NeuralShipper: Generative AI for Ship Design with Shahroz Khan, CEO – Compute Maritime
Barcelona Speaker Series
This excerpt from an interview with Shahroz Khan, CEO of Compute Maritime, previews his presentation at CDFAM Barcelona within the broader context of how AI and machine learning are reshaping engineering design. As computational methods move from automation toward AI-native generation, the conversation highlights NeuralShipper, a generative system for ship design that produces CAD-ready geometry and supports simulation-driven workflows from the earliest stages of development.
Can you introduce Compute Maritime and explain what you’ll be presenting at CDFAM in relation to NeuralShipper and its role in generative AI for ship design?
Compute Maritime is a UK-based deep tech company building the next generation of AI-native design tools for the maritime industry. Our flagship platform, NeuralShipper, is the world’s first generative AI co-pilot specifically developed for ship design, optimisation, and simulation-ready engineering workflows.
At CDFAM, we will be presenting how NeuralShipper introduces a new computational design paradigm for maritime: instead of relying on slow, manual parametric modelling, engineers can generate thousands of high-performance vessel concepts within minutes, directly from a small set of specifications.
What makes this particularly relevant to the CDFAM community is that NeuralShipper is built around a Large Geometric Foundation Model that produces CAD-native outputs, primarily as valid NURBS surfaces, enabling immediate downstream simulation and manufacturability. It is a practical example of how generative AI can transform one of the most conservative engineering sectors while addressing sustainability at scale.
You describe NeuralShipper as a generative co-pilot for maritime systems. What types of inputs does it require, and how are user-defined constraints and performance criteria handled in the design process?
NeuralShipper is designed for early-stage concept exploration, so it requires only minimal inputs to begin generating meaningful design candidates.
Typically, users provide high-level specifications such as vessel type, principal dimensions, displacement targets, speed requirements, operational constraints, and sometimes mission-specific objectives like fuel efficiency or seakeeping priorities.
From there, the platform allows designers to define custom constraints and performance criteria, which are integrated directly into the generation and optimisation loop. This means NeuralShipper does not simply generate random shapes, it generates candidates that satisfy engineering feasibility and are aligned with the designer’s intent.
The result is a human-in-the-loop workflow where AI accelerates exploration, but the naval architect remains in control of design direction and decision-making.
Register to attend CDFAM Computational Design Symposium in Barcelona to connect with Shahroz Khan and other leaders applying AI and machine learning in engineering and architecture. Join the discussion on practical computational design methods, simulation-driven workflows, and the future of AI-augmented design tools.