AI application platforms

A successful AI prototype can still lack the controls needed for a dependable service. Establish a deployable platform for models, knowledge, and tools with explicit evaluation, release, monitoring, and cost responsibilities before production use.

Capabilities

Select hosting and supported model options against quality, latency, region, and cost requirements.
Design identity, secrets handling, knowledge access, and approved Model Context Protocol integrations.
Define orchestration boundaries, workflow handoffs, retries, and human escalation paths.
Create representative evaluations and release gates for quality, access controls, and failure behavior.
Use GitHub Copilot for development assistance with human code review and tests.
Plan budgets, monitoring, recovery, version changes, and accountable support.

Deliverables

A reference architecture with hosting choices, trust boundaries, and documented tradeoffs.
A scoped platform foundation with environment configuration and repeatable deployment steps.
An evaluation and release plan with test datasets, acceptance thresholds, and rollback criteria.
An operations runbook covering monitoring, cost alerts, incidents, and model or application changes.

Business value

Make the transition from prototype to operation a deliberate engineering decision. Baseline task quality, security findings, latency, availability objectives, and cost per useful task; release only when agreed readiness criteria are met.

Start with a use case and a production-readiness review.

Bring a representative application, data and model requirements, intended integrations, and operating constraints. Agree the platform boundary and pilot workload; production readiness remains separate from prototype performance.

Foundry, Azure, Entra, GitHub, GitHub Copilot