AI & Intelligent Workflows

What this looks like in practice

• Retrieval-augmented generation grounded in your own trusted data
• Evaluation harnesses and guardrails before anything reaches production
• Human-in-the-loop review for decisions that carry real consequences
• Model routing across providers, so no single vendor is a single point of failure
• Workflow automation embedded in the tools your team already uses
• Cost and latency tuning so AI features stay usable at real volume

How I approach it

AI is not the product strategy. The workflow is. The question I start with is never "where can we add AI" — it's what decision or task is slow, expensive, or error-prone today, and whether a model actually helps with that specific step.

Every system is built with an evaluation set from day one, so "it seems to work" is replaced with a number that goes up or down when something changes. Guardrails and human review sit wherever the cost of being wrong is high.

The result is judged the way any other production system is: by outcomes, not demos. If it doesn't hold up under real data, real load, and real edge cases, it isn't done.