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Validation Logic

Antei applies structured, multi-layered validation to every record ingested from integrations or file uploads. This ensures your organization only works with complete, de-duplicated, and jurisdiction-ready data — reducing errors and increasing audit risk coverage.

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Why This Matters

  • Prevents downstream compliance failures
  • Surfaces incomplete or mismatched records early
  • Enables consistent reporting and reconciliation
  • Ensures controlled deduplication and data ownership
  • Keeps your organization’s tax posture clean and defensible
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1. Schema Validation

Each incoming record is validated against Antei’s internal schema:
  • Required fields (e.g. contact_id, amount, jurisdiction) must be present
  • Field types must conform (e.g. number, enum, ISO date)
  • Conditional rules (e.g. country required if tax_applied) are enforced
❗ Invalid records at this stage are immediately flagged and not processed further.
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2. Entity Normalization & Component Breakdown

Valid records are decomposed into sub-entities (e.g., Transaction, Product, Contact, Invoice, TransactionOp) and mapped to Antei’s internal data registry.
  • Mappings are config-driven and aligned with the source system
  • Each component inherits sync metadata and classification
  • Nested objects are flattened for processing
📌 Example: One invoice may contain multiple transaction ops and line-item products.
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3. Deduplication & Matching Engine

Antei applies a weighted deduplication algorithm that combines:
  • Fuzzy Matching — For loosely matching names, addresses, etc.
  • Structured Field Scoring — For fields like tax ID, country, and external references
  • Threshold Score — ≥ 90% similarity flags a match as Unprocessed (needs user override)
  • New Object Creation — If similarity is < 90%, a new record is auto-created
Deduplication is applied both:
  • At the entity level (e.g., Product, Contact)
  • At the final transaction compilation stage
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4. Mapping & Link Validation

Antei checks relationships to ensure:
  • Each entity references a valid product_id, contact_id, or jurisdiction_id
  • Linkages are consistent with existing org-scoped records
  • Duplicate relationship mappings are avoided
Example: A transaction line referencing a deleted product will be flagged for review.
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5. Classification & Enrichment

Antei auto-applies enrichment tags such as:
  • Tax classification (e.g. exempt, taxable, reverse charge)
  • Entity role (e.g. supplier, customer, tax authority)
  • Sync metadata (e.g. source, timestamp, entity_type)
These enrichments power downstream logic like taxability rules and country-specific filings.
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6. Completeness & Final Checks

A final check ensures all mandatory fields and linked entities are present.If anything is incomplete, the entity is marked as:
  • Unprocessed — Requires manual resolution
  • Logged in dashboards (e.g. Unprocessed Products, Unprocessed Transactions)
  • Skipped from downstream flows until corrected
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7. Manual Override & Reprocessing

Users can resolve unprocessed items via the UI:
  • Fill in missing fields
  • Override and reassign to correct entities
  • Re-run validation post-manual mapping
✔️ Once approved, these records are stored as processed and marked clean.
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8. Final Persistence & Usage

Successfully validated records are committed to your workspace:
  • Structured by module (Transactions, Products, Contacts, etc.)
  • Linked to sync payloads and original metadata
  • Available for reconciliation, return generation, analytics, and audit trails

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Common Unprocessed Scenarios

EntityReason for UnprocessedExample
TransactionMissing contact_id or fuzzy match ≥ 90%Refund not linked to contact
ProductTax classification missing or duplicate matchImported product lacks taxability
InvoiceEntity missing or tax rate mismatchInvoice linked to unregistered entity

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Logs & Observability

  • Validation failures are logged per record
  • Retry logic is applied for transient integration/API errors
  • Metadata tags identify all skipped, unprocessed, and user-modified entries
  • UI allows filtered download of failed records
  • Validation logs are permanently attached to sync and ingestion records

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Summary

This layered validation pipeline combines config-based extraction, fuzzy matching, and score-driven deduplication to deliver clean, connected, audit-ready data for every jurisdictional workflow.

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Need Help?

For validation-specific help or field mapping support, contact [email protected].