Auditing someone else’s output, the highest-trust way to start
A review and audit layer operated over a third party’s delivery: the client’s own crowd, or another vendor. We publish the metrics either way, including the ones that are inconvenient.
A documented program shape with illustrative volumes, not a named client reference.
The problem
The buyer had no reliable read on the quality they were paying for. Vendor-reported metrics were self-graded, and internal researcher time was being consumed by spot-checking.
How it runs
- 1
Define the bar in writing
A defect taxonomy and acceptance definition agreed up front, so a finding is a fact rather than an argument.
- 2
Sample with intent
Stratified sampling across task type and contributor, weighted toward the categories where defects are most expensive.
- 3
Report without editing
Findings go to the buyer unedited, with the evidence trail. We do not soften an audit to protect a commercial relationship, including our own.
Outcome
- Visibility
- Per-batch
- Quality becomes a measured input
- Researcher time
- Returned
- Spot-checking moves off the research team
- Taxonomy
- Shared
- Findings feed rubric corrections upstream
What makes it stick
Once the quality benchmark inside an account is ours, the delivery conversation follows. That is deliberate sequencing, and we say so.
Other patterns
A 1,000-item coding evaluation, delivered a day early with the audit attached
The standard six-week play: pilot scoped against the client’s own acceptance bar, pod calibrated to an agreement gate before production, batch delivered with full QA evidence, converted to a dedicated pod inside two weeks.
ReadAgent BenchmarkingTerminal-agent benchmark built end to end, from task taxonomy to grading pipeline
Agentic evaluation is where crowd models break. Task authoring, Dockerised environment engineering, verified gold solutions and step-level trajectory grading, run as one accountable workstream.
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