LLM-powered document extraction replacing 3 full-time roles.
A financial services firm was drowning in manual data entry across invoices, contracts, and compliance forms. We built an AI-powered extraction pipeline that processes documents with 97% accuracy and cut processing costs by $200K annually.
The short version.
A mid-size financial services company was processing 2,000+ documents per month manually: invoices, vendor contracts, compliance forms, and insurance claims. Three full-time employees spent their days copy-pasting data from PDFs into spreadsheets and their ERP system.
We built an LLM-powered document extraction pipeline as part of our AI development services that automatically classifies documents, extracts structured data with 97% accuracy, validates against business rules, and pushes clean data directly into their existing systems. Processing time dropped from 15 minutes per document to under 30 seconds.
Manual data entry at scale doesn’t scale.
The company handled documents from 200+ vendors and clients, each with different formats, layouts, and field names. Previous attempts at automation had failed because traditional OCR couldn’t handle the variety:
- Format chaos: invoices from 200+ vendors in completely different layouts; no two looked alike
- Error-prone manual entry: 8% error rate on manually entered data, causing reconciliation issues downstream
- Processing bottleneck: month-end closings delayed by 3-5 days waiting for document backlog to clear
- Failed OCR attempts: two previous OCR implementations abandoned; couldn’t handle handwritten notes, stamps, or non-standard layouts
- Compliance risk: manual processes meant inconsistent data validation; audit findings were increasing
They needed a system that could understand document context, not just read characters, but comprehend what each field means regardless of layout. Our AI integration framework guided the decision to go LLM-first.
LLM-first extraction with human-in-the-loop.
We designed a pipeline that uses large language models as the primary extraction engine, with traditional OCR as a preprocessing step and human review for edge cases:
- Document classification: automatic categorization into 12 document types using a fine-tuned classifier (invoice, contract, compliance form, etc.)
- Multi-modal extraction: GPT-4 Vision processes document images directly, understanding layout and context without rigid templates
- Schema-driven validation: extracted data validated against document-type-specific schemas with business rules (date ranges, amount thresholds, required fields)
- Confidence scoring: each extracted field gets a confidence score; low-confidence fields routed to human review queue
- ERP integration: validated data pushed directly into their existing ERP and accounting systems via API
- Continuous learning: human corrections feed back into the system, improving accuracy over time
Event-driven pipeline with confidence routing.
Documents enter the system via email, file upload, or API. Each document flows through classification, extraction, validation, and integration stages, with low-confidence results routed to a human review dashboard.
Event-driven | 30sec per document, 97% accuracy
The system processes documents asynchronously via a task queue. High-confidence extractions (above 95%) flow straight to ERP integration. Everything below goes to the review dashboard where a single operator can process 10× more documents than before — they only review flagged fields, not entire documents.
“We went from three people spending their entire day on data entry to one person spending an hour reviewing edge cases. The accuracy is actually better than manual entry, and our month-end close is now 3 days faster.”
CFO, Financial services firm
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