding strategies, response generation with citations. AI that answers from your knowledge, not general knowledge.

Bronze LayerRaw Data
Silver LayerCleaned
Gold LayerBusiness Ready

Agent Development

We build agents using Azure AI Agent Service. Conversation design, tool configuration, production deployment. Agents that automate real business processes, integrated with your systems.

Source
Lakehouse

Fine-Tuning When Needed

When foundation models need domain-specific accuracy, we fine-tune. Industry terminology, specific output formats, specialized tasks. Better results for your use case.

Asset NameSensitivity
Customer_PIIHighly Confidential
Q3_FinancialsConfidential
Roadmap

Fourteen Weeks to Production AI

A proven path. No endless discovery. Real deliverables, every phase.

Fixed Scope Guarantee

10-Week Execution

Structured phased delivery with dedicated Microsoft Fabric architects. No endless discovery, just results.

Timeline GO LIVE
Wk1
Wk4
Wk8
Audit-Ready Infrastructure
Full Codebase Ownership
Week 1-2

Use Case Definition

We define objectives and success metrics. We select models. We design RAG strategy and data integration. We establish responsible AI requirements.

Key Deliverable
AI application architecture
Week 3-6

Proof of Concept

We deploy AI Foundry environment. We implement RAG with sample data. We build agent with core capabilities. We validate accuracy.

Key Deliverable
Working POC
Week 7-12

Production Development

We expand data integration. We add tool integrations. We implement observability. We configure security.

Key Deliverable
Production-ready application
Week 13-14

Deployment

We deploy to production. We tune performance and cost. We train operators. We establish improvement process.

Key Deliverable
AI application live
Featured Case Study

Service Technician Co-Pilot

Context

Medical device manufacturer. 400 field service technicians. 10,000+ product documents across PDFs, wikis, and training materials.

The Challenge

Technicians needed answers about specifications, installation, and troubleshooting. Documentation existed but was scattered and unsearchable. Finding the right information took 15+ minutes per question. First-time fix rates suffered.

What We Built
RAG application using AI Foundry and Foundry IQ
Document indexing across all product documentation
Conversational interface for technicians to ask questions
Citation of source documents for every answer
Integration with service ticketing system

"Answers in seconds instead of 15+ minutes. First-time fix rate improved 22%. Documentation that existed but was inaccessible is now available to the entire field team."

Got Questions?

Frequently Asked Questions

Copilot Studio is low-code for conversational agents with straightforward knowledge retrieval. AI Foundry is for custom AI applications that need fine-tuned models, complex RAG, or deep system integration. Sometimes you use both.

Ready to ship production AI?

We'll help you define use cases, architect solutions, and deploy AI that creates actual business value.