Product
Nautilus ML at a glance
"Anything one man can imagine, other men can make real." — Jules Verne
AI-assured subsea engineering from concept studies to live offshore operations.
1. What Nautilus ML does
Nautilus ML turns complex subsea analysis into a steady, real-time signal you can act on.
It replaces months of scattered simulations and manual assurance with a single, consistent workflow that supports:
- Early-stage feasibility and route planning
- Detailed design and verification
- Operational planning and weather-window decisions
- Real-time support during offshore campaigns
2. Who it's for
- Cable installation contractors and vessel operators
- Developers and owners (offshore wind, interconnectors, O&G, defense)
- Engineering houses and consultants
- Insurers / class / certification stakeholders who must sign off on risk
3. What you get
- Faster decisions – Real-time expectations instead of waiting on new studies.
- Clearer risk picture – One place where assumptions, limits, and safety margins are explicit.
- More operating days – Confidence to use real-world conditions instead of over-conservative scenarios.
- Defensible documentation – A clear trail you can take into internal review, partners, and regulators.
4. Where it fits in your workflow
- Nautilus ML doesn't replace your engineers or tools, it connects them.
- It consumes existing studies and models, and produces outputs that slot into today's reports and decision gates.
5. Infrastructure requirements
- Compute-intensive platform – Combines large-scale simulation execution with real-time, low-latency inference for subsea engineering decisions.
- Dual workload architecture – Runs extensive simulation campaigns across high-dimensional state spaces while delivering sub-second inference for time-critical operations, requiring robust scheduling and repeatable execution.
- Elastic cloud infrastructure – Requires CPU- and GPU-backed compute, high parallelism, and reliable orchestration to scale from validation to production deployments and to support concurrent projects and operational campaigns.
- Persistent engineering artifacts – Generates and retains large volumes of structured simulation outputs, model versions, assumptions, and audit trails that must be stored, indexed, and queried across projects and over time.