Fordel StudiosIntelligent Document Routing for Government Services
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
Paper-based application processing with 15-day average turnaround from submission to department assignment. Documents misrouted 18% of the time, creating rework cycles. No digital record of document status or processing history.
Government application forms are among the most structurally varied documents in any domain. Applications span land registration, business licensing, social welfare, health certifications, and infrastructure permits — each with different form layouts, terminology, and processing requirements. Many incoming documents are third-party-submitted forms with handwritten sections, non-standard layouts, or poor scan quality. The misrouting problem was driven partly by human inconsistency and partly by ambiguous cases where the correct department assignment depended on fine-grained content analysis rather than document type alone. The integration constraint was significant: each of the 14 destination departments used a different case management system with a different API or file-drop intake mechanism, requiring 14 separate integration adapters. Audit trail requirements were stricter than commercial deployments: every routing decision had to be fully documented and reversible.
How we
built it
- 01
Mapped the full classification taxonomy of document types the department processed — 47 distinct categories across 6 departments — before designing the model, to ensure the classification schema matched the actual routing decision rather than a simplified version of it.
- 02
Built a multi-stage processing pipeline: scan/OCR, document classification, data extraction, and routing recommendation — with each stage producing confidence scores that allowed the system to flag uncertain cases for human review rather than routing everything automatically.
- 03
Designed the human review interface to show the model's classification, confidence score, and extracted key data fields — giving reviewers a starting point rather than requiring them to re-read the full document.
- 04
Created a feedback loop where human corrections to model classifications fed back into a retraining pipeline, allowing the model to improve on the specific document types and edge cases that appeared in that department's actual workload.
This engagement digitized and automated the intake layer of a citizen services application processing operation. The system handles incoming applications in paper and scanned-image form, runs them through an OCR and classification pipeline, extracts applicant identifiers and application metadata, determines the correct destination department and priority level, and inserts them into the appropriate digital workflow queue. The destination departments retain their existing case management systems; the routing layer integrates via API adapters, with a MinIO object store holding the digitized document images and a PostgreSQL database holding the routing records and processing history. Average turnaround from document receipt to department queue entry is 3.2 days, down from 15 days.
What we
delivered
OCR and classification pipeline that digitizes incoming applications, classifies them by document type and destination department, and routes them to the correct workflow queue automatically. Paper documents are digitized at intake; all downstream processing is digital.
Measurable
outcomes
- Auto-classification rate reached 87% of submitted documents, reducing manual triage from the primary review task to an exception-handling function.
- Average turnaround from submission to department assignment dropped from 15 days to 3.2 days — the majority of the improvement coming from eliminating the manual classification backlog.
- Misrouting rate fell from 18% to 2.1%, eliminating the majority of rework cycles that had been consuming staff time and delaying applicants.
“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 DepartmentReady to build
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