Accenture Public Sector European Union

Directorate-General for Maritime Affairs and Fisheries (DG MARE)

Application Refactoring

DG MARE needed to modernize siloed fisheries applications and thousands of legacy business rules quickly enough to support better maritime enforcement, but manual refactoring would have taken too long and delayed the broader cloud transformation.

12,000 person-days
90% accuracy
just one minute
more than 2,000 business rules

Reported impact

Business outcome

DG MARE sharply reduced the time needed to modernize legacy rules, improved the foundation for maritime analytics, and created a more agile public-service platform for fisheries oversight and data-driven enforcement.

  • Productivity Improvement
  • Scalability

Solution

Accenture is helping DG MARE migrate fisheries and maritime applications to AWS and uses a generative AI refactoring tool to accelerate one of the hardest parts of the program: rewriting more than 2,000 legacy business rules for the cloud. DG MARE's applications manage vessel locations, fish-catch declarations, and related regulatory data from EU member states and third countries. The target state is Ocean Store, a unified platform that consolidates previously siloed application data for analysis and transparency. To speed the move, Accenture built a large-language-model-based tool that reads DG MARE's existing rule logic, codebase, and documentation, then generates cloud-ready code for the business-rule engine. Developers validate and correct the generated output where needed, while the unified platform creates room for additional analytics such as detecting unusual vessel behavior. The combination of cloud migration, data unification, and LLM-assisted refactoring gives DG MARE a faster path to modern enforcement technology.

Data flow

Fishing-vessel and catch-declaration data from member states feeds DG MARE applications that are being consolidated into Ocean Store. In parallel, legacy business rules and documentation are passed to the LLM-based refactoring tool, which generates cloud-ready code that developers validate before the new rule engine runs on AWS.

Solution architecture

5 components · 3 layers
  1. Compute
    • generative AI tool Uses DG MARE code and documentation to generate cloud-ready replacements for legacy rules.
    • large language models Interpret existing rule logic so the refactoring tool can produce new code quickly.
    • AWS cloud platform Hosts the target application environment after the migration from legacy systems.
  2. Storage
    • Ocean Store Acts as the unified data platform that consolidates previously siloed fisheries application data.
  3. Orchestration
    • business rule engine Executes the fisheries-validation logic that must be rewritten for the cloud platform.

Architecture clues

  • The generative AI tool uses DG MARE's existing codebase and documentation as grounding context.
  • The target platform is called Ocean Store.
  • Unified fisheries data is intended to support future analytics on suspicious vessel behavior.

Evidence from the source

"The result of the cloud migration will be a single platform, called Ocean Store, that unifies and integrates all the data from the legacy applications."
"it uses DG MARE's existing codebase and documentation, enabling high-quality code"
"the tool can refactor each rule in just one minute with 90% accuracy"
"we developed a generative AI tool that uses large language models to understand the rules' logic and automatically generate new, cloud-ready code"
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability19/25
    9 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Accenture

    Safeguarding Maritime with AI Algorithms | Case Study | Accenture

    First-party
    https://www.accenture.com/en/case-studies/public-service/harnessing-ai-safeguard-fisheries
    Source type
    Vendor-published customer story
    Published
    Feb 8, 2024
    Supports
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 8, 2024 · Accenture · First-party
    Open date source
Not separately sourced Customer, Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.