Manual data entry from documents is slow, error-prone, and scales linearly with volume. When the invoice pile doubles, so does the headcount. When the scan quality drops, so does the accuracy. When the document format changes, the process breaks.
We build production OCR systems that read your specific documents accurately, with AI extraction, validation pipelines, and exception handling for the cases where the system needs a human. We've shipped industrial OCR systems deployed in real production environments.
Production OCR systems built for your document types, not generic demos
AI extraction with confidence scoring and human review for exceptions
Structured data output delivered to your ERP, database, or downstream system
Built and shipped a production gas station fuel delivery invoice OCR system
Recent outcomes
AI OCR · Gas station operations
20K+ daily transactions
Built a fuel delivery invoice OCR system processing thousands of invoices per month with zero manual data entry from email to ERP.
AI OCR · Supermarket loyalty platform
100+ receipts in month one
Deployed receipt OCR for a supermarket chain loyalty program, processing product receipts and eliminating manual entry errors on day one.
Document extraction · Financial services
87% straight-through rate
Built a multi-format invoice extraction system with exception queues and human review, cutting processing time from 4 minutes to under 30 seconds per document.
The problem
Data entry team keying numbers from PDFs and scanned documents into your system all day?
OCR attempts that failed because document layouts vary or scan quality is inconsistent?
Short answer
RaftLabs builds production OCR systems for clients across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia. AI extraction, confidence scoring, exception handling, and structured output to your ERP or database. We shipped a gas station invoice OCR system processing 20,000+ daily transactions. Fixed price, production-ready.
Key takeaways
Trusted by
Every "OCR" demo looks impressive on clean, formatted documents. Production systems deal with scans at an angle, handwriting on pre-printed forms, faxed documents, photos taken on a phone in poor lighting, and vendor invoice formats that change without notice.
According to a McKinsey global survey, 70% of organizations are at least piloting automation of business processes like document workflows in one or more business units. For most of them, OCR is where that automation stalls — not because the technology doesn't exist, but because generic APIs fail on real-world document variation.
The hard part is not reading the text. It's extracting the right fields from variable layouts, validating them against business rules, routing the exceptions to the right people, and delivering clean data to a system that needs it in a specific format.
We shipped a gas station fuel delivery invoice OCR system, thousands of invoices a month, multiple supplier formats, processing from email attachment to ERP posting without human data entry. That's the production-grade OCR we build.
Capabilities
Automated document capture from every source your business uses: email attachments, upload portals, network folder polling, and system-to-system handoff, across digital PDFs, scanned PDFs, images, and multi-page files. Deduplication by file hash prevents the same invoice from being processed twice when it arrives via two channels, and processing status tracking gives your operations team visibility into the queue.
Image quality preprocessing that fixes the real-world scan conditions that break naive OCR: deskewing phone-captured invoices, contrast normalization for faded receipts, noise removal for fax artifacts, and upscaling low-resolution images. These steps are the difference between 70% accuracy on real-world documents and 95%+.
Extraction of the specific data fields your downstream system needs: invoice headers, line items, totals, and custom fields specific to your document types. Template-based extraction handles vendors with consistent formats, AI layout-aware extraction handles variable formats, and confidence scoring on every field tells you which values to trust and which to route for review.
Field-level validation before any extracted data reaches your system: format checks, required field presence, and cross-field consistency like line item totals summing to the subtotal. Business rules run against your reference data, and documents that fail validation are never silently discarded; they enter the human review queue with the specific failure reason attached.
Web interface where your operators review documents that didn't pass straight-through processing, built for high-volume queues. The original document sits beside its extracted fields and confidence scores, so one-click accept, inline correction, and batch review let reviewers process 40-50 documents per hour. Every correction feeds back into the retraining pipeline, so the exception rate drops over time.
Structured output delivered to your downstream system in the format it consumes, with the output schema mapping extracted fields to your target data model exactly so nothing needs downstream transformation. Delivery runs in real time or on a batch schedule, and a full audit trail records every document from receipt to delivery.
How we work
Every OCR project follows the same four phases. Scope is locked and price is fixed before development starts.
