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.
Discuss Microsoft AI Foundry
Architecture
flowchart LR
ID[Entra ID] -. auth .-> APP
subgraph VNET[Private VNet - no public egress]
APP[Your App] --> PE[Private Endpoint]
end
PE --> AOAI[Azure OpenAI]
AOAI --> DATA[Your Data - RAG]
MON[Cost and Usage Monitor] -. watches .-> AOAI
Business challenges
Microsoft AI Foundry challenges we solve.
Every recommendation starts with business pressure, technical risk and the operating model required after launch.
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.
Architecture model
A practical delivery architecture before implementation begins.
We define the target operating model, controls, integration points and ownership path before building, so the solution can be supported after launch.
CloudevTech
Enterprise delivery model
01
Discover
02
Architect
03
Implement
04
Validate
05
Operate
Our approach
Structured delivery from discovery to operational handover.
Every engagement is shaped around the service goal, current constraints and the operating model your team needs after launch.
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.
Business benefits
Outcomes designed for decision makers and delivery teams.
Benefits are framed around measurable improvement, operating confidence and reduced delivery risk.
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.
Technology stack
Implemented with proven platforms and tools.
Technology choices are confirmed during discovery, with a preference for reliable, maintainable platforms your team can support.
Microsoft AI Foundry
Azure OpenAI
Prompt Flow
Azure Monitor
Private networking
Enterprise AI agents needed access to production tools. Security needed to sleep at night.
Controlled AI-agent tool access with least-privilege scoping, mediated APIs and BigQuery audit logging.
Read the story ->
FAQ
Common questions before engagement.
Short answers to common planning questions for Microsoft AI Foundry.
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.
Enterprise consultation
Book a consultation