Product Leader.Founder.Builder.
I turn ambiguous problems into real software — designing AI workflows, marketplaces, and recruiting technology that compound over time.
Focus areas
What I've Built
The products, the problems they solve, and the decisions behind them.
Ryger
Founder · Case StudyA recruiter-owned talent network, discovered one workflow problem at a time.
Ryger didn't start as a recruiting product. It started as a question I kept running into during my own job search — and every answer changed what it should be. What follows is the sequence of discoveries that turned a matching tool into a network.
The discovery arc
- 01Initial Observation
- 02Research
- 03Workflow Discovery
- 04AI Trust Discovery
- 05Recruiting Nuance Discovery
- 06Marketplace Discovery
- 07Network Discovery
Lesson Plan
BetaAn end-to-end teaching workflow platform that orchestrates AI from lesson planning through Google Classroom delivery.
- Problem
- Teaching isn't a sequence of blank pages — it's a workflow. A lesson has to align to authoritative standards, hold up under review, adapt to the class in front of you, carry its own materials, land on a specific day, and reach students through the tools a school already runs on. Most AI education tools generate a lesson and stop there, dropping a plausible document into the middle of a workflow they don't understand.
- Why I built it
- The workflow became the product. Lesson generation is one capability inside a larger system that orchestrates AI across the full arc of teaching. A lesson is grounded in authoritative multi-state standards through retrieval rather than recollection, reviewed and evaluated against those standards, adapted for a shortened block or an ELL group or a substitute without losing its objective, extended into quizzes, worksheets, and exit tickets as reusable assets, scheduled into real classes, and delivered into Google Classroom as Docs, Forms, or live links. One orchestration layer coordinates retrieval, generation, review, adaptation, evaluation, material creation, scheduling, and publishing — and every AI write is previewed and teacher-approved before it counts. The teacher directs the workflow; AI runs the stages.
- Lesson
- The engineering that matters lives in the coordination: hybrid standards retrieval that filters, ranks by vector similarity, then reranks; immutable, trigger-written version history that makes every change reversible and attributable; copy-on-write scheduled instances that let one lesson run in many classes without collisions; cost- and stakes-based model routing; and destination-abstracted publishing to Google Classroom that stays idempotent. AI earns its place by being orchestrated into real work and keeping the teacher accountable — not by making decisions on its own.
Architecture
- Stack
- Next.js 16React 19TypeScriptSupabase / PostgresVercel
- Data & standards
- pgvectorMulti-state standards (NJ · CA · NY · OH · PA)Authoritative ingestImmutable trigger-based versioning
- AI orchestration
- One orchestration layer across all AI stagesModel-agnostic routing via AI GatewayCost / stakes-based model routingRetrieval where correctness mattersPreview-first, teacher-approved writes
- Google Classroom
- OAuth with encrypted tokensPersistent period → course mappingPublishing as Docs · Forms · live linksIdempotent re-publish
- Key Systems
- Hybrid standards retrieval (filter + vector + rerank)AI orchestration across stagesReusable instructional assetsCopy-on-write scheduled instancesLesson review & evaluationInstructional material generationScheduling into classesDestination-abstracted publishingDuplicate-publication prevention
Resume
Resumes grounded in your whole career, not just one role
- Problem
- Resumes are built for a single role, so experience that doesn't match the current application quietly gets cut. Most tools make it worse — optimizing for keywords, or inventing accomplishments that no longer sound like you.
- Why I built it
- Built to tell a more complete career story. Resume pairs a traditional resume with a deeper career narrative, then reads a job description against both — surfacing the most relevant real experience first, always grounded in your actual work history.
- Lesson
- Trust is the product. Every resume has to stay truthful, free of invented metrics, and still sound like the person behind it — even though a model does the drafting.
Architecture
- Frontend
- Next.jsTypeScriptTailwindVercel
- Platform
- AWS LambdaAPI GatewayDynamoDBS3SES
- AI
- OpenAI
- Identity
- Custom authentication
- Key Systems
- Career story engineResume generation workflowsGrounded content generationJob alignment workflowsTruth-preservation controls
ReadMyStrip
AI-Powered Water Test Strip Reader for iPhone
- Problem
- Testing pool, spa, or aquarium water means reading a strip of subtle color pads against a reference chart — slow, subjective, and easy to get wrong. Results shift with lighting, eyesight, and guesswork, right when accuracy matters most.
- Why I built it
- ReadMyStrip turns that into a single photo. I led the product from concept through production release — product strategy, UX, AI workflow design, engineering, backend architecture, and App Store launch — so anyone can point their iPhone at a used test strip and get reliable, interpreted results in seconds.
- Lesson
- Reliability is the product. A technically complex image-analysis workflow has to land as one trustworthy tap — onboarding, edge cases, subscriptions, and Apple's review process all handled so the user never has to think about them.
Architecture
- App
- ExpoReact NativeTypeScriptiOS
- AI
- Image analysis workflowTest strip interpretation
- Platform
- AuthenticationBackend APIsCloud storage
- Payments
- RevenueCatSubscription management
- Key Systems
- Single-photo capture flowAI water-chemistry interpretationEnd-to-end onboarding to resultsSubscription managementApp Store production release
Principles over playbooks.
A consistent way of thinking about product problems — the lens I bring before any specific tool or feature enters the conversation.
Start with the workflow, not the technology.
Understand how the work actually gets done before deciding what to build. The best technology disappears into a better workflow.
Optimize for outcomes, not activity.
Shipping features is easy. Moving the metric that matters is the job. I measure progress by the problem getting smaller.
Reduce friction before adding features.
Most products are slowed by friction, not missing capability. Removing steps usually beats adding them.
Build systems that compound over time.
Favor decisions that get more valuable as they accumulate — data, relationships, and structure that pay off later.
Make AI useful, explainable, and practical.
AI should earn trust by being transparent and reliable in real work — not impressive in a demo and fragile in production.
Why I approach problems the way I do.
Over the last 15+ years I've worked across customer service, team leadership, process improvement, process management, enterprise risk, data governance, and product management — inside banking, risk, operations, and product organizations.
The same problems kept recurring: workflow design, operational efficiency, stakeholder alignment, governance, and reducing friction in complex processes. Many of the themes in my projects — trust, reducing context switching, preserving valuable work, and building systems that scale — originated long before I started building software.
Career highlights
Capital One
Product Management & Enterprise Risk
Built and managed products supporting governance, compliance, risk management, enterprise data workflows, and operational decision-making — focused on product strategy, roadmap development, stakeholder alignment, and large-scale enterprise initiatives.
PNC Bank
Process Improvement Consulting
Partnered with business leaders and operational teams to identify inefficiencies, improve workflows, coach teams, and drive continuous-improvement initiatives.
TD Bank
Process Management & Operational Leadership
Progressed from frontline leadership into business process management and enterprise process improvement — leading large-scale process optimization, data analysis, operational redesign, and organizational improvement programs.
The projects demonstrate what I build. My experience explains why I think about problems the way I do.
Building something interesting?
I'm always glad to trade notes on products, marketplaces, and making AI genuinely useful. The best way to reach me is below.