Turning business problems into useful AI and product experiences.

Customer + Market Discovery

Founder experience taught me to start with user friction, market timing, and activation moments before deciding what to build.

Data-Informed Prioritization

Finance, regulatory, and product analytics work shaped how I evaluate tradeoffs, risk, adoption signals, and measurable business outcomes.

AI Prototyping + Execution

I build in my free time to test AI workflows, UX patterns, and technical feasibility. ChatVET is live with users and shows how I move from concept to usable product.

Cross-Functional Delivery

Experience across finance, operations, product, and engineering-adjacent systems helps me communicate clearly with business and technical teams.

Outcomes Over Output

I define success in terms of user and business outcomes, not shipped features. Every roadmap item should trace back to a measurable change in adoption, retention, revenue, or operational leverage.

Judgment in Ambiguity

Regulated finance work and founder experience both demand decisions with incomplete information. I frame the risk, pick a reversible path, set a checkpoint, and move, rather than waiting for certainty that never comes.

What I'm Learning Now

• How to make LLM outputs trustworthy: grounding responses in reliable sources, reducing hallucination, and knowing when a model should say "I don't know."
• Evaluating AI quality systematically instead of by gut feel, so improvements are measurable and regressions get caught.
• Agentic and multi-step AI workflows, and where they genuinely beat a single well-designed prompt.
• The economics of AI products: cost, latency, and quality tradeoffs that decide whether a feature is viable at scale.

How I Learn

• Build first: every concept I read about gets tested in a working project before I consider it learned.
• Ship to real users: live products expose the gaps that tutorials and demos hide.
• Use AI to build AI: modern AI-assisted development tools are part of my daily workflow, which compounds how fast I can learn and iterate.

AI Product Application

• Design AI-assisted user flows that reduce search friction and speed up first-value moments.
• Use recommendation loops to improve retention, repeat usage, and conversion quality.
• Translate business goals into practical AI features aligned with measurable outcomes.

AI + Business Execution

• Combine finance and analytics discipline with AI workflows for operational decision support.
• Prioritize scalable implementations with low operational burden and clear feedback loops.
• Focus on production-minded AI use cases that improve leverage without unnecessary complexity.

Builder Mindset

• Build in my free time to learn LLMs hands-on: prompting, retrieval, evaluation, AI UX, and technical tradeoffs.
• Use side projects as a proving ground for ideas, workflows, and product thinking.
• Launched ChatVET with users: an AI clinical assistant that helps veterinary professionals get trusted, sourced answers fast.

Design + UX Goals

• Reduced cognitive load with simple navigation and clear CTAs.
• Improved activation speed by guiding users to first domain actions quickly.
• Built progressive disclosure so advanced users could go deeper without clutter.

Growth + Retention Outcomes

• Scaled to 1,300 MAU across 10+ countries.
• Improved return usage with Clio, an AI domain discovery tool across blockchain networks.
• Combined product analytics with iteration to increase engagement and adoption.