Ukrainian prosecutors office AI

Our client was the Office of the Prosecutor General of Ukraine, supported by the European Union Advisory Mission Ukraine, responsible for managing criminal proceedings and overseeing pre-trial investigations across the country.

Criminal proceedings were largely managed in paper form, limiting access, slowing down collaboration, and making it difficult to analyze large volumes of information. At the same time, multiple information systems operated independently, creating inefficiencies and increasing workload for users.

The lack of unified processes and analytical tools further limited the ability to manage cases consistently and effectively, particularly in the context of large-scale investigations.

Civitta supported the development of a comprehensive e-Case Management System (ECMS) under the SMEREKA initiative.

• Phase 1: Business process analysis and gap identification

We started with a comprehensive analysis of pre-trial investigation processes, mapping both as-is and to-be workflows using BPMN notation. This allowed us to identify operational inefficiencies, process fragmentation, and key improvement opportunities.

Based on this analysis, we developed recommendations for process standardization, workflow optimization, and required regulatory adjustments.

• Phase 2: System concept and architecture design

Based on the identified needs, we designed a unified digital platform integrating case management, document processing, collaboration tools, analytics and reporting.

The architecture was defined to include modular system components, integration with national and international systems (courts, law enforcement agencies, ICC, Europol), and a centralized data environment based on the Data Lake concept.

• Phase 3: Functional and analytical system design (including AI-enabling layer)
  

We translated the concept into detailed functional design covering case and document management, investigation workflows, collaboration tools, and analytics. This included digitization of criminal proceedings, OCR-based document recognition, search and tagging, task and deadline management, and reporting dashboards.

In parallel, we defined supporting AI-enabling components such as OCR, NLP-based text processing, entity extraction, relationship graphs, and pattern detection to support more structured and data-driven investigations.

• Phase 4: Implementation design and delivery planning
  

Finally, we developed a complete implementation package, including functional requirements, technical specifications (TSD), system architecture documentation, and service level definitions.

A phased implementation roadmap was defined to support modular rollout, and procurement-ready documentation was prepared to enable transition into development and deployment.

The project delivered a complete design and specification for the SMEREKA system and established a foundation for its implementation.

The outputs include redesigned processes, system architecture, and technical specifications required for procurement and development. Implementation and measurable impact depend on subsequent project phases.

This case demonstrates that effective digital transformation in the public sector requires combining process redesign, system integration, and gradual introduction of analytical capabilities. AI plays a supporting role by enabling more effective use of data once it is digitized and structured.