Abundant — Code Evaluation & RL Data

We build 500+ coding tasks with a 30% frontier model pass rate, and the training environments to run them.

01 — SOURCE

Real experts. No AI slop.

Every expert in our network is identified and recruited directly by our founders. Each one is interviewed in person by a calibrated domain expert with real industry experience in their field — no AI screeners, no recent grads setting the bar.

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02 — BUILD

Task sample turnaround in under 1 day.

Every task is created by a domain expert in our network. We have 500+ tasks ready to deploy immediately, and we can scope and deliver 1,000 custom RL environments in 4 weeks.

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03 — VALIDATE

We reject 20–40% more tasks than standard pipelines.

Most vendors stop at human review. We add deterministic verifier testing and agentic trace validation — running every task in a code sandbox to confirm that failures reflect model capability, not task ambiguity.

"Abundant's format was the easiest for us to ingest. When we hit limitations with our training infrastructure, they quickly adapted."

Product LeadershipGoogle DeepMind

"Turned around high-quality data super fast — sometimes within 24 hours. Other vendors took months to finalize spec. Abundant did it in weeks."

Founder/CEOFirecrawl

"We showed Abundant's annotation platform to other teams, and everyone immediately wanted access. What started as a pilot for my team became the standard across our research org."

Research ScientistAdobe Research

We move faster than anyone else.

24h

Sample turnaroundFirst sample delivered within 24 hours

500

Tasks available todaySWE-bench + Terminal Bench, immediate delivery

100×

Yield vs. automated collection~10 PRs per repo automated → 100s per repo with Abundant

2 wks

To spec finalization"Other vendors took months to finalize spec. Abundant did so within weeks." — Data Ops Manager

We break our own tests before you do.

100%

of tasks pass human review, Oracle validation, and reward-hack stress testing before deliveryNothing leaves until it's verified end to end

30%

Frontier model pass rate calibrationCalibrated to be hard but solvable — not trivial, not impossible

1:8

Reviewer to task creator ratioDedicated QA on every task batch

Every

task is adversarially tested before delivery

We're the team you want to work with.

Jesse Hu

CEO

Ex-Waymo ML Engineer
TL for Data Quality & Evals
Co-author on Terminal Bench

Ke Huang

CTO

Google Assistant Eval Lead
GDM Trust & Safety
Brex Compliance & Ops Lead

Rishi Desai

Research Lead

Coding agents since 2023
Creator of SWE-Gen
Contributor to Terminal Bench

Meji Abidoye

Co-founder, Data Quality Lead

Ex-AWS Infrastructure Lead
Early contributor to Terminal Bench and Harbor