We built the tool we wished existed at our last company.
In 2021, James Nakamura was the engineering enablement lead at a mid-size fintech company in Los Angeles — 120 engineers, a legacy payments codebase, and an L&D program that ran a skill survey every six months. The survey results looked reasonable. The incident data told a different story. Three P1s in one quarter traced back to the same distributed systems failure pattern — one that the engineers involved rated themselves as competent in, because they'd never encountered that specific failure mode before.
He started pulling PR review comments and Jira ticket escalation chains manually, mapping where knowledge was concentrated and where it was missing. The gaps in that map didn't match the survey at all. They matched the incident log exactly. He spent six months trying to build internal tooling to automate that analysis. In early 2023, he left to build it properly.
That's Tunlai: a path engine that reads the data your engineering organization is already producing — PR patterns, incident involvement, ticket ownership — and surfaces the skill gaps that surveys consistently miss.
Founded 2023 — built in Los Angeles, California by a team that came out of engineering enablement and distributed systems.
Bootstrapped — we grow by building something engineering teams actually want to use, not by chasing a funding round.
Los Angeles, CA — 11150 Santa Monica Blvd, Suite 1550, Los Angeles, CA 90025.
Not a content library. We don't sell courses. We surface which skills to build and in what order — using your codebase, not a generic catalog.
Surveys lie. Codebases don't.
Our mission is to make evidence-based upskilling the default for engineering organizations — not the exception. Self-reported skill data is systematically biased toward confidence in familiar domains and away from the gaps engineers haven't yet encountered. Engineering workflow data records those encounters directly.
We believe L&D managers and Engineering VPs should be working from the same underlying data. Not surveys on one side and incident postmortems on the other. A single shared picture of skill coverage, risk areas, and where upskilling investment actually closes gaps.
Tunlai is not a course platform. We don't replace the content your engineers already use. We provide the competency framework and skill gap analysis that tells you what to focus on — so the content you have gets directed at the problems that matter.
The team
Five people. Engineering and enablement backgrounds on both sides. We've run the upskilling programs from the inside and built the infrastructure that makes them measurable.
James Nakamura
Co-Founder & CEO
Previously engineering enablement lead at a 120-person fintech org. Spent 2021–2022 manually mapping PR and incident data to skill gaps before building Tunlai to automate it.
Priya Mehta
CTO
Graph algorithms and distributed data systems. Designed the Competency Graph engine and the inference model that maps workflow signals to skill nodes. Holds strong opinions about data minimization in enterprise tooling.
Carlos Reyes
Head of Engineering
Platform infrastructure and integration reliability. Designed the read-only OAuth scope architecture and the per-tenant data isolation layer. Came from platform reliability at a logistics tech company.