Embedded finance · Published work
AI and consequential financial systems
A Helix by Q2 article on generative AI in embedded finance, published with my role and author biography.
Read the archive and source noteAbout
Engineering depth, operating judgment, and a record you can inspect.
I’m Eric Rhea, a principal engineer and technical leader. My work spans financial platforms, applied AI, and the practices that help teams make complex systems reliable and ready to change.
The work
Make complex systems legible, reliable, and ready to change.
That is the through-line in how I work. I turn ambiguity into decisions and interfaces, reduce the places where teams can be surprised, and make standards teachable so quality does not depend on one person being in every room.
My public record includes a 2023 Helix by Q2 author record naming me as Principal Engineer and an original engineer behind CorePro, an early banking-as-a-service platform that became Helix. My Leadership OS describes the same arc in my own words: founding and principal engineering work on complex financial systems, paired with a responsibility to grow the judgment around those systems.
Selected evidence
Claims should lead somewhere you can inspect.
Leadership · Public briefing
Turning AI pilots into durable capability
A DMACC leadership briefing about decisions, governance, and cross-functional value beyond the first demonstration.
Review the talk summaryCommunity · Build receipts
Learning through public competition
Spring Into AI and Summer Into AI preserve prompts, rules, submissions, and lessons from builders shipping in public.
Explore Spring Into AI Explore Summer Into AIResearch · Working systems
Experiments with the receipts left in
Public work across agent systems, small-model training, local inference, generated worlds, and software reliability.
Browse research and experimentsHow I operate
Clarity is an engineering control.
Over more than a decade, my working record accumulated across decisions, architecture notes, reviews, escalations, and postmortems. When I examined that record for recurring behavior, clarity was the invariant: clear goals, interfaces, ownership, next steps, and standards.
I publish the resulting Leadership OS because operating principles are more useful when other people can inspect, challenge, and apply them.
- Make the consequential decision explicit.
- Design interfaces that expose ownership and failure.
- Prefer evidence and working artifacts to performance.
- Raise team judgment so leadership scales beyond a bottleneck.
Current practice
A continuing practice across systems and applied AI.
The technology changes, but the practice stays consistent: understand the real problem, make important decisions clear, build something people can operate, and leave the system easier to change.
If you’re working through a difficult technical question or need a practical briefing for your team, start a conversation.