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AI USE CASE

Government Document Classification and Routing

Automatically classify, extract, and route government documents to cut processing backlogs.

Typical budget
€30K–€150K
Time to value
10 weeks
Effort
8–20 weeks
Monthly ongoing
€2K–€6K
Minimum data maturity
basic
Technical prerequisite
some engineering
Industries
Cross-industry, Professional Services
AI type
nlp, computer vision

What it is

Combining NLP and computer vision, this system reads incoming government documents—forms, applications, correspondence—classifies them by type, extracts key data fields, and routes each to the correct department or workflow. Typical deployments reduce manual sorting effort by 60–80% and cut document handling time from days to hours. Data extraction accuracy above 90% is achievable on standardised form types, reducing downstream errors and re-work.

Data you need

A labelled corpus of historical documents covering the main document types the organisation receives, plus digitised or scannable incoming documents.

Required systems

  • erp
  • data warehouse

Why it works

  • Start with the two or three highest-volume document types to demonstrate early ROI before expanding coverage.
  • Involve frontline administrative staff in validating classification labels and routing rules from the outset.
  • Build a human review queue for low-confidence predictions rather than forcing automated decisions on ambiguous documents.
  • Establish a continuous retraining pipeline to handle new document types and evolving layouts.

How this goes wrong

  • Poor OCR quality on low-resolution scans leads to misclassification and data extraction errors.
  • Insufficient labelled training data for rare document types results in low accuracy on edge cases.
  • Routing logic not aligned with actual administrative workflows causes downstream bottlenecks rather than resolving them.
  • Staff resistance to automated routing without a clear human-in-the-loop review process erodes adoption.

When NOT to do this

Avoid this if the organisation cannot provide at least a few hundred labelled examples per document type — without them, the classifier will not generalise and the project will stall in an endless data-labelling phase.

Vendors to consider

Sources

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