AI

What this capability covers

AI here means applied engineering: integrating AI and machine-learning services into products and workflows where they solve a defined problem, and building the controls that make the result operable.

This capability covers AI-enabled product features, model and service integration, intelligent workflows, engineering automation, retrieval and orchestration patterns where they suit the problem, and the evaluation and operational controls that sit around an AI-enabled system.

This capability focuses on applied AI engineering and integration rather than claims of proprietary foundation-model development. AI is treated as part of the system it runs inside, not as a category separate from it.

Typical engineering problems

• A promising prototype with no clear path into a production system.
• An AI-enabled feature with no defined way to tell whether its output is getting better or worse.
• Retrieval that returns plausible material rather than the right material.
• Cost, latency and failure behavior that were never designed for, and become visible only under real usage.
• Automation that is valuable when correct and expensive when wrong, with nothing in between.

Engineering approach

• Start from the system constraint. The useful question is which problem needs solving and what a wrong answer costs, not which model to adopt.
• Design the boundary. What an AI component is allowed to influence, and what stays under deterministic control, is an architectural decision.
• Evaluate deliberately. An AI-enabled feature needs a defined notion of acceptable output before it ships, not after a complaint.
• Plan for failure modes. Timeouts, degraded output and unavailable providers are normal operating conditions and are designed for.
• Keep it operable. Cost, latency and behavior need to be observable in production like any other part of the system.