Turn Unstructured Documents Into Structured Data
Intelligent document processing combines OCR, layout understanding, and language models to extract structured fields from invoices, claims, contracts, and forms — replacing manual keying with automated extraction that routes exceptions to humans instead of routing everything to humans.
What RLM Delivers on Intelligent Document Processing
IDP is among the most reliably positive-return AI investments available, because the baseline is expensive manual labour and the output is measurable. The risk is not that it fails outright — it is that straight-through processing rates land far below the demo, and the exception queue quietly recreates the cost you were removing.
How We Approach Intelligent Document Processing
A structured advisory process tailored to ai & automation — from discovery through vendor selection and implementation support.
Document Inventory & Volume Analysis
We catalogue the document types you process, their monthly volumes, current handling cost per document, and the variability of format and quality — the inputs that determine whether automation pays and where to start.
Extraction Requirements Definition
We define the fields that must be extracted, the accuracy each one requires, and — critically — the cost of a wrong value versus a flagged exception, which differs enormously between an invoice line and a claim decision.
Platform Evaluation on Your Documents
We evaluate platforms — ABBYY, Hyperscience, Instabase, Rossum, Azure Document Intelligence, AWS Textract — using a blind sample of your real documents, including the poor-quality ones you would rather not show a vendor.
Exception Workflow & Human-in-the-Loop Design
We design the exception path: what gets flagged, who reviews it, how corrections feed retraining, and how you prevent the review queue from becoming the bottleneck the project was meant to remove.
Intelligent Document Processing Evaluation Criteria
The dimensions that consistently separate a deployment that pays for itself from one that quietly becomes shelfware.
Straight-Through Processing Rate on Real Documents
Demo accuracy uses clean documents. Insist on a blind test with your worst-quality real inputs — the delta between demo and reality is the actual business case.
Field-Level Accuracy, Not Document-Level
A document that is 95% correct may still be unusable if the wrong 5% is the payment amount. Require accuracy reporting per field, weighted by what an error actually costs.
Exception Queue Economics
If 30% of documents require review, you have automated 70% of the work and added a new system to maintain. Model the exception rate honestly before approving the business case.
Template-Free vs Template-Based
Template-based tools are accurate on stable formats and brittle on new ones. If your supplier base changes, template-free extraction is worth the premium — otherwise it is not.
Handwriting, Scans & Quality Floor
Establish the input quality below which the platform simply will not perform, and confirm what share of your real volume falls under that floor.
Data Residency & Document Sensitivity
Documents frequently contain regulated personal data. Confirm where processing happens, whether your documents train shared models, and what the retention policy actually is.
"RLM made us cost out the exception queue. It changed which platform we chose and it changed how we scoped the pilot."
Independent means we will tell you when the answer is to keep what you have.
Where This Matters Most
Sector-specific considerations we see repeatedly in ai and automation engagements.
A Sample of the AI & Automation Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Ready to Get Intelligent Document Processing Right?
Start with a no-cost conversation with an RLM advisor — vendor neutral, no agenda, just clarity on the right path forward for your environment.
Talk to an AI Advisor