Rapid Progress in Maritime AI

OceanAI

Rapid Progress in Maritime AI

From enterprise multi-agent orchestration to AI-driven vessel inspection, OceanAI’s latest releases push the boundaries of what software can think, test, and decide autonomously.

12 mins read

Featuring

Ravi Shankar

Ravi Shankar

Head of AI, OceanAI (MariApps Group)

OceanAI is the artificial intelligence division of the MariApps Group, developing maritime AI solutions for shipping and enterprise operations. These products were built in the operational reality of running fleets on maritime ERP software smartPAL — then engineered to hold up anywhere complex operations need to move faster than their backlog.

Software has always been good at storing what we know. OceanAI’s 2026 product suite is about making the process easier: making enterprise AI software act on what it knows — without waiting to be asked, without requiring an expert at the keyboard, and without breaking when the world changes around it.

Across five new releases, OceanAI delivers tools built for the people who actually run complex operations: fleet superintendents, QA leads, finance managers, and enterprise teams who can’t afford to work on ten systems together just to get one answer. Each product is different, but the underlying ambition is the same — intelligence that works at the pace of the business, not the pace of the IT backlog.

Enterprise AI

EagleAI — The AI That Thinks as a Team

From intent to intelligence to execution.

Most enterprise AI tools give you one smart response. EagleAI gives you a coordinated workforce through a multi-agent AI platform. The platform breaks down business challenges into specialized agents — analysis, data retrieval, execution, domain reasoning, synthesis — and runs them in parallely, resolving dependencies automatically and combining their outputs into decisions that are ready to act on.

The result isn’t a chatbot with a wider vocabulary. It’s an intelligent operating layer that sits across every system, understands organizational context, and transforms natural language intent into multi-step workflows without needing a prompt engineer in the room.

Here is how the orchestration works in practice:

  1. 1
    Natural Language InputUser describes the business problem or goal.
  2. 2
    Multi-Agent PlanningEagleAI breaks it into coordinated agent tasks.
  3. 3
    Parallel ExecutionAgents run simultaneously, sharing intermediate outputs.
  4. 4
    Intelligence SynthesisResults are merged into a single structured output.
  5. 5
    Business-Ready DeliveryReports, dashboards, workflows, or executable actions.

 

Supporting this are four foundational capabilities:

  1. A Prompt Library Governance system that ensures consistent agent behavior across every department.
  2. An Enterprise Knowledge Base that grounds agents in your own documents, workflows, and data rather than generic training.
  3. Event-Driven Automation that triggers workflows from emails, schedules, and system events.
  4. Multi-Source Data Reasoning that connects across SQL databases, REST APIs, documents, and vector knowledge simultaneously.
Key outcomes
  • Compressed decision cycles
  • Unified intelligence layer
  • Scalable AI workforce
  • Reduced operational overhead
  • Continuous organizational learning
QA Automation

Kriya AI — Tests That Write Themselves, Then Correct Themselves

From user interaction to intelligent automation.

The most expensive thing in QA isn’t the tooling — it’s the time spent writing, maintaining, and rewriting scripts, by humans, every time a UI changes. Kriya AI is an AI test automation solution eliminates that loop. Feed it a screen recording or describe a scenario in plain language, and it generates structured test cases, automation scripts, and validation logic automatically.

When the application changes, Kriya AI adapts. Its self-healing engine detects broken locators, modified flows, and UI changes, repairing test scripts automatically so releases don’t stall for QA to catch up. The system also uses historical defects and past test cases to ensure complete, context-aware coverage every cycle.

Key outcomes
  • Eliminate manual script writing
  • Faster release cycles
  • Higher test coverage
  • Reduced maintenance effort
  • Unified QA platform
QA Intelligence

QA Agent — QA That Learns From Mistakes

From requirements to real-world failures.

The gap in traditional QA is this: test plans are written from requirements, but bugs come from reality. QA Agent closes that gap by operating on both sides simultaneously — ingesting FSDs, FRDs, and user manuals to understand what the product should do, and processing Jira tickets, support emails, and incident logs to understand what it failed to do.

Every resolved ticket becomes a new test case. Every document update expands the knowledge base. The system doesn’t just generate QA artifacts — it builds a living, self-improving intelligence library that gets more accurate with every release cycle. Ticket completeness is validated automatically, similar past issues are surfaced, and edge cases missed in manual QA are identified by combining design-time requirements with runtime failure patterns.

Key outcomes
  • Design and reality coverage combined
  • Faster delivery cycles
  • Self-improving QA system
  • Reduced triage overhead
  • Enterprise-ready consistency
Maritime · smartPAL

InspectAI — Smarter Inspections, Before the Problem Exists

Enhanced AI-powered vessel inspections through AI-driven data analysis.

The best inspection is one that finds a problem before it becomes a casualty. InspectAI gives superintendents that edge — generating customized question sets and checklists derived from a vessel’s own historical data, past inspection patterns, and risk profile, rather than from a generic regulatory template.

During inspections, the tool provides context-aware prompts in real time, guiding teams toward the areas where risk is highest and compliance gaps are most likely to appear. The result is inspections that are sharper, faster, and more defensible — with a proactive stance built in from the start. The module is native to the smartPAL ecosystem, integrating seamlessly within existing maritime workflows.

Key outcomes
  • Enhanced vessel safety
  • Improved regulatory compliance
  • Increased inspection efficiency
  • Proactive risk management
  • Scalable across fleet
Finance · smartPAL

FinBot — Ask Your Financial Data a Question. Get an Answer.

Your AI financial assistant, built for maritime.

FinBot makes financial data conversational — users ask questions in plain language and receive instant, accurate responses with interactive charts, tables, and visualizations, all drawn from live smartPAL financial data.

Beyond on-demand queries, FinBot automates recurring reporting — scheduling and generating PDF reports that adapt as business needs change. Contextual memory means follow-up questions flow naturally, turning a single query into a full financial exploration without starting from scratch each time. The result is faster decisions, less dependency on specialist support, and financial visibility that is always current.

Key outcomes
  • Instant financial answers
  • Automated report generation
  • Reduced analyst dependency
  • Contextual follow-up queries
  • Latest financial visibility
MariApps team presenting OceanAI, showcasing maritime artificial intelligence, AI-powered maritime solutions, and enterprise AI software for shipping

Intelligence Across Every Layer

Five products. One ambition. Intelligence that works at the pace of the business — not the pace of the IT backlog.

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