DeepSea Technologies Review: From raw vessel data to real fuel cuts
January 29, 2026
DeepSea is one of the clearest examples of “AI as a fuel-saving tool” in shipping: they build vessel specific digital twins and optimisation engines so you can plan voyages, set speeds and run engines in a way that cuts fuel and CO₂ without asking crews to become data scientists overnight. For fleets trying to improve CII and charter performance using real operating data rather than averages, this is very much in play.
DeepSea Technologies • Headquarters
24 Stadiou Street, 105 64 Athens, Greece
How shipowners and operators benefit
DeepSea builds vessel specific AI models and control tools so you can cut fuel and emissions using the data you already generate on board.
- Turning raw data into vessel specific models: High frequency and low frequency data are used to build digital twins for each ship, so recommendations reflect real hull, propeller and engine behaviour rather than generic curves.
- Optimising routes, speeds and ETAs with Pythia: The Pythia voyage planning platform looks at weather, market drivers and up to date performance profiles to suggest speed and routing choices that target both TCE and CII outcomes.
- Tracking hull and engine performance over time: Vessel optimisation tools like Cassandra highlight performance drift, fouling and engine inefficiencies, which helps owners decide when to clean, repair or adjust operating profiles.
- Automating engine speed control with HyperPilot: For owners who are ready, HyperPilot can control engine RPM directly according to an AI plan, so crews do not have to constantly recalculate optimal speeds during the voyage.
- Supporting compliance and reporting: Better visibility on fuel, speed profiles and emissions helps with CII tracking, ESG reporting and internal performance dashboards without having to build a data stack from scratch.
- Benefiting from Nabtesco backing: DeepSea operates as an AI research and product centre within the Nabtesco Group, which gives it industrial backing while keeping the focus on maritime optimisation software.
- Applying AI to tramp as well as liner trades: Collaborations with bulk and tanker operators show that the tools are used in tramp segments where schedules are less predictable, not only on fixed liner loops.
- Going deeper on the product set when needed: Technical detail on Pythia, Cassandra, HyperPilot, Neuro and case studies is maintained on the DeepSea site. Visit deepsea.ai .
Notes: This is an operator oriented summary. Actual savings depend on vessel type, trade, data quality, crew engagement and how tightly commercial and technical teams use the recommendations.
Notable mentions and external references
Selected third party stories that show DeepSea’s optimisation tools in real fleets, corporate transactions and decarbonisation coverage.
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Wallenius Wilhelmsen targets up to 10 percent savings Ship & BunkerShip & Bunker reports on Wallenius Wilhelmsen using DeepSea performance software with a goal of up to 10 percent fuel savings across dozens of vessels. Read the Ship & Bunker story .
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Voyage optimisation roll out with G2 Ocean Hellenic Shipping NewsHellenic Shipping News covers DeepSea and G2 Ocean deploying an AI voyage optimisation tool that works from AIS and noon report data, without new onboard hardware. Open the G2 Ocean coverage .
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Greek AI startup acquired to decarbonise fleets The RecursiveThe Recursive explains how Japan based automation company Nabtesco acquired DeepSea to support fleet decarbonisation and to create a centre of excellence for maritime AI. Read The Recursive profile .
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Acquisition note and transaction details MarketScreenerMarketScreener carries a deal note on Nabtesco’s acquisition of DeepSea Technologies, confirming that DeepSea continues as an Athens based AI specialist within the group. View the transaction summary .
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Feature on an Athens AI company making shipping greener 3 Seas Europe3 Seas Europe publishes a narrative feature on DeepSea, quoting a target of around 10 percent efficiency gain when routes and vessel operation are optimised together. Read the 3 Seas Europe article .
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Open innovation case study on the Nabtesco deal Emerald Technology VenturesEmerald Technology Ventures uses the Nabtesco and DeepSea transaction as a case study in corporate venturing and AI enabled maritime routing and performance systems. Open the Emerald case study .
This is not a full credential list. It is a quick set of external touchpoints that show DeepSea’s optimisation stack in real contracts, fleets and decarbonisation coverage.
AI fuel savings sketch
A simple way to see what DeepSea style optimisation could mean in fuel cost and emissions over a contract period, based on your own assumptions.
Inputs
Use main engine consumption at sea, not boiler or port fuel.
Many case studies talk about a band around 4 to 10 percent. Use your own target.
Sketch impact
Annual fuel saved
0 mt
Annual cost saving
$0
Total saving over contract
$0
Saving per vessel per day
$0
| Category | Calculated value |
|---|---|
| Baseline annual fuel use | 0 mt |
| Annual fuel saved with AI optimisation | 0 mt |
| Annual cost saving at given fuel price | $0 |
| Total cost saving over contract term | $0 |
| Average saving per vessel per day at sea | $0 |
| Implied percentage reduction in fuel cost | 0% |
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