Confidence scores
Each prediction includes an accuracy score to help you automate straight-through processing or send cases to review.
What accuracy means
Every prediction includes accuracy: a score between 0.10 and 0.95 that reflects how confident the classification is for that code.
| Range | Typical interpretation |
|---|---|
| 0.75 – 0.95 | Strong match — reasonable to auto-accept when the rest of your file agrees |
| 0.50 – 0.74 | Moderate — worth a rules check, a second source, or human review |
| 0.10 – 0.49 | Weak — treat as uncertain and review before binding |
Use accuracy alongside the code and title, not on its own. A high score on the wrong industry is still the wrong industry.
Primary and alternate predictions
When you request two predictions (the default), you receive:
prediction_a— our primary classification for the businessprediction_b— a plausible alternate when the business could fit more than one industry
Compare the two accuracy values to see how clear-cut the answer is:
| Gap between primary and alternate | Suggested handling |
|---|---|
| Large (about 0.15 or more) | The primary code is usually the right place to start |
| Moderate (about 0.05 – 0.14) | Show both codes to an underwriter |
| Small (under 0.05) | The business may be ambiguous — review or gather more detail |
The codes can differ even when scores are close (for example, full-service restaurant vs. limited-service). Always display title with code so reviewers understand the distinction.
Using scores in your workflow
Straight-through processing
Use a primary accuracy threshold and a minimum gap before auto-selecting prediction_a. Many teams start around 0.80 accuracy with a 0.15 gap, then tune from production results.
Existence check
If you enable includeExistenceCheck, treat very low accuracy together with exists: false as a stronger signal to review the entity itself, not just the code.
Your own data
If you already trust a website, prior policy class, or application narrative, weigh disagreements heavily even when accuracy looks high.
When scores are low
Very low accuracy usually means the service could not classify the business with confidence — for example, sparse inputs, conflicting signals, or an unusual operation. It is not a substitute for a careful manual pick.
To improve results:
- Send
nameplusstateor a full address when you have them - Add a clear
descriptionwhen the name alone is generic - Use description-only mode only when your description is detailed and trustworthy
Include responseId in support requests if many records cluster at the low end of the range.