Data Collection & Validation

Growing ML programs need more than one-off labeling batches — they need reliable collection, validation, and refresh cycles. Our data collection and validation services combine human-in-the-loop gathering, golden-set benchmarking, and multi-tier QA so your training data stays accurate as models and taxonomies evolve.

Close the loop between models and data

Pre-label validation, error mining from production inference, and targeted re-labeling of failure modes keep datasets aligned with real-world drift — without rebuilding pipelines from scratch.

Collection and validation services

Custom image and video capture campaigns; audio corpus gathering; validation of auto-label outputs; consensus adjudication on disputed spans; golden set creation and maintenance for ongoing QA.

When teams need validation most

Before major model releases, after taxonomy changes, when merging vendor datasets, or when production metrics drop — structured validation prevents silent label regression.

Multi-tier QA methodology

Tier-one annotation, tier-two senior review, and tier-three auditor sign-off — with IAA tracking, error categorization, and weekly quality reports for enterprise program managers.

Operational partnership

Dedicated PMs, 24/7 coverage, and SLAs for continuous ingest from global ML teams. Validation throughput scales with your release cadence — not just initial dataset builds.

Get started

Keep training data trustworthy as you scale. Tell us your current QA gaps, volume, and release schedule — we design collection and validation programs that match your ML operations.