We audit your document types, scan quality, field extraction requirements, and downstream system. You leave week 1 with a written scope and a fixed-price quote. No development starts without your sign-off.
We design the extraction pipeline before writing production code: engine selection (Tesseract, AWS Textract, Google Document AI, or LayoutLM), pre-processing steps for your scan conditions, and exception routing rules. The spec is locked before build starts.
Working extraction at a staging environment by the end of sprint one. Bi-weekly accuracy reports. QA runs in parallel, not as a phase at the end. Integration to your ERP or database tested against real documents from your production environment.
Production deployment with monitoring and exception queue activated on launch day. 8 weeks of post-launch support included. Accuracy benchmarks reviewed at 30 days and 60 days with retraining if needed.
Why us
The engineers who assess your OCR problem also build the solution. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 10.
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record across AI, OCR, SaaS, automation, and enterprise platforms across healthcare, fintech, logistics, and hospitality.
GDPR, HIPAA, SOC 2 - compliance requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant document processing systems for US healthcare clients and GDPR-compliant OCR pipelines for European markets.
Custom AI Development
End-to-end AI systems from model selection to production deployment.
AI Agent Development
Autonomous agents that take action inside your workflows.
Generative AI Development
LLM-powered products and content automation pipelines.
RAG Pipeline Development
Retrieval-augmented generation grounded in your proprietary data.
Type, volume, current accuracy problems. We'll design the system and give you a fixed cost.
Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.
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Read moreCustom OCR development is the process of building an optical character recognition system designed for your specific document types, extraction requirements, and output destinations, rather than a generic OCR API that reads text but doesn't extract structure. A custom OCR system reads your documents, understands which fields matter, extracts them accurately, validates the output against your business rules, and delivers clean structured data to your downstream system. We've built production OCR systems for industrial environments where accuracy and throughput matter.
For clean, digital PDFs, accuracy is typically 97-99%. For scanned documents, accuracy depends on scan quality, resolution, skew, noise, and contrast. We improve accuracy for challenging scans through pre-processing (image enhancement, deskewing, contrast normalization), vendor-specific extraction templates for high-volume document sources, AI-based fallback for fields that rule-based extraction misses, and confidence scoring that routes low-confidence extractions to human review. Most production systems we build reach 85-95% straight-through processing.
Layout variation is the hardest problem in OCR. The same invoice from the same vendor might be formatted differently depending on the system it was generated from. We handle variation through a combination of adaptive template matching (the system selects the best extraction template for each document based on layout features), AI extraction that generalizes better than rule-based approaches, and exception queues where high-variation documents go to human review with guided extraction. For known high-volume vendors, we build specific extraction rules that give the best accuracy.
Every production OCR system we build has an exception path. Low-confidence extractions and documents that fail validation go to a human review queue. Reviewers see the original document and the extracted fields side by side, correct any errors, and confirm the output. Corrections feed back into the system to improve future accuracy for similar documents. The exception path is designed to be fast, a reviewer handles an exception in under 60 seconds. The goal is high automation rates with a clean fallback for the cases that need a human.
We've built production OCR systems for: invoices (our gas station fuel delivery case, thousands of invoices per month, automated from receipt to ERP posting), purchase orders, delivery notes and packing lists, forms and applications, identity documents for KYC, shipping labels and customs documents, industrial inspection reports, and certificates of analysis. The extraction requirements differ significantly by document type. We design the extraction approach based on your specific document characteristics.
A focused OCR system, one document type, extraction of 5-15 fields, validation, and output to one target system, typically runs $20,000--$50,000. Multi-document type platforms with exception workflows, human review interfaces, and multiple output integrations run $50,000--$120,000. We've built industrial-grade production systems across this range. We scope every project before pricing it.
Work with us
We scope OCR Development Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Insurance automation
Claims, policy processing, and compliance workflow automation.
Healthcare admin automation
Prior auth, patient intake, scheduling, and billing automation.
Logistics automation
Dispatch, tracking, exception handling, and document processing.
Legal automation
Contract drafting, client intake, billing, and compliance automation.
Real estate automation
Lease management, maintenance requests, and tenant communication.
Restaurant automation
Order management, staff scheduling, and inventory automation.