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Use Case

AI Document Processing and Extraction

Most enterprises process thousands of documents weekly using manual workflows built for a pre-AI world. We replace those workflows with AI systems that extract, validate, and route document data automatically.

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The Challenge

Enterprises across financial services, legal, and healthcare receive enormous volumes of unstructured documents — contracts, forms, invoices, clinical notes — that contain critical data locked in formats no system can read automatically. Teams of people read those documents, extract the relevant data by hand, and key it into downstream systems. The process is slow, expensive, and error-prone.

Our Approach

We build multimodal AI pipelines that ingest documents regardless of format — PDFs, scanned images, handwritten forms, email attachments — classify them by type, extract structured data fields, validate against business rules, and route to the appropriate system or workflow. The human reviews exceptions, not every document.

How We Do It

1

Document Ingestion and Classification

AI ingests documents from any source — email, upload, fax, API — and classifies each by document type with confidence scoring. Documents below a confidence threshold are flagged for human classification before processing continues.

2

Structured Data Extraction

AI extracts pre-defined data fields from each document type — names, dates, amounts, clauses, codes — using a combination of layout analysis, named entity recognition, and semantic understanding. Extraction accuracy is validated against your specific document templates and formats.

3

Validation and Quality Checks

Extracted data is validated against business rules you define — cross-field consistency checks, format validation, reference data lookups. Documents that fail validation are queued for human review with the specific validation failures highlighted.

4

Downstream Routing and Integration

Validated data is pushed to your downstream systems — ERP, CRM, document management, or workflow tools — via API or structured file. The system logs every document, every extraction, and every routing decision for audit purposes.

What You Get

✓85% reduction in manual document handling time across processing teams
✓Extraction accuracy of 95%+ on standard document types within 4 weeks of deployment
✓Processing capacity scales to handle 10x volume without adding headcount
✓Audit trail for every document and every extracted data point, reducing compliance risk

Technology Stack

GPT-4o VisionClaude 3.5 SonnetAWS TextractLangChainApache KafkaPostgreSQL

Related Services

Multimodal RAG SystemsView →
Agentic AutomationView →
Enterprise AI IntegrationView →

Industries We Serve

Financial ServicesLegalHealthcare

Frequently Asked Questions

What document formats can your AI process?+
We handle PDFs (native and scanned), TIFF images, JPEGs, Word documents, Excel files, and email with attachments. Handwritten documents are supported with somewhat lower accuracy depending on handwriting clarity. Multi-page documents and documents with mixed content types are fully supported.
How do you handle documents with low image quality or unusual layouts?+
Low-quality scans and non-standard layouts are handled through a combination of image preprocessing and confidence-based routing to human review. Rather than failing silently, our systems surface quality issues explicitly so operators can see exactly where and why accuracy drops.
How long does it take to configure the system for our specific document types?+
A new document type with a consistent format typically takes 1-2 weeks to configure and validate — defining the extraction schema, providing sample documents for testing, and tuning confidence thresholds. Highly variable or poorly structured document types take longer. We assess this during discovery.
What happens to documents that the AI cannot process confidently?+
Documents below your defined confidence threshold are routed to a human review queue with the extracted data pre-populated. The reviewer corrects the extraction, and that correction feeds back into system improvement over time. Nothing drops — every document gets processed, one way or another.

Ready to build this for your team?

We take this from concept to production deployment. Usually in 3–6 weeks.

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