Data Pipeline Management
Run large human-data pipelines without slowing down your research
Design and run the workflows behind your datasets on your infra or ours.
Trusted by leading U.S. companies.
Workflow design
We map the steps, dependencies, edge cases, and validation criteria for your data workflows so they can scale without losing structure or control.
Environment setup
We configure repos, Docker images, scripts, tools, and test harnesses so your workflows run in stable, repeatable environments with minimal friction.
RL-Gym–style environments
We build step-based environments for agents to act, receive feedback, and learn across sequences, useful for RL and agentic evaluations.
Quality assurance
We run calibration rounds, spot checks, and agreement tracking to keep quality stable as volume and task complexity grow.
Infra-agnostic execution
We help you understand where your data We can run and monitor workflows entirely on your infra, on ours, or in a hybrid setup, depending on security and operational needs. from, how it was built, and whether it is safe and appropriate to use in training or evals.
Case study
A hyperscaler building a coding agent needed full-trace data to boost performance on benchmarks like SWE-bench. We built a custom workflow, brought in elite engineers with niche repo expertise, and delivered rapid, measurable gains beyond expectations.
Collection velocity
10x
Reduction in AHT
48%
Quality score improvement
20%
Experts activated
210+
“Their pipeline let us scale a month of research into a week.”
Lead Researcher at Meta
Frequently asked questions
Can you run on our infra?
Our network of 400,000 experienced engineers includes experts in every coding language that exists.
How fast can you scale?
Our technology allows us to scale up and down extremely quickly. We can typically spin up new evaluation capacity within 48 hours, and can onboard hundreds of new developers to your project every week.
Do you support RL-Gym style workflows?
As much or as little as you want. Some clients are hands-off and just want the data, others want to deeply collaborate on evaluation criteria. We're flexible — though we do recommend initial calibration sessions to align on quality standards.
How do you ensure quality?
Data quality is the most important issue that we focus on. To have a detailed conversation about our processes and systems we use to ensure high-quality data, get in touch with our team.
Do you set up environments end to end?
Yes! Most clients start with a pilot project — usually a few hundred evaluations across different problem types. This lets us calibrate our process to your needs and demonstrate our quality before scaling
Scale human-data operations without slowing research.
