About
Hi, I'm Riley Moynihan.
I founded Cygnus Engineering to help teams build AI and data systems that work in production—whether that means turning a rough idea into a working prototype, or scaling a demo to handle real traffic and edge cases.
I build for real-world conditions: messy data, production load, and the inevitable surprises that come with both.
Riley Moynihan
Background
Master's degree from the University of Texas. Over five years in industry building data pipelines, retrieval systems, and AI/RAG products that serve production traffic. I was building with LLMs before ChatGPT, leading early initiatives that balanced reliability, safety, and cost—back when there was no ecosystem to fall back on.
I've worked with venture-backed startups and established teams, helping them ship systems that handle real-world edge cases, scale under load, and stay within budget.
Principles
- • Ship thin slices fast. Don't delay launch for "just one more feature"—you're guessing until real users touch it. Instrument, ship, and let usage data guide what to build next.
- • Fast iterations are worthless if you can't measure whether they made things better or worse. Observability and eval loops aren't optional—they're how you know you're not just rearranging deck chairs.
- • Keep latency, cost, and user experience in balance. The cheapest model that solves your problem is the right model.
How I work
- • I embed with your team—pair programming, code reviews, and decision-making—not weekly status calls.
- • Discovery to align on outcomes and constraints, then architecture and delivery plans that prioritize quick wins.
- • Build, evaluate, and iterate with measurable metrics. Every release includes instrumentation, tests, and rollback plans.
- • You get documented code, runbooks, and a trained team—not a dependency on me.