Azure AIAzure AI Foundry
From prototype to production. The platform for building AI applications that actually ship.
When the Numbers Don't Line Up.
The prototype graveyard.
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.
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.
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.
What AI Foundry Provides
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.
Agent Service for Real Workloads
Azure AI Agent Service lets you build agents that actually do things. Connect to business systems via 1,400+ connectors. Configure tools and actions. Deploy, scale, monitor. Agents that automate processes, not just chat.
Ground AI in Your Data
Foundry IQ provides retrieval-augmented generation powered by Azure AI Search. Index your documents, knowledge bases, and databases. The AI responds with your information, cited with sources. Enterprise RAG without building the infrastructure.
Observability Built In
Foundry Observability provides end-to-end monitoring. Latency, throughput, cost, quality metrics. Trace logs of each agent's reasoning steps and tool calls. Know what your AI is doing, why it made decisions, and what it costs.
Responsible AI Controls
Content safety filters. Bias evaluation. Transparency documentation. Built into the platform, not afterthoughts. Your AI applications can meet enterprise governance requirements because the tools exist to make it practical.
Enterprise Scale and Security
Private endpoints, managed VNets, customer-managed keys. Deploy to 60+ regions. Scale from prototype to production without re-architecting. The security your organization requires, built into the platform.
What You Actually Get
Everything you need to go from fragmented data to a governed, production-ready platform.
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.
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.
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.
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.
Fabric Integration
We connect AI Foundry to your Microsoft Fabric data. Agents that query your Lakehouse. ML models that score data. RAG applications grounded in governed datasets. AI and data, unified.
Responsible AI Implementation
Content safety, bias evaluation, transparency. We configure these as part of every implementation. Your AI applications meet governance requirements from day one.
Fourteen Weeks to Production AI
Tangible deliverables each phase — from architecture sign-off to analysts on governed data.
Use Case Definition
We define objectives and success metrics. We select models. We design RAG strategy and data integration. We establish responsible AI requirements.
AI application architecture
Use Case Definition
We define objectives and success metrics. We select models. We design RAG strategy and data integration. We establish responsible AI requirements.
AI application architecture
Service Technician Co-Pilot
The Context
Medical device manufacturer. 400 field service technicians. 10,000+ product documents across PDFs, wikis, and training materials.
The Reality
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
The Outcome
"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."
Frequently Asked Questions
Got questions? We've answered the most common ones. If yours isn't here, reach out — we'll give you a straight answer.
Ready to ship production AI?
We'll help you define use cases, architect solutions, and deploy AI that creates actual business value.