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Once you’ve validated documents, anyformat helps you measure how well your workflows are performing. The Analytics tab in each workflow gives you visibility into:
  • How reliable the extraction is
  • Where errors tend to appear
  • Which fields need attention

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Confidence explained

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What is confidence?

Confidence represents how certain anyformat is about an extracted value. It’s expressed as a percentage:
  • High confidence - the model is very sure
  • Low confidence - the value may be ambiguous or unclear
Confidence is calculated per:
  • Field
  • Document
  • Workflow (average)

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What confidence is (and isn’t)

Confidence IS

  • A signal, not a verdict
  • A way to prioritize human review
  • A guide for where to look first

Confidence is NOT

  • A guarantee of correctness
  • A replacement for validation
  • A measure of business accuracy
A value can have:
  • High confidence and still be wrong
  • Low confidence and still be correct

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How to use confidence effectively

Use confidence to:
  • Focus review on low-confidence fields
  • Skip reviewing obviously reliable values
  • Reduce overall human effort
A good workflow doesn’t eliminate low confidence — it contains it.

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Accuracy explained

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What is accuracy?

Accuracy measures how often extracted values are correct, based on human validation.
Accuracy reflects validated correctness, not model certainty.
Accuracy is calculated from:
  • Fields validated as correct
  • Fields corrected by humans

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Accuracy vs confidence

ConfidenceAccuracy
Model certaintyHuman-confirmed correctness
Available immediatelyImproves over time
PredictiveRetrospective
Helps prioritize reviewMeasures real performance
You need both:
  • Confidence to guide review
  • Accuracy to judge quality

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What accuracy tells you

Accuracy helps you answer:
  • Can I trust this workflow?
  • Is it ready to scale?
  • Which fields are fragile?
Low accuracy usually points to:
  • Ambiguous instructions
  • Poor field definitions
  • Edge cases in documents

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Viewing analytics

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Workflow-level analytics

In the Analytics tab of a workflow, you can see:
  • Average confidence
  • Average accuracy
  • Trends over time (if available)
This gives you a high-level sense of workflow health.

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Field-level analytics

You can also analyze metrics per field:
  • Average confidence by field
  • Accuracy by field
  • Sort fields by performance
This is often the most useful view. It helps you quickly spot:
  • Fields that consistently fail
  • Fields that don’t need review anymore
  • Outliers dragging accuracy down

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Improving results

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How to improve confidence

To improve confidence:
  • Make instructions more explicit
  • Clarify where information appears
  • Reduce ambiguity in field definitions
  • Split complex fields into simpler ones
Confidence improves when extraction becomes clearer.

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How to improve accuracy

To improve accuracy:
  • Correct wrong values during validation
  • Review low-confidence fields carefully
  • Refine the workflow when patterns appear
  • Adjust schemas or instructions if needed
Accuracy improves through human feedback loops.

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When to refine the workflow

You should consider refining a workflow when:
  • The same field is often corrected
  • Accuracy plateaus below expectations
  • New document variations appear
Refinement improves future documents, not past ones.

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A realistic quality goal

You don’t need:
  • 100% confidence
  • 100% accuracy
A good goal is:
High accuracy with focused human review on low-confidence cases.
That’s how anyformat scales without burning time.

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How Analytics fits into the bigger picture

Validation tells you what is correct now. Analytics tells you how good the system is overall. Together, they help you:
  • Decide where to spend time
  • Decide when to scale
  • Decide when a workflow is “good enough”

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What’s next?

Data & Structure

Improve field definitions and instructions

API Reference

Automate workflows programmatically