Maritime

Intelligent Maritime Compliance at Scale

Pole Star Global’s PurpleTRAC platform is a trusted compliance and risk solution for maritime operators worldwide. But as sanctions evolve and data volumes grow, compliance teams face increasing pressure from manual workflows, fragmented alerts, and escalating cognitive burden. Kagen.ai partnered with the PurpleTRAC team to embed domain-specific GenAI capabilities—streamlining screening, accelerating Go/No-Go decisions, and delivering explainable insights directly into operational workflows.

90%
Reduced Manual Work
70%
Accuracy Improved
30%
Faster Compliance Decisions
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The Challenge
PurpleTRAC is relied upon for vessel sanctions screening, dark activity monitoring, and ESG reporting. However, frontline users were increasingly impacted by
Alert Overload & Cognitive Fatigue – Thousands of fragmented alerts per day with limited context
Alert Overload & Cognitive Fatigue – Thousands of fragmented alerts per day with limited context
False Positives & Noise – Time wasted on non-critical flags due to over-sensitive rules
False Positives & Noise – Time wasted on non-critical flags due to over-sensitive rules
Manual Reporting – High effort to compile audits, summaries, and compliance trails
Manual Reporting – High effort to compile audits, summaries, and compliance trails
Reactive Risk Management – Little ability to simulate future compliance risks
Reactive Risk Management – Little ability to simulate future compliance risks
Poor Operational Integration – Insights not embedded into voyage planning or route optimization
Poor Operational Integration – Insights not embedded into voyage planning or route optimization
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Kagen’s Solution
Kagen delivered a multi-pronged GenAI transformation, combining structured prediction models with retrieval-augmented generation (RAG) and LLM summarization:
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Automated Interpretation & Risk Summarization
1
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  • What it does: Converts raw compliance data into explainable summaries and next-step recommendations.
  • Example Output: This vessel has a Warning due to advanced age (42 years) and Liberia flag. No sanctions or dark activity detected. Request updated inspection certificates.
  • Data Used: Vessel metadata, AIS gaps, ownership chain, sanctions watchlists, PSC records, historical voyage behavior.
Predictive Risk & Scenario Modeling
2
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  • What it does: Simulates future compliance risks for proposed voyages and enables trade-off modeling.
  • Example Use Case: Changing route to avoid Strait of Hormuz reduces compliance risk by 40%, increases cost by 15%.
  • Data Used: Historical compliance outcomes, political risk data, charter terms, emissions, port congestion.
Intelligent Alert Prioritization
3
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  • What it does: Categorizes alerts by real impact, severity, and required action—reducing noise.
  • Outcome: Teams focused only on top-tier alerts with clear business/regulatory relevance.
  • Data Used: Historical alert handling data, resolution outcomes, severity metadata.
Dynamic Compliance Assistant
4
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  • What it does: An AI co-pilot guiding teams through screening steps, report generation, and audit prep.
  • Capabilities: Conversational interface for queries, Automated report generation, Built-in SOPs and checklists, RAG-based compliance citations
  • Data Used: SOPs, templates, historical reports, regulatory documentation.
ESG & Emissions Advisory
5
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  • What it does: Merges emissions and sanctions data for holistic ESG-compliance risk recommendations.
  • Example Output: Route A results in 10% lower emissions but enters OFAC scrutiny zones. Recommend Route B for ESG-compliance balance.
  • Data Used: Emissions records (CII, EEXI), voyage benchmarks, regulatory thresholds.
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Status Snapshot: Summarization Engine Output
90%
“CONDOR MAGALLANES presents a moderate-to-high risk profile due to multiple AIS gaps in OFAC zones, frequent STS activities, and recent flag changes. No detentions recorded. Enhanced scrutiny recommended.”
Breakdown
  • AIS Gaps: Multiple in East China Sea
  • STS Transfers: Prolonged, multi-vessel interactions
  • Flag Changes: Recent shift from Panama to Cyprus
  • Inspections: Cleared, but some deficiencies
  • Ownership: Cleared of sanctions
Status Snapshot: Summarization Engine Output
Strategic Alignment with Business Goals
Objective GenAI Enablement Reduce manual effort Summarization, automation, and guided workflows Improve compliance decision speed Alert prioritization and scenario simulation Enhance accuracy and transparency Predictive modeling + explainable summaries Increase trust in automation Human-readable outputs and audit-ready logic Enable partner ecosystem integration Modular architecture and API-ready outputs
From Friction to Flow: The Measurable Impact
≥50%
Reduction in time spent investigating false-positive alerts
≥30%
Acceleration in Go/No-Go compliance decisions
≥20%
Improvement in user trust scores
≥70%
Automation of compliance and ESG reporting workflows
Notable reduction in missed flags and delayed voyage approvals
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