Establish context
Capture the user problem, current product state, constraints, decisions, and the evidence required for completion.
Context artifactsProduct briefs · architecture notes · decision records · acceptance criteria
I lead AI products from problem framing to production—connecting product strategy, system architecture, and human oversight. Over the past decade, I’ve led product development across AI SaaS, live streaming, digital twins, community platforms, and high-scale operations.
AI infrastructure, decision systems, and operational tooling for complex workflows.
Live AI application workspace that turns a person’s real experience and evidence into reusable application materials. The Job Application workspace is available now, with further application workflows in development.
Built around evidence-grounded drafting rather than generic text generation: users can structure their experience once, tailor it to opportunities, review each output, and reuse the underlying material across applications.
Visit project Read product brief →Civic-process platform in development for making grants, laws, permits, and related public decisions visible, auditable, and easier to follow.
The work brings requirements, evidence, and process records into one accountable pathway—supporting traceable decision-making, protected AI assistance, and audit-ready outputs for citizens, municipal teams, and oversight bodies.
Visit project Read product brief →Live AI research workspace for exploring Swedenborg’s writings with source transparency. It combines corpus-grounded retrieval, structured citations, and research tools that keep the original passages visible alongside AI-assisted answers.
Built to make a dense, interconnected body of work more navigable without replacing careful reading or hiding the evidence behind a generated response.
Open the app Read product brief →A reliable AI workflow is not a list of models. Product intent is captured in durable context, reusable instructions, bounded execution, and verifiable evidence—so work can continue across agents and sessions without resetting the plan.
The files hold the durable memory—not the model. Models and tools can change while product intent, operating rules, and evidence remain available.
Product, platform, and consulting roles across streaming, digital twins, spatial experiences, and applied AI. Overlapping engagements are shown as they occurred.
Head of Product
Led the product lifecycle from discovery to scaling for an AI-powered event platform, launching an MVP that reached 900+ active users within two months.
Managing Partner
Advised event- and platform-technology clients on product engineering, digital transformation, technical solution architecture, and implementation planning.
Product Owner
Owned backlog and roadmap for a digital-twin SaaS platform connecting CAD and machine data with business priorities and customer needs.
Product Manager
Coordinated distributed engineering, design, and marketing teams building a Unity-based 3D content platform for real-time brand experiences.
Managing Partner
Built and scaled a live-streaming and social-media platform for B2B and B2C events, spanning product strategy, monetization, operations, and partnerships.
Business Development Manager
Supported product roadmap and delivery for ultra-low-latency live-streaming infrastructure, cloud services, and SDK solutions for enterprise clients.
Decision-critical systems fail when they hide their logic. Explainability and operational transparency aren't optional — they're first-class product requirements.
Users and businesses don't care about the architecture. They care about consistency, auditability, and predictable failure modes. The best AI systems feel boring to use.
AI in regulated or high-stakes environments isn't automation — it's tooling. The interface between AI output and human decision-making is where product strategy lives.
Model performance matters less than data pipelines, evaluation systems, and operational feedback loops. Sustainable AI products are built, not found.
Matthew brought structure and stability to technically complex environments while keeping teams aligned under real operational pressure.
Matthew combines strategic product thinking with strong technical execution and consistently delivered solutions that improved engagement at scale.
Matthew blends systems thinking with hands-on execution. He communicates clearly across technical and non-technical teams, anticipates failure points, and keeps live operations calm.
Tell me what you are working on, where the challenge sits, and what a useful next step would look like.
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