Data Quality Audit
Independent data audits for reliability
Review datasets end-to-end to ensure trust and correctness.
Trusted by leading U.S. companies.
Contamination detection
We detect when your training or eval data overlaps with public benchmarks or your own test sets, to avoid inflated scores and hidden leakage.
Bias & diversity review
We analyze domains, languages, topics, and sources to surface skew, gaps, and overrepresented patterns that can distort training or evaluation.
Quality checks
We flag broken samples, inconsistent labels, missing fields, hallucinated content, and other issues that quietly degrade training quality.
Formatting validation
We normalize fields, enforce schemas, and fix structural issues so your datasets are ready for large-scale training without manual cleanup.
Source provenance checks
We help you understand where your data came 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+
“They caught contamination we never would've found alone.”
Lead Researcher at Meta
Frequently asked questions
Can you detect contamination?
Our network of 400,000 experienced engineers includes experts in every coding language that exists.
Review bias and diversity?
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.
Fix issues or only flag them?
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.
Audit internal evals?
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.
How long does an audit usually take?
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
Audit your data before you trust results.
