Azure AI and AI application engineering
AI applications engineered like the rest of your software
AI application architecture on Microsoft Foundry and Azure OpenAI: retrieval, enterprise search, evaluation, security, observability and cost optimisation.
The challenge
Prototypes are easy; production AI needs retrieval that respects permissions, evaluation that catches regressions, security against prompt injection and cost that doesn't surprise finance.
What you get
- A governed AI platform teams can build on
- Retrieval grounded in your content with access control preserved
- Automated evaluation in the release pipeline
- Per-application visibility of usage and cost
Reference topology
AI application reference topology on Microsoft Foundry
Apps and business systems call an API Management AI gateway in front of the AI application, which uses Foundry models, Foundry Agent Service, Azure AI Search over indexed content, and Content Safety. Managed identity and Key Vault secure access; evaluations, tracing and red-team tests keep quality and cost visible.
What we deliver
- 01AI application architecture
- 02Model and service integration
- 03Knowledge retrieval and enterprise search
- 04Evaluation and testing
- 05AI security
- 06Deployment
- 07Observability
- 08Cost optimisation
Architecture layers
- Web and mobile apps
- Agents
- APIs
- Azure OpenAI in Foundry Models
- Other Foundry models
- Foundry Agent Service
- Search indexes
- Document processing
- Permission-aware retrieval
- Evaluation pipelines
- Tracing and monitoring
- Cost attribution
Technologies and availability
Azure OpenAI in Foundry Models
Generally availableOpenAI models sold directly by Azure through Microsoft Foundry. Model availability varies by region.
Official source (opens in a new tab)Microsoft Foundry Agent Service
Generally availableSome sub-features remain in public preview. The classic agents experience retires on 31 March 2027.
Official source (opens in a new tab)GitHub Copilot
Generally availableAI pair-programming across IDEs and GitHub; certification exam GH-300.
Official source (opens in a new tab)
Also used
- Google Cloud Vertex AI and Gemini (where you standardise on Google Cloud)
- Retrieval-augmented generation
- LLMOps
Availability checked against official vendor sources on 11 October 2026. Features, licensing and regional availability change — we confirm them for your tenant during assessment.
How we work
Stage 1
Architect
Reference architecture, landing zone and model choices.
Stage 2
Build
Retrieval, orchestration and application integration.
Stage 3
Evaluate and secure
Quality, safety and red-team testing; guardrails.
Stage 4
Deploy and optimise
CI/CD, monitoring and cost tuning.
Security and governance, built in
- Network isolation and managed identities
- Content safety and prompt-injection defences
- Evaluation gates before release
- Logging that supports audit without storing unnecessary personal data
Ways to engage
- Assessment
- A fixed-scope review with findings, priorities and a costed roadmap.
- Pilot
- A time-boxed build with agreed success criteria and a go/no-go decision.
- Delivery
- Full implementation by a Techcerv team working alongside yours.
- Managed
- Ongoing operation and improvement under a contracted service scope.
Talk to an AI expert
Tell us about your situation. You'll hear from a consultant — not an automated sequence.
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