Automating application processing in a public body
A mid-sized public body receives thousands of requests and applications every month in paper, PDF or PEC format. Each case requires manual completeness checks, requirement verification, protocol registration and preparation of a summary for the officer: long handling times, growing backlog and strong dependence on individual operator experience.
Analysis
End-to-end mapping of the case-processing workflow, analysis of document types, identification of repetitive steps and definition of where AI can support without replacing administrative responsibility. Ambiguous cases, exceptions and thresholds for human review are also identified.
Solution
A Document Intelligence system that classifies incoming cases, extracts relevant data, flags missing documents, prepares a structured summary for the operator and links every piece of information to its source. The assistant does not decide the outcome: it accelerates checking, reading and case preparation.
Architecture
OCR and document classification, data extraction pipeline, internal knowledge base with regulations and procedures, integration with protocol and document repositories, audit trail for consulted sources and on-premise or hybrid deployment to meet security and data-residency constraints.
Implementation
Rolled out in two phases: first on a low-risk case type, then progressively extended after validation with operators. Training covers system limits, exception handling and procedures for correcting or confirming extracted information.
Duration
Eight-week pilot on a low-risk case type, progressively extended over 3-4 months.
Expected results
- Average pre-processing time: in projects of this kind it typically goes from 20-25 to 10-15 minutes per case
- Backlog: on a homogeneous, high-volume case type the reduction can reach around 90% in the first months after rollout, because the bottleneck is the repetitive document check; on heterogeneous cases or those with many exceptions the margin is appreciably smaller
- Completeness errors detected in later phases of the procedure: expected reduction in the order of 50-60%
- Staff freed from repetitive checks to focus on higher-responsibility work
ROI: ROI is measured through recovered operating hours, backlog reduction, lower rework and improved response times perceived by citizens and businesses. The figures above are expected orders of magnitude: on a real project they have to be agreed before kickoff and measured against the starting baseline.
Post go-live: Quarterly accuracy review with operators, knowledge-base updates on every regulatory change, support SLA for exception handling.