Intelligent document processing (IDP) means turning PDFs, scans, contracts, invoices, forms, and emails into structured, validated data your systems can act on. Modern intelligent document processing solutions do this with multimodal LLMs, plus OCR and layout models where they help, rather than a template per document type. The outcome is fewer people re-keying data, faster turnaround on the processes that depend on those documents, and a searchable record of what every document said.
Most IDP pilots stall the same way. The model extracts beautifully from twenty sample documents, then meets a scanned fax, a three-column table, or an amended clause on page 47. Nobody defined what "correct" means per field, and there is no path for the cases the model gets wrong. Production-grade means schema-constrained output, per-field validation and confidence, a review queue for uncertain cases, and an eval set that tells you whether a change made things better or worse.
What We Build
Extraction and Classification Pipelines
A pipeline that classifies each document, routes it to the right extraction prompt, and returns output constrained to your schema: typed fields, line items, normalized dates, entities resolved against your reference data. Multimodal models read the page as an image, so tables, stamps, and handwriting survive; OCR and layout models are used where they are cheaper or more accurate for a document class.
Validation, Confidence, and Human Review
Every field carries a confidence score and runs through validation rules: totals must reconcile, dates must be in range, a supplier must exist in your ERP. Anything that fails or falls below threshold lands in a review queue where a person confirms or corrects in seconds. Corrections flow back into the eval set, so the pipeline improves with use instead of drifting.
Evaluation and Cost per Page
We build a labeled document set with you and measure field-level precision and recall before and after every change. Cost per page is a design input: cheaper models for classification and clean documents, stronger models only for the pages that need them, caching and batching where the workload allows.
Downstream Integration
We wire the pipeline into your database or warehouse, your workflow system, or a retrieval layer so documents become part of an AI knowledge base your teams and agents can query. Chunking and embedding strategy follows your document types, not defaults; see our notes on semantic chunking.
How It Runs on Your Stack
The pipeline runs in your cloud or on-prem, on your data, under your access controls. On AWS we build on Amazon Bedrock for multimodal extraction and embeddings, use Amazon Textract where its OCR or forms output beats a model on a given document class, and index results into OpenSearch for retrieval. As an AWS Advanced Tier Services Partner with the AI Services Competency, we can take this through the GenAI and RAG fast track on AWS. Other clouds and self-hosted models work too.
On build versus buy: a packaged IDP platform is reasonable when your documents are standard, volume is modest, and a form-style output is enough. A custom pipeline on your stack wins when documents are messy or domain-specific, when accuracy on specific fields drives real money, when output has to land in your own systems and knowledge base, or when per-page vendor pricing at your volume dominates the budget. Our guide to multimodal RAG over images, PDFs, and text covers the retrieval tradeoffs for document-heavy corpora.
Proof
We supported Boomi in building the Boomi Knowledge Hub, the core of their RAG pipeline at global scale. The solution ingests documents, performs validation, chunking, and embedding via Amazon Bedrock, then indexes the results into OpenSearch. Boomi's team said the work helped them accelerate development, resolve key product and technical uncertainties, and establish a scalable technical foundation they could build on with confidence.
From Idea to Production in Weeks
Document processing projects run through the AI Launchpad Launch Sequence. Use-Case Fit: pick the document flow with the clearest ROI, audit a sample of real documents, define the schema and per-field accuracy targets. Build on Your Data: the extraction, classification, and validation pipeline, engineered against your real documents from day one, in your cloud or on-prem. Harden for Production: eval set, review queue, guardrails, cost and latency tuning, observability. Launch and Run: go live, hand over, and keep improving as new document types and edge cases appear.
Frequently Asked Questions
What is intelligent document processing?
Intelligent document processing is the use of AI, today mostly multimodal LLMs alongside OCR and layout models, to extract, classify, and validate data from unstructured documents such as PDFs, scans, contracts, invoices, and forms. Unlike template-based capture, it handles layouts it has not seen before and returns structured output that downstream systems can consume directly.
How accurate is LLM-based document extraction?
It depends on the document class and the field, which is why we measure it rather than quote a number. A labeled eval set gives you field-level precision and recall, and validation rules plus confidence thresholds route uncertain cases to a human reviewer, so errors are caught before they reach your systems.
Should we buy an IDP platform or build our own pipeline?
Buy when your documents are standard, volume is modest, and a form-style output is enough. Build when documents are domain-specific, when accuracy on particular fields has financial weight, when output needs to land in your own database or knowledge base, or when per-page vendor pricing at your volume outweighs owning the pipeline.