From the Fieldiq team
Technical and practical writing on document extraction, back-office automation, and operations ROI.
How Long Should You Keep Extracted Document Data? Retention Policies for AP and Claims Teams
Regulatory requirements, audit windows, and operational reuse patterns all influence how long extracted invoice and claims data should be stored.
A Practical Guide to Implementing AP Automation at 25,000 Invoices Per Month
What the first 90 days of an AP automation rollout actually look like: ERP mapping, exception threshold calibration, team retraining, and the metrics you should track from day one.
Handling Multi-Vendor Document Formats Without Retraining Your Extraction Model
Enterprise procurement teams receive invoices from hundreds of vendors — each with a different layout. We explain how template-free extraction handles format variability.
Headcount vs Automation: When the Crossover Point Actually Makes Financial Sense
The 10,000 document/month threshold isn't magic. We show the math on where document automation becomes cheaper than adding another AP specialist.
Purchase Order Extraction: Field Coverage, Edge Cases, and Multi-Format Handling
POs come in dozens of formats. We cover how we handle variadic line-item tables, multi-currency POs, and header-footer extraction across formats we've seen from 50+ enterprise procurement teams.
Texas TDPSA and Document Processing: What Operations Teams Need to Know
If you process documents containing personal data in Texas, the TDPSA creates new obligations around data minimization and retention. Here's what it means for your AP and claims workflows.
How Document AI Accuracy Is Actually Measured (And How to Evaluate Vendor Claims)
Field-level accuracy vs form-level accuracy vs end-to-end throughput rate — these are not the same number. A guide to reading vendor accuracy claims without getting misled.
Three ERP Integration Patterns for Document Extraction (SAP, NetSuite, Dynamics)
Direct API push, file-drop + polling, and webhook-triggered — the three integration patterns we see most. We explain when each one is appropriate and what the data mapping looks like.
Automating Insurance Claims Intake Without Breaking Your Adjuster Workflow
Claims processing has specific regulatory and workflow requirements. Here's how we approach claims extraction without disrupting the downstream adjuster and compliance review process.
Designing an Exception Routing System That Ops Teams Actually Use
The extraction accuracy number is important. But what happens to the 1% of documents that don't hit the threshold? We explain how exception routing should work — and how we built it.
Every Field Fieldiq Extracts from an Invoice — and Why Each One Matters
Vendor ID, invoice number, line items, tax codes, payment terms — a complete breakdown of the 23 structured fields we extract from invoices and how they map to your ERP schema.
The Real Cost of a 20-Person BPO Operation (Beyond the $18/Doc Invoice)
The per-document fee is only part of it. Error remediation, rework cycles, vendor management overhead, and turnaround delays add up to a number most ops leaders haven't calculated.
Why OCR Fails on Real Invoices (And What ML Extraction Does Instead)
Traditional OCR converts pixels to characters. ML extraction understands what those characters mean — vendor ID, line item, tax code. We break down the difference and why it matters at scale.