Discovery & Requirements
Define model goal, task, data, edge cases, volumes, formats, risks and acceptance.
We eliminate black-box labeling with structured milestone gates. From initial taxonomy alignment and gold-set calibration to multi-pass SME review and statistical acceptance testing, every delivery is fully auditable and backed by agreed precision SLAs.
Define model goal, task, data, edge cases, volumes, formats, risks and acceptance.
Review source quality, rights, coverage, balance, metadata and preparation needs.
Create label definitions, examples, exclusions, ambiguity rules and escalation paths.
Qualify contributors and align decisions through feedback and reviewed examples.
Test representative samples before committing to production volume.
Run controlled batches with allocation, issue tracking and guideline change control.
Execute multi-pass validation using dual-blind review, statistical consensus algorithms, and senior SME adjudication to systematically eliminate human error.
Check completeness, schema, consistency, leakage, balance and acceptance results.
Deliver model-ready datasets via encrypted transfer protocols (S3, GCS, secure API) accompanied by checksum integrity logs, schema documentation, and audit certificates.
Incorporate downstream model inference errors, benchmark shifts, and edge cases back into living guidelines to drive continuous pipeline improvement.
Share your dataset requirements, target volume, and acceptance criteria. Our engineering team will review your specifications and structure a custom delivery plan.