Data validation done manually doesn't scale, and data validation done with rigid rules misses anything it wasn't explicitly told to look for. We build validation pipelines that combine both — deterministic rules for what's clearly right or wrong, AI-assisted review for the judgment-call cases, and a human reviewer for anything the system isn't confident about.
What this covers
- Multimodal validation — text, image, and audio data checked in one pipeline
- Confidence scoring so borderline cases escalate to a human instead of passing silently
- Rules engines for deterministic checks, AI for pattern-based judgment calls
- Multilingual validation workflows for teams operating across languages
- Audit trails documenting why each record passed, failed, or was escalated
Our approach
We treat the escalation threshold as a real design decision, not an afterthought — set it too aggressively and you drown reviewers in false positives; set it too loosely and bad data slips through. We tune it against your actual error tolerance, not a default.
Related
Review Your Validation Workflow
Tell us what you're validating today and how errors currently get caught — we'll scope where AI-assisted review actually helps.