Maritime & Shipping
AI for fleet operations that cuts fuel costs, port delays, and compliance exposure
You get voyage optimisation agents, predictive maintenance, port logistics intelligence, and IMO compliance automation. All running on your cloud, connected to your existing fleet systems, with governance built in.
Built on your AWS, Azure or GCP tenant. IMO-ready. Flag-state compliant.
Proof of concept
To measurable ROI
AWS, Azure or GCP
Fuel, port time, and compliance are the three margin killers in shipping. AI addresses all three.
Your fleet generates terabytes of AIS data, engine telemetry, weather feeds, and port schedules every week. Most of it sits unused. The carriers that convert this data into routing decisions, maintenance schedules, and compliance documentation are the ones reducing cost per TEU while their competitors absorb it.
We build AI agents that connect your vessel data to your operational decisions from voyage optimisation and predictive maintenance to port call scheduling and IMO 2023 CII compliance, with audit trails your flag state and P&I club can verify.
fuel savings reported by AI-optimised voyage routing (McKinsey, 2024)
average annual saving per vessel from predictive maintenance (BCG X, 2023)
reduction in port idle time with AI berth scheduling (Deloitte Insights, 2024)
of shipping firms report CII compliance as top operational risk (McKinsey, 2024)
From vessel sensor to compliance record, in one workflow
We connect your AIS feeds, engine telemetry, port management systems, weather APIs, and ERP to AI agents that optimise voyages, predict equipment failures, schedule port calls, and generate the CII and EEXI documentation your flag state and classification society require
AIS, IoT sensors, ERP, port systems and weather
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Voyage reports
- Compliance records
- Fleet analytics
What we deliver for maritime and shipping enterprises
From voyage planning to port operations, governed, auditable, enterprise-ready
Voyage route optimisation
Generate fuel-optimal routes for every voyage automatically.
ROI
Process
Aggregate voyage data
Weather forecasts, current patterns, port congestion, charter constraints, and vessel performance data ingested in real time
Model optimal routes
AI calculates fuel-optimal routes accounting for weather windows, ECA zones, speed profiles, and arrival time requirements
Deliver voyage plans
Recommended routes with fuel estimates, ETA projections, and risk assessments pushed to bridge systems and operations desks
Sources
Originally published: McKinsey QuantumBlack, 'AI in Shipping Operations' (2024); BCG X, 'Digital Shipping' (2023), reproduced for illustrative purposes
Predictive maintenance
Predict component failures before unplanned drydock events.
ROI
Process
Collect equipment data
Engine telemetry, vibration sensors, lubrication analysis, and maintenance history streamed from vessel systems
Predict failures
ML models identify degradation patterns and forecast remaining useful life for critical components
Schedule maintenance
Service recommendations aligned to next port call, spare part availability, and drydock windows
Sources
Originally published: McKinsey QuantumBlack, 'Predictive Maintenance in Maritime' (2024); Deloitte Insights, 'Smart Shipping' (2023), reproduced for illustrative purposes
Port call optimisation
Coordinate arrivals and berths to cut port idle time.
ROI
Process
Integrate port data
Berth availability, tide windows, cargo readiness, and terminal capacity data aggregated from port systems
Optimise scheduling
AI models coordinate arrival times, berth assignments, and cargo sequences to minimise waiting time
Coordinate stakeholders
Automated notifications to terminal operators, agents, and vessel crews with optimised schedules
Sources
Originally published: BCG X, 'Port Operations Analytics' (2024); McKinsey QuantumBlack, 'Smart Ports' (2023), reproduced for illustrative purposes
CII and EEXI compliance automation
Calculate CII and EEXI scores with voyage-level recommendations.
ROI
Process
Track emissions data
Fuel consumption, distance travelled, cargo carried, and operational parameters captured per voyage
Calculate compliance scores
AI computes CII ratings and EEXI scores in real time with projected annual trajectories
Generate compliance reports
Flag-state-ready documentation with corrective action recommendations when ratings deteriorate
Sources
Originally published: McKinsey QuantumBlack, 'Decarbonisation in Shipping' (2024); Deloitte Insights, 'IMO 2023 Compliance' (2023), reproduced for illustrative purposes
Fuel consumption analytics
Pinpoint factors driving excess fuel burn across your fleet.
ROI
Process
Baseline fuel performance
Historical consumption data normalised against weather, load, speed, and hull condition variables
Identify excess consumption
AI isolates the root causes of fuel overuse: hull fouling, suboptimal trim, engine degradation, or routing
Deliver actionable insights
Vessel-specific recommendations for hull cleaning timing, speed profiles, and operational adjustments
Sources
Originally published: BCG X, 'Fleet Decarbonisation Analytics' (2024); McKinsey QuantumBlack, 'Fuel Optimisation' (2023), reproduced for illustrative purposes
Cargo and container tracking
Track containers from booking to delivery with exception alerts.
ROI
Process
Connect tracking data
Container IoT sensors, terminal operating systems, and carrier APIs integrated into unified visibility platform
Detect exceptions
AI flags delays, temperature deviations, route anomalies, and customs holds in real time
Automate notifications
Proactive customer and operations alerts with root cause analysis and revised ETAs
Sources
Originally published: AWS Case Study, Maersk; McKinsey QuantumBlack, 'Container Logistics AI' (2024), reproduced for illustrative purposes
Trade document processing
Extract and reconcile shipping documents in minutes, not days.
ROI
Process
Ingest documents
Bills of lading, customs forms, letters of credit, and certificates of origin captured from email, EDI, and portals
Extract and validate
AI extracts key fields, cross-references against booking data, and flags discrepancies for review
Reconcile and route
Validated documents matched to shipments and routed to customs brokers, banks, and compliance teams
Sources
Originally published: Deloitte Insights, 'Trade Finance Automation' (2024); BCG X, 'Digital Trade Documents' (2023), reproduced for illustrative purposes
SVP, Fleet Operations Technology, Wärtsilä
Esa Henttinen
"Azure AI gives us the ability to process engine telemetry from thousands of vessels simultaneously. We can predict component failures weeks before they happen, schedule maintenance at the next port of call, and avoid the unplanned drydock events that cost our customers millions per incident"
Originally published: Microsoft Customer Stories — Wärtsilä — reproduced for illustrative purposes
How we work in maritime and shipping environments
Maritime AI is only valuable when it connects to the data your fleet already generates. We start by mapping your existing sources: AIS feeds, engine telemetry, port management systems, weather APIs, and ERP. Then we identify the one use case where AI will create the most immediate, measurable cost reduction.
Everything we build runs on your cloud tenant. Your vessel data, your operational records, your compliance documentation. None of it goes to a third-party platform. Your flag state, classification society, and P&I club can audit the system directly.
01
Data and use case mapping
We assess your existing fleet data sources, identify the highest-value AI use case, and map it to your compliance and operational requirements.
02
30-day proof of concept
A working AI agent on your cloud, connected to your vessel or port data, with demonstrable output in 30 days.
03
Governance by design
Audit trails, compliance records, and explainable outputs built in, structured to meet flag state and classification society requirements.
04
Scale in 12-18 weeks
From one validated workflow to measurable operational ROI across your fleet or port operation.
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