Human validation of AI output
Extracted fields checked against source documents or systems.
AI data verification — human validation of AI-extracted data, accuracy checks, error correction and QA reporting — so AI-driven data pipelines stay trustworthy.
AI extraction is fast but not perfect. OCR mistakes, misclassified fields, hallucinated values and low-confidence predictions slip into databases, CRMs, invoices, claims and reports — and downstream teams inherit the errors. "Trust the AI" isn't a data strategy.
Opervics runs AI data verification as a managed function — human validation of AI-extracted data, accuracy checks against source, error correction, confidence-based routing and QA reporting — so AI-driven pipelines stay trustworthy as volume scales.
Extracted fields checked against source documents or systems.
Field-level and record-level accuracy scored and trended over time.
Mistakes fixed at the reviewer stage — not just flagged for later.
Low-confidence AI output routed to human review; high-confidence sampled.
Findings turned into prompt, rule and model improvements with your team.
Accuracy rate, error categories, correction rate and volume handled.
These are the same operational issues we see across clients — and the same ones our process is designed to remove.
Bad extractions land in CRMs, ERPs and reports before anyone notices.
AI output goes straight to production with no validation step.
Some fields are accurate, others aren't — nobody knows which.
Internal teams can't keep up with AI output volume — review queue grows.
Errors are found but never turned into prompt or model improvements.
Nobody knows the actual accuracy rate of AI extraction.
Every service inherits the same baseline security posture, then adds service-specific controls on top.
Access, change management, monitoring and incident response aligned to SOC 2.
PII handling, redaction and access controls defined with your team.
Every reviewer action logged and sampled for quality.
Accuracy rate, error categories and correction rate reported weekly.
Human validation of AI-extracted data, accuracy scoring, error correction, confidence-based routing and QA reporting.
OCR-extracted documents, LLM-extracted fields, form data, invoices, claims, contracts, CRM records and any AI pipeline output.
Both — low-confidence output goes through full human review; high-confidence output is sampled based on your risk profile.
We correct errors at the reviewer stage and log findings so your team can improve prompts, rules and models.
Field-level and record-level accuracy scored against source — trended weekly by data type and error category.
Per record, per volume band, or as a monthly retainer — agreed upfront.
A short conversation is usually enough to scope the pipeline and propose a starting point.