Full-stack engineering | AI products | Product strategy

I turn complex systems into useful products.

I work across frontend, backend, data, and design to build polished products for the people who use them—from understanding the problem through prototyping, deployment, measurement, and iteration.

Explore the workAbout my approach

Selected product and platform work

GOINVO WEBSITE interface preview

0105Full-Stack Product EngineeringProduction

GOINVO WEBSITE

Led the full-stack rebuild of GoInvo's live Next.js and Sanity platform, translating editor feedback and business priorities into self-serve publishing, reliable releases, and first-party experiment measurement.

  • Editor workflows
  • Production architecture
  • Measured experiments
View case study
WRITROSPECT interface preview

0205Full-Stack AI Product EngineeringHosted baseline · AI flow validated locally

WRITROSPECT

Built an AI planning prototype where the system proposes options, users choose the path, and approved actions flow through typed APIs into durable, reviewable changes.

  • Reviewable model actions
  • UI/API/data boundaries
  • Deterministic evaluation
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TREECHI interface preview

0305AI Systems + Developer ToolsPrivate alpha · Windows

TREECHI

Designed a desktop developer platform that runs graph-based C++ workflows, records why changes were made, and couples AI-assisted work with observable state, performance measurements, and release gates.

  • Observable system state
  • Human-agent provenance
  • Fail-closed verification
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RENDOMAT interface preview

0405Full-Stack Workflow AutomationWorking internal system

RENDOMAT

Built an internal React and TypeScript production tool that turns structured content into reusable multi-format video, with scene-level caching, local and cloud render paths, and automated visual QA.

  • React component system
  • Workflow automation
  • Artifact-level visual QA
View case study
CANIS interface preview

0505Data Platform + AI EvaluationArchitecture prototype

CANIS

Built a Rust and WebAssembly evidence platform that keeps source data, human review, and AI evaluation traceable from ingestion through publication—piloted on biological data.

  • Graph architecture
  • Content-addressed provenance
  • Fail-closed gates
View case study

Other work

About

I connect strategy to execution.

Product engineering across boundaries

I do my best work between an ambiguous product problem and a dependable product. I work with the people closest to the workflow, frame a solution, and own implementation across interface, backend, data, and deployment. In interdisciplinary teams, I document decisions, invite feedback, and leave systems easier to understand and maintain. I use AI as an engineering tool while retaining ownership of architecture, review, testing, and outcomes.

Core strengths

  1. 01

    Product and user context

    I listen to the people closest to a workflow, reconcile different personas and constraints, and define what useful success looks like.

  2. 02

    Full-stack product engineering

    I connect React and TypeScript interfaces to APIs, content and data models, automation, deployment, and the supporting systems around them.

  3. 03

    Interpretable AI and data

    I make model behavior and complex data inspectable through provenance, evaluation, visualization, review, and explicit failure states.

  4. 04

    Production ownership

    I carry work through performance, documentation, release checks, observability, and production feedback so the next decision is grounded in evidence.

Contact

Let's make a hard system genuinely useful.

I'm interested in full-stack product work where complex data, AI behavior, and real-world constraints have to become a clear, dependable tool. If you have an ambiguous problem that needs a thoughtful path to production, I'd love to hear about it.