Extraction stack
Layout-aware VLMs plus rule engines for arithmetic and cross-field checks.
Services · 03
Intelligent document processing for high-volume operations
Intelligent document processing (IDP) is how you classify documents, extract fields, validate them against business rules, and route them into downstream systems with measurable straight-through processing. Databotiq builds IDP stacks for finance, healthcare operations, insurance, and logistics teams where silent errors are unacceptable.
At a glance
- Practice
- Intelligent Document Processing
- Best fit when
- you have high-volume document families where silent errors cost money downstream.
- Typical Rapid POC
- 14 days, fixed scope.
Problems we solve
The pains buyers describe to us first.
Templates change by payer, region, or vendor, and brittle rules do not scale.
OCR text without layout loses tables and checkboxes.
Ops teams cannot trust “100%” demos that hide low-confidence tails.
Auditors ask for lineage from a posted field to the source page.
Approach
Our approach.
We treat IDP as a quality system. Extraction is only half the problem. Calibration, monitoring, and exception review complete it. We ship confidence scores per field, per document, and aggregate dashboards so you can watch drift as vendors change PDFs.
Technical depth
Human-in-the-loop that pays for itself
Humans review the smallest possible set: low-confidence fields, rare document types, and high-impact financial fields. Everything else straight-through processes once metrics prove stability.
Tech (May 2026)
Named tools, not vague acronyms.
Specificity earns trust. The choices below reflect what we ship today, and they will evolve as new models and tools clear our internal evaluations.
Post-processing
Canonicalization, unit normalization, and payer-specific maps.
Workflow engines
Review queues and service level tracking, wired to operator dashboards.
Where this fits
Industries and roles we ship for.
Revenue cycle
Remittance advice, EOBs, and denial management workflows.
AP and AR
Invoices, credit memos, statements.
Insurance
Loss runs, endorsements, submission packages.
Case pattern
Two hundred thousand pages a month of remittance advice and EOBs
This pattern is for revenue cycle teams where payer PDFs and faxes arrive in bulk and posting accuracy is non-negotiable. The goal is high straight-through processing with a tight human review surface on the fields that actually drive cash.
Read the case patternOutcome
What this means for you.
You increase straight-through processing without opening a liability hole. Finance and compliance see lineage, and operators see fewer mystery exceptions.
Further reading
Related practice notes and patterns.
FAQ
Questions buyers ask about intelligent document processing.
Specifics on accuracy, deployment, integration, and the proof path. If something isn't covered here,ask us directly.
What is IDP versus OCR?
OCR turns pixels into text. IDP turns documents into decisions: what it is, what it means, whether it is valid, and where it should go next.
How do you handle document variants?
We cluster samples, build per-cluster prompts and rules, and measure confusion matrices before merging clusters. Variant strategy is explicit in the runbook.
What accuracy should we expect?
It depends on document quality and field difficulty. On many doc families we target high-nineties precision on money fields with human review on the tail. Your Rapid POC quantifies this on your PDFs, not ours.
How do you deploy?
VPC or SaaS API patterns depending on your controls. We document data paths and retention for security review.
Can we keep humans in the loop permanently?
Yes, for some fields that is the correct steady state. The economics still improve when the machine handles preprocessing and humans only adjudicate edge cases.
How fast is a pilot?
Rapid POC timelines are typically 14 days for a bounded document family with agreed metrics.
See it on your data in 10 days.
We run a sandboxed Rapid POC so you can evaluate outputs, integrations, and risk before you fund production.