Finance and accounting teams across industries face a shared operational burden processing high volumes of invoices, purchase orders, and compliance documents manually, with every error carrying real financial consequences. The vision behind this solution was to eliminate that burden entirely: an intelligent document processing platform that combines multi-engine OCR with Large Language Models to extract, contextualise, and validate financial data automatically turning messy, varied documents into structured, action-ready information at scale.
Manual financial document processing is slow, costly, and error-prone and the consequences compound at scale.
Invoices, purchase orders, and compliance forms arrive in inconsistent formats across suppliers and systems. No single OCR engine handles every document type with reliable accuracy, and errors in extraction cascade into downstream payment and reporting failures.
Verifying invoices against purchase orders and goods received - the three-way match is a critical but labour-intensive process. Done manually, it creates payment delays, supplier disputes, and exposure to overpayments that are difficult to recover.
Raw OCR output, even when mostly accurate, lacks the contextual understanding needed to map extracted values into structured financial records. Without a validation layer, data integrity cannot be guaranteed at volume.
High-accuracy cloud OCR services are powerful but expensive. Running every document through a premium engine regardless of complexity is neither cost-efficient nor necessary - but without a smarter routing strategy, there is no alternative.
Focaloid built an intelligent OCR orchestration platform that combines open-source and cloud OCR engines with confidence-based routing and LLM-powered contextualisation delivering maximum accuracy at controlled cost.
Documents are first processed through DocTR, an open-source OCR engine, for initial extraction. Only documents that fail to meet confidence thresholds are escalated to Amazon Textract ensuring premium processing is applied where it matters, keeping costs in check without sacrificing accuracy.
At each stage of the pipeline, extracted data is evaluated against established accuracy thresholds. Documents only progress when confidence is validated maintaining data integrity throughout and flagging exceptions for human review rather than letting errors propagate silently.
Specially configured OpenAI models transform raw extracted data into structured, validated financial information. Rather than returning loose text, the LLM understands document context - supplier details, line items, quantities, pricing and maps it directly to the data structures needed for downstream processing and reporting.
Extracted invoice data is automatically compared against corresponding purchase orders and delivery receipts. Mismatches in quantity or pricing trigger instant alerts to the accounts payable team preventing erroneous payments before they are made.
The system continuously refines its extraction and classification capabilities using few-shot learning, improving accuracy over time with minimal manual input.
Once an invoice is confirmed accurate, data updates automatically in the client's financial system enabling prompt, accurate payment without any manual re-entry.
Assessed the range of document types, layouts, and processing volumes in scope. Designed the multi-engine OCR routing logic, confidence threshold framework, and LLM contextualisation layer.
Implemented the DocTR-first pipeline with Amazon Textract escalation, wired to confidence score evaluation at each checkpoint. Validated extraction accuracy across a representative document set.
Configured OpenAI models for financial document contextualisation. Built the automated three-way matching engine with discrepancy alerting and exception handling.
Connected the pipeline to the client's financial systems for automated data updates. Implemented few-shot learning loops to enable ongoing accuracy improvement post-deployment.
Finance teams drown in documents and the manual work of reading, matching, and validating them is slow, costly, and error-prone. A single mismatched invoice can mean an overpayment, a late fee, or a strained supplier relationship. By orchestrating multiple OCR engines with confidence checkpoints and layering LLMs on top to understand and structure the data, this solution does the reading and matching automatically - accurately, affordably, and at scale. The result is a finance function that spends its time on decisions, not data entry.
We build intelligent OCR-plus-LLM document solutions that automate extraction, matching, and validation for finance teams - accurate, compliant, and cost-efficient at scale.