Useful intelligence, built to be trusted.
I design and build AI into real products and workflows: assistants, research systems, recommendations, content tools and multi-modal experiences. The model is only one part. Product judgement, grounded data, evaluation, cost control and clear human control are what turn a compelling demo into dependable software.
Discuss this kind of work
The important parts, held together.
AI-native product design
Shape the experience around what intelligence makes possible without making the product feel unpredictable.
Assistants and intelligent workflows
Conversational and background systems that can find information, use tools and move work forward with clear boundaries.
Evaluation and guardrails
Quality checks, permissions, audit and human confirmation designed in from the start rather than added after launch.
Production engineering
A maintainable product integrated with your data and existing systems, monitored for quality, latency and cost.
Bring me in while the important decisions are still movable.
A valuable workflow is trapped in manual work
People spend too much time finding, comparing, rewriting or moving information instead of applying judgement.
The prototype is impressive but unreliable
The demo proves possibility, yet no one can say how quality, permissions, cost or failure will behave in daily use.
The business knows AI matters, but not where
There are many plausible ideas and vendors, but no product view of which capability would create a durable advantage.
A defined job and clear boundary
A product shaped around the work intelligence should do, the decisions a person should keep and the evidence each needs.
A production capability
A useful experience integrated with the right data and tools, engineered for latency, failure, permissions and cost.
A way to earn trust over time
Evaluation, feedback and operational controls that make quality visible and allow the system to improve safely after launch.
One context. Short, informed loops.
The same senior practice runs through research, design and engineering. Each discipline sharpens the others, so decisions move quickly and the original intent does not disappear between departments.
Prove the useful unit
Choose one valuable slice of the workflow and test it with real inputs, real constraints and an explicit quality bar.
Engineer for uncertainty
Build evaluation, grounding, permissions, fallback and human confirmation into the product rather than around the demo.
Launch a learning system
Release with observability and feedback so quality, adoption and cost can guide the next iteration with evidence.
You work with the person doing the thinking and making the product. Trusted specialists join when the work genuinely needs them — never as layers between you and the decisions.