Intelligent Document Processing System
A document extraction pipeline for ClearLedger that turns incoming financial statements into structured, validated data.
Customer: ClearLedger (demo)
Read the full case studyClearLedger's reconciliation team was manually re-keying data from incoming financial statements and supporting documents. We built an extraction pipeline that reads these documents, extracts structured data, validates it against business rules, and routes anything uncertain to a human reviewer.
Challenge
ClearLedger's operations team spent significant time manually transcribing data from incoming PDF statements into their reconciliation system — slow, error-prone work that also created a processing backlog during high-volume periods.
Solution
We built a document processing pipeline that extracts structured fields from incoming statements using a combination of layout-aware parsing and model-based extraction, validates extracted values against expected formats and cross-field consistency rules, and flags any low-confidence or rule-violating extraction for human review before it reaches the reconciliation system. Reviewers see the original document alongside the extracted data for fast verification.
Architecture
Incoming documents land in a processing queue, are parsed page-by-page, and pass through an extraction step that combines structured layout detection with model-assisted field extraction for less consistently formatted documents. A validation layer checks extracted values against expected ranges, formats, and cross-field relationships (e.g., line items summing to a stated total). Documents passing validation flow directly into ClearLedger's reconciliation system via API; flagged documents appear in a review queue with the source document and extracted fields side by side.
Key features
Layout-aware extraction
Combines structured parsing with model-based extraction for varied document formats.
Cross-field validation
Extracted data is checked against business rules before acceptance.
Human review queue
Low-confidence extractions are routed for review with source and extracted data side by side.
Direct reconciliation integration
Validated data flows automatically into the existing reconciliation system.
Integrations
Reconciliation system
Validated extracted data is posted directly via API.
Document intake
Automated ingestion from existing email and upload channels.
Representative outcomes
Reduced manual transcription
Most incoming statements are processed without manual re-keying.
Faster processing during peak volume
The backlog that used to build up during high-volume periods no longer accumulates the same way.
Errors caught before reconciliation
Validation rules catch inconsistencies before they reach downstream systems.
Technologies
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