How Fieldiq extracts structured data at 99% accuracy
ML field detection trained per document class — not a generic OCR engine. Every extracted field carries an individual confidence score. Fields below your threshold are flagged and routed before they reach your ERP. This is what 99% field-level accuracy actually means.
Field-level extraction — not form-level OCR
Traditional OCR reads pixels as characters. Fieldiq's ML extraction understands what those characters mean in context — vendor ID, not just a string. Invoice number, not just a sequence of digits. That semantic understanding is why our field-level accuracy reaches 99%.
When extraction flags a field — here's what happens
The 1% of documents that don't meet confidence thresholds go somewhere useful — not back to a BPO queue.
Data in the format your system expects
Extracted fields push into SAP, NetSuite, Dynamics, or any ERP via REST API, webhook, SFTP drop, or structured JSON export. No middleware step between Fieldiq and your system of record.
Use whichever pattern matches your infrastructure. Most teams use REST API push for their primary ERP and SFTP flat-file for archive and audit workflows.
- REST API push — direct to SAP, NetSuite, Workday, Dynamics
- Webhook delivery — triggers your existing workflow automation
- Structured JSON — field-mapped to your schema definition
- CSV flat-file — SFTP drop for batch processing workflows
// POST /api/v1/extractions/result { "document_id": "inv_07421", "doc_type": "invoice", "status": "clean", "fields": { "vendor_id": "meridian-supply", "invoice_no": "07421", "total": 12480.00, "tax_code": "TX-8500", "due_date": "2026-07-01" }, "confidence": 0.993, "exception": false, "erp_push": "SAP_S4HANA", "processed_ms": 847 }
See the extraction engine run on your actual documents.
Bring a sample invoice, claim, or PO to the demo call. We'll extract it live and show you the field-level confidence scores, exception routing logic, and ERP output format.