Seanergy reports 8-12% fuel savings through use of DeepSea voyage optimisation


AI-led maritime technology company DeepSea Technologies has partnered with Seanergy Maritime Holdings Corp. to develop, test, and improve Pythia, the world’s first weather routing and voyage optimisation platform tailored to the exact performance of vessels, under all conditions.

The platform has allowed Seanergy’s Capesize vessels to achieve a reduction of fuel consumption of up to 12%, with average fuel savings of 8%, as recorded over a series of voyages during the first four months of 2021.

Seanergy and DeepSea have worked closely on the development of Pythia with the aim to build a unique performance routing tool tailored to the needs of top-tier shipping companies. Pythia is an industry-first next generation weather routing and voyage optimisation platform which uses AI-based performance models – based on highly detailed real-time data – to analyse 19 different parameters. The AI models accurately track how a vessel performs over time under any conditions, including those related to the weather and the state of the vessel – such as the fouling levels.

As a result of using real-time data, Pythia obtains a highly accurate understanding of the vessel and develops dynamic, tailor-made performance models for each ship, which are then used to determine the optimum routes, speeds, and trims for minimum fuel consumption.

Commenting on the announcement, Roberto Coustas, Co-founder and CEO of DeepSea Technologies, said: “Our great work with our partners and friends at Seanergy goes back several years, and now it’s fantastic to be able to take the traditional service of weather routing one step further by collaborating side by side with them. Together, we have managed to evolve weather routing into a true performance routing solution, that adapts to each individual company’s objectives.”

Stamatis Tsantanis, CEO of Seanergy Maritime Holdings Corp., added: “Our partnership with DeepSea has been incredibly powerful in granting us a greater and more accurate understanding of our fleet performance, and in informing our decision-making process to achieve higher efficiency standards.”