Me
Sean Koval
I use data and AI to tackle complex problems and push toward more general, adaptive systems. In practice, that means building the full ecosystem — models, agents, tools, and evaluation — with a focus on client-facing services in finance and healthcare, where better systems can materially change outcomes.
I love building. Whether it's agents, full-stack applications, data infrastructure, or developer tools — I'm happiest when shipping something end-to-end. My projects span Python, TypeScript, and Rust, from weekend prototypes to production systems.
What I do
- ML Product
- Architecture, experimentation, rollout, monitoring. Most of the work happens after the model is already good enough.
- LLM Systems
- Rubrics, regression suites, human review in the loop. If you can't catch a regression, you don't have a system — you have a demo.
- Full-Stack
- APIs, frontends, infrastructure. Python and TypeScript mostly; Rust when latency is the requirement.
- Data Platforms
- Retrieval, semantic registries, dashboards, automation. The goal is a question answered in seconds instead of a ticket filed.
Experience
Led a team of 2, architected RAG + agent features, built LLMOps evaluation pipelines, and shipped full-stack AI products.
Built real-time topic detection, analytics dashboards, and audio classification pipelines.
Built quant trading tools, data pipelines in Rust, and crypto market research systems.
Latest writing
View all →Private Equity's AI Problem Is Not a Model Problem
Why LLM rollouts at investment firms stall out at search, what the four workflows actually demand, and the products that should exist but don't.
GitHub Pulse: Week of March 1, 2026
Agents learned to work in teams, SpacetimeDB collapsed the stack, and WiFi replaced cameras for human sensing.