Companies are shipping AI features. Users aren't adopting them.
The models work. The trust, governance, workflow, and ownership layers don't.
I close that gap, whether you're deploying AI into products or applying it to how your team sells and grows.
Bridging the gap between AI capability and user reality.
Product Intelligence
Designing the trust, decision, and workflow layers that move AI from pilot to daily adoption inside products and teams.
Growth Intelligence
Applying behavioral science to outreach, targeting, and sales systems so teams know what to automate and what to keep human.
Trust & Governance UX
Building transparent citation, fallback states, and clear decision boundaries for regulated environments.
Adoption Instrumentation
Measuring behavioral metrics like override rates and time-to-decision, not just API calls.
The Trust & Decision Framework
01
Efficiency
Streamlining cognitive load, not just click paths. Good workflow design anticipates operator needs and pre-loads context, reducing the time spent finding right information.
02
Trust
Designing transparent, governable feedback loops. System confidence requires explicitly explaining "why" a model made a recommendation, including sources, citations, and risk-levels.
03
Ease of Use
Making advanced capability feel like native intuition. Interface patterns should stay familiar, gently introducing new capabilities within workflows people already know.
04
Foundations
Creating resilient component systems for AI UI. Standardized design tokens ensure generative content is presented consistently, building structural reliability.
05
Cost
Measuring outcome impact, not just token usage. A successful implementation bridges the gap between infrastructure cost and business value generated by user action.
Latest Insights
One workflow. Real results. Low risk.
A focused sprint to design and implement one AI workflow in your existing stack. You see how the intelligence layer works before committing to a larger engagement.
View Pilot Details