ParseRail
api.thecompound.tech/v1
39 endpoints1 paused242/242 checks passingcredits from $20

Extract & structure

Batch categorization

Up to a hundred items against your taxonomy in one call, products, transactions, tickets, each with a confidence.

POST/v1/categorize$0.03/batch, 3 credits, charged only on success120 calls a minute per key

The example in the published schema

charged 0 credits
  1. auth (bearer key)
  2. per-key throttle
  3. validate body
  4. balance precheck
  5. run the capability
  6. charge credits ON SUCCESS ONLY
  7. JSON
The request body/v1/categorize
{
  "items": [
    "AWS invoice June",
    "Uber to airport",
    "Figma annual"
  ],
  "taxonomy": [
    "software",
    "travel",
    "office",
    "other"
  ]
}
The responsepublished example
// sending the call
// 0 credits charged so far
curl https://api.thecompound.tech/v1/categorize \
  -H "Authorization: Bearer $COMPOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"items":["AWS invoice June","Uber to airport","Figma annual"],"taxonomy":["software","travel","office","other"]}'

Scored

10 of 10 field checks passed when the eval suite last ran against production, on 2026-07-08. Every endpoint's score

The Extract & structure guide has this endpoint with its example response. The reference has every parameter.

More from Extract & structure

Field extraction $0.04/callPull a field set you define out of any block of text. You name the fields; you get typed values with confidence.Classification $0.02/callRoute or tag text against your own taxonomy, a label, a confidence, and a one-line rationale.Structure to your schema $0.06/callAny messy input, text, HTML, an email, plus YOUR JSON schema → output shaped to it, validated against your required fields and property types, with an automatic corrective retry and a `valid` flag.Record normalization $0.05/batchA batch of messy records → clean canonical rows, with a change log of every fix (casing, formats, dedup-ready values).Entity matching $0.08/runTwo record sets → which rows are the same real-world thing, with confidence and reasoning. Fuzzy names, typos, aliases handled.
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