Whatever they typed for their degree, turn it into clean data.
People don't write "Bachelor of Science." They write "bachlers in compueter sciecne," or "went to trade school for HVAC," or just "some college." One API call maps any of that to a standard taxonomy, in real time.
curl -X POST https://degree-classification.onrender.com/classify \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_KEY" \
-d '{"text": "bachlers in compueter sciecne"}'{
"level": "bachelors_degree",
"field": "computer science",
"confidence": 0.94,
"method": "fuzzy_match"
}Free text is where clean data goes to die
Application forms, resumes, and license databases all collect education and credential info as open text. Every downstream report, filter, and dashboard ends up guessing at what it means.
Handles the mess
Typos, abbreviations, regional phrasing, full sentences. It does not need clean input to give a clean answer.
One consistent taxonomy
Every response lands on the same set of standardized levels, so two systems can finally agree on what a credential means.
Confidence, not just a guess
Every match comes back with a confidence score and the method used, so you know when to trust it and when to flag it for review.
Where this fits
Hiring and staffing platforms
Normalize education fields on resumes and applications so filters and matching actually work.
Licensing and compliance
Turn self-reported license and certification text into structured records you can audit.
HR and background systems
Clean up decades of inconsistent education data during a migration or audit, without re-keying anything by hand.
Forms and intake flows
Classify credential text the moment it's submitted, instead of cleaning it up in a batch job later.
Try it on your own data
Free during early access. A key takes about a minute to get, no card required.