AI Verifications: Automated Data Quality Checks for Document Processing
Learn how AI Verifications automatically review extracted data, catch errors before they enter your systems, and ensure 95%+ accuracy without manual review.
Manual checks are no longer the only way to ensure clean, accurate data extraction. Our latest feature, AI Verifications, mimics the "manual review" step that is often required to ensure data quality.
What It Does
AI Verifications is built to replicate the logic of a human teammate: look over the extracted data and flag anything that seems off. It works in two key ways:
1. Generic Verification
After TableFlow extracts data from a file (PDF, CSV, Excel, etc.), it checks whether the output looks correct. Think of it as a second set of eyes: "This item number doesn't match the source file," or "This row might have been misread." It's a fast sanity check to catch common extraction issues.
2. Custom Verification
For more complex documents, you can define specific rules the extracted data should follow. For example, you can cross-check hundreds of extracted rows against total values that appear elsewhere in the PDF. If the totals don't match? We flag it before anything moves forward.
Understanding Issue Severity
To help you prioritize what needs attention, issues are categorized into three severity levels. Each verification calculates a score from 0-100% based on the number and severity of issues found.
Error
Critical issues that halt the workflow. Examples: missing required fields, data mapped to wrong fields, or incorrect row counts.
Warning
Potential problems worth reviewing. These flag suspicious data that might need a second look but won't stop the process.
Info
Minor observations that provide helpful context without impacting data accuracy.
How It Works in Flows
AI Verifications is built right into Flows, our tool for building repeatable document workflows. Here's a simple setup:
- 1. Add an Extraction step
- 2. Add a Verification step
If verification passes, move forward. If it fails, TableFlow can flag the issue, stop the flow, and even auto-correct based on what it learns.
Monitoring Data Quality
The interface gives you a clear view of your data's health at a glance:
Visual Score Indicator
Color-coded progress bar: Green (>90%) for great data, yellow (>70%) suggests review needed, red (<70%) indicates critical problems.
Grouped Issues
Findings organized by severity with count badges (e.g., "2 Errors"), letting you focus on what matters most.
Pass/Fail Status
Clear visual indicators instantly show whether verification passed or failed.
Complete History
Timeline of all extraction and verification attempts for full audit trail and debugging.
Self-Correcting Workflows
When combined with rerun capabilities, AI Verifications create powerful self-correcting workflows. If verification fails, the system uses the error list as direct feedback to automatically trigger a new extraction attempt, fixing the specific issues found.
This means:
Why It Matters
Your team shouldn't have to review every single extraction. AI Verifications gives you confidence that extracted data is right, letting you build more robust, reliable workflows you can trust completely.
In Summary: AI Verifications act as an automated quality control layer for document processing, catching errors before they enter your systems while adapting to your specific business rules and document types.
Frequently Asked Questions
About Eric Ciminelli
CTO & Co-Founder at TableFlow. Expert in AI/ML systems, distributed computing, and building enterprise-grade document processing solutions.
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