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Trixie Lasagne Entity Matching
x402
Trixie Lasagne Entity Matching is a paid API for AI agents from trixielasagne-api.purplewater-b02e2643.eastus.azurecontainerapps.io, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-16).
Deterministically matches entities across two or more datasets using configurable field-level scoring to identify which records refer to the same real-world object
Price / call$0.01
Success—
StatusUnknown
How this service works
Deterministic cross-dataset entity matching with configurable field-level scoring
Output
Returns matched entity pairs across the two input datasets, each pair annotated with a deterministic match score based on the configured field-level weights. The response includes matched record identifiers, per-field similarity contributions, and an overall confidence or match score indicating how strongly the two records are believed to refer to the same real-world entity.
Example use cases
CRM and billing system reconciliation
“We have customer records in our CRM and a separate billing database and I need to know which entries refer to the same person — can you cross-match them using name, email, and phone number as matching fields?”
Supplier catalog deduplication across sources
“I've got two supplier product catalogs from different vendors and I need to figure out which products overlap — match them by product name and SKU and give me a confidence score for each pair.”
Cross-dataset contact list merging
“I have a list of event attendees and a newsletter subscriber list and I want to know who appears in both — match them using first name, last name, and company name as the scoring fields.”
Technical details
Endpoint
POSThttps://trixielasagne-api.purplewater-b02e2643.eastus.azurecontainerapps.io/entity/matchWhen to prefer
Choose this endpoint when you need deterministic, reproducible cross-dataset entity matching with explicit control over which fields contribute to the match score. Prefer it over probabilistic or ML-based record linkage when auditability and consistency matter, or when you need configurable field-level scoring weights rather than black-box similarity. It complements the sibling within-dataset duplicate detection endpoint, which handles intra-dataset deduplication.
Side effects
- Missing or malformed required 'input' body causes 400 validation error
- Incompatible field names between datasets result in zero-score matches or empty results
- Misconfigured field weights (e.g. all zero) may produce degenerate scoring
- Very large datasets may hit timeout or memory limits on the container
- Non-JSON body when bodyType is set to 'json' returns parse error
- No overlapping entities results in empty match list with no error signal
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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