What your lab gets
Environments
Sandboxed environments that mirror the software and workflows you want agents to handle.
Task sets
Tasks written for each environment, with a clear goal and a clear definition of done.
Checkers
Automated checks that score an attempt, so rewards come from the result, not from opinion.
Upkeep
Fixes when tasks turn out ambiguous or checkers let wrong answers through.
Kinds of environment work
Built to your spec and run inside your own infrastructure.
Software environments
Codebases, services and tools an agent works in to complete engineering tasks.
- repo
- tools
- tests
Workflow environments
Simulated business software where an agent has to finish a multi-step job.
- state
- actions
- goal
Task authoring
Goals of graded difficulty, each with what counts as success.
- goal
- difficulty
- success rule
Reward checkers
Code that inspects the end state and returns a score or pass/fail.
- verifier
- score
- logs
Checker audits
Attempts to break a checker, so agents cannot pass without doing the work.
- edge cases
- exploits
- fixes
Reference runs
Worked solutions that show a task is possible and the checker accepts it.
- solution
- trace
- result
Who does the work
Engineers from a software engineering company that has shipped 377 product builds across 19 sectors, so the environments behave like real software.
- Software engineers used to building and testing production systems.
- Engineers who think about how a checker can be fooled, not only how a task can be solved.
- Matched to the stack your environments need. We confirm coverage when we scope the work.
Four checks before work counts.
The same process runs on every OceanLab Labs project, and your QA always has the final say.
- 1
Scored trial task
Every person completes a scored trial task before they join your project, so only people who meet the bar work on your data.
- 2
Second reviewer
Submitted work is checked by a second reviewer before it reaches you.
- 3
Lead calibration
A lead runs calibration rounds so everyone applies your guidelines the same way as the work goes on.
- 4
Your QA decides
Your own QA has the final say on what is accepted. Anything you reject comes back to us and is fixed.
Your data stays yours.
NDA for every person
Everyone who works on your project signs an NDA before they see any of your material.
Your tools, your accounts
Work happens only inside your own tools and accounts. Your data does not leave them.
A dedicated team
The people on your work do not work for a competing lab at the same time.
Start small, then scale.
- 1
Tell us what you need
Share the work, the skills it needs, your guidelines or rubric, and your timeline. We match people to those requirements.
- 2
Paid pilot, small team
A small team works on your real tasks first, so you can judge the output before you commit to more.
- 3
Scale up
When the pilot meets your bar, we add people to the same work and keep it running for as long as you need.
Pricing is scoped per project, usually after a short paid pilot. See how it works.
Do the environments run on our infrastructure?
Yes. The work happens only inside your own tools and accounts, and your data does not leave them.
Do you write the checkers as well as the tasks?
Yes. Each task comes with an automated check that decides whether an attempt succeeded, and we test that the check cannot be passed without doing the work.
How do you check quality?
Each person passes a scored trial task before joining, a second reviewer checks submitted work, a lead runs calibration rounds, and your own QA has the final say. Anything you reject is fixed.
How is the work priced?
Pricing is scoped per project, usually after a short paid pilot on your real tasks. There is no public rate card.
How do you keep our data and tasks private?
Everyone on your project signs an NDA, the work happens only inside your own tools and accounts, and the team does not work for a competing lab at the same time.
Related pages.
- OceanLab Labs overview Everything OceanLab Labs does for AI labs, on one page.
- Coding dataCoding tasks, reference solutions, tests and reviews of model-written code.
- Evals and benchmarksEvaluation sets and benchmarks designed, written and graded to your spec.
- Human feedbackRLHF and SFT data: rating, ranking, writing and correcting model outputs.
- Domain expertsSpecialists who write and check tasks where subject knowledge decides the answer.
- How it worksFrom request to paid pilot to scale: quality, security, pricing and time zones.
Looking to automate your company's workflows with AI instead? See AI automation.
