Microsoft AI Foundry
Prototype-only AI apps
AI Foundry applications work in testing but lack a path to governed production use.
Microsoft AI
A platform for building AI is only useful if what you build survives production.
AI Foundry gives you the tools to build, evaluate and deploy AI apps on Azure. We help you use it the way an enterprise needs to: with proper evaluation before launch, content safety and data-handling controls, and monitoring after. The point isn't to ship an impressive demo; it's to ship something you can defend to security and operate for years.
Architecture
TLS 1.3 / mTLS
NIST Aligned
Zero Public Egress
AI Operating Challenges
Microsoft AI Foundry challenges we solve.
Moving AI from fragile prototypes to governed production requires solving data boundary security, token economics, and model auditability upfront.
No evaluation process
Model and prompt changes ship without a repeatable evaluation step.
Unmonitored model operations
Deployed models run without cost, latency or quality monitoring.
Solution overview
Microsoft AI Foundry designed for production readiness.
We help you use it the way an enterprise needs to: with proper evaluation before launch, content safety and data-handling controls, and monitoring after. The point isn't to ship an impressive demo; it's to ship something you can defend to security and operate for years.
AI app architecture
Design the Foundry project structure around your real use case, models and data sources.
Model deployment patterns
Deploy with private networking and identity controls appropriate for production.
Prompt flow operations
Manage prompt and model changes through a repeatable, versioned process.
Evaluation and monitoring
Evaluate quality before launch and track cost and performance with Azure Monitor after.
AI Architecture Blueprint
Governed AI pipeline architecture with strict security boundaries.
We establish isolated network boundaries, least-privilege tool access via MCP, identity enforcement, and comprehensive audit telemetry before launch.
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Understand the business goal, current environment, risks and constraints before any design work begins.
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Design the target operating model, controls and integration points so the solution fits how the business actually runs.
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Build and configure the solution using repeatable, reviewed engineering practices, not one-off manual work.
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Test against real scenarios and security requirements before anything reaches production.
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Hand over documentation, runbooks and support so the client team can run the result with confidence.
Delivery model
5 Phases
Milestone-Gated Delivery Model
Every phase ends with a documented deliverable and a clear go/no-go decision before the next one starts.
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Documented Deliverable & Formal Go/No-Go Decision Gate
No code or infrastructure moves to production without passing automated compliance checks and client review.
Zero Vendor Lock-In
Delivered into client Git & cloud accounts
Infrastructure-as-Code
Terraform / Bicep repeatable blueprints
Operational Runbooks
Knowledge transfer & team enablement
AI Engineering Methodology
Disciplined AI engineering from readiness assessment to production rollout.
Every milestone validates accuracy, latency, safety guardrails and regional data residency before widening organizational access.
Define the Foundry project
Scope the use case, models and data sources the project needs.
Architect for production
Design private networking, identity and evaluation before any model reaches real users.
Deploy and integrate
Connect the Foundry project to enterprise systems with monitored, governed access.
Evaluate and monitor
Track model quality, cost and performance, and iterate based on real usage.
Strategic Value
Measurable operational velocity and protected enterprise IP.
Equip enterprise workflows with verified intelligence without exposing sensitive enterprise data or falling out of regulatory compliance.
Production-ready AI apps
AI Foundry applications are architected for real deployment, not just demos.
Repeatable evaluation
Prompt flow and model changes go through a defined evaluation process.
Monitored model operations
Azure Monitor tracks cost, performance and quality after launch.
AI Stack
Enterprise AI models, orchestration & governance toolchain.
Industry-standard AI platforms engineered for predictable inference latency, high security, and regional data residency.
Microsoft AI Foundry
Azure OpenAI
Prompt Flow
Azure Monitor
Private networking
AI agents needed real tools. The security team needed to sleep at night.
AI agents got only the tools they need, through one gateway, with every call logged.
Read the story
AI FAQ
Key technical and governance questions before deploying AI.
Direct answers regarding model evaluation, security boundaries, and production readiness.
Why Foundry over rolling our own?
You get evaluation, safety and Azure-native integration out of the box, which shortens the safe path to production.
Can you integrate our own data?
Yes, with governed access patterns so the AI only sees what it should.
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