Classify, extract, and route financial documents without manual triage.
A financial operations team receives 500+ documents daily: invoices, bank statements, tax forms, contracts, correspondence, and compliance filings. Staff manually determine type, extract data, and route to the correct queue. Misclassification creates downstream errors — an invoice in the correspondence queue gets delayed; a tax form in the wrong client folder creates compliance risk.
Volumes spike at quarter-end and tax season. Temporary staff require training on types and routing rules. Error rates increase with volume.
The challenge is not OCR — it is classification. The same email attachment might be an invoice, statement, or contract amendment, and routing depends on accurate identification and type-specific extraction.
Three-stage processing. First, classify document type using a multi-class model trained on your taxonomy — not generic categories but yours: "vendor invoice," "client bank statement," "K-1 tax form," "engagement letter."
Second, type-specific extraction. Invoices get vendor, number, amount, due date, line items. Tax forms get taxpayer ID, year, filing type, key figures. Validation rules per type: does invoice total match line items? Is tax ID valid format?
Third, route to correct workflow: invoices to AP, statements to client file, tax forms to prep queue. Low-confidence items route to human verification rather than potentially misrouting.
We build this as a productized deployment: a Python/FastAPI service backed by a LayoutLM model fine-tuned on your labeled document corpus — typically 1,000+ historical samples across your actual document types. Email parsing, portal integrations, and scanner feeds connect via Celery workers with Redis queuing, so ingestion is async and retryable. Extracted fields land in PostgreSQL with Elasticsearch indexing for audit search. Setup takes 3–4 weeks, including model training, integration wiring, and review UI handoff.
Classification trained on your actual document types — not a generic model. Handles 50+ distinct types after fine-tuning on your historical corpus. New types can be added with incremental labeled batches without retraining from scratch.
Each document type has its own extraction template: invoices pull vendor, line items, totals, and due dates; tax forms capture TINs, withholding figures, and filing periods; contracts extract parties, effective dates, and obligation clauses. No one-size-fits-all field mapping.
Extracted data runs through configurable validation rules before it leaves the pipeline — invoice line items must sum to declared totals, date fields must fall within fiscal windows, ID numbers must match expected formats. Failures are flagged with specific error codes, not silently passed through.
High-confidence extractions route automatically to the correct downstream system — ERP, AP queue, contract management, or archival storage. Low-confidence results go to a human review queue with the model's top candidate highlighted. Confidence thresholds and routing rules are configurable per document type.
Anonymized work with hard metrics — NDA-bound, no client names.
87%
Auto-Classification Rate
3.2 days
Avg Turnaround (from 15)
2.1%
Misroute Rate (from 18%)
“The misrouting rate was the metric that mattered internally — every misrouted document created rework cycles that consumed staff time and delayed the original applicant. Getting that from 18% to 2% changed the entire operations picture.”
— Director of Digital Services, Regional Government Department
94%
Classification Accuracy
340ms
Avg Processing Time
78%
Manual Review Reduction
“The context understanding is the part that changed the team's view of AI moderation. It is not just pattern matching — it is understanding that the same phrase can be a policy violation in one context and completely acceptable in another.”
— Trust and Safety Lead, User Content Platform
We custom-build each agent to fit your data, your rules, and your existing systems.
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Fordel Studios