From data to decisions, without the complexity
"If we are to achieve things never before accomplished we must employ methods never before attempted." — Francis Bacon, Novum Organum
We connect your existing engineering work, build an assured AI model around it, and deliver a simple way to use it in day-to-day decisions.
Step 1 – Understand your project and decisions
We start with the questions you need answered:
- Can we operate tomorrow? How about today?
- What if the weather shifts?
- Are we still within safe tension and clearance limits?
Together we map the decisions, constraints, and safety margins that matter for your operations.
Step 2 – Bring your existing work into one place
We leverage what you already trust.
We jointly consider:
- Existing simulations and analyses
- Vessel and equipment data
- Environmental data and design criteria
- Operational experience and engineering judgement
Step 3 – Build and validate the AI model
We work with you.
- We train an AI model to reproduce the behaviour of your trusted engineering analyses across a huge range of conditions.
- We systematically compare its predictions against your reference methods and define where it is valid and where it is not.
- We continue to validate the model against new data as you run more projects.
"And damned be him that first cries "Hold! Enough!"" — William Shakespeare, Macbeth, Act 5, Scene 8
We document limits, assumptions, and confidence levels.
Step 4 – Deploy into your day-to-day decisions
Your team gets a clear interface that answers:
- Is this scenario safe?
- How far are we from the limit?
- What happens if conditions change?
Outputs can be used in planning tools, dashboards, or live offshore support, depending on your needs.
5. Step 5 – Monitor, learn, and improve
As you run more projects, Nautilus ML learns from reality. We use new data to continuously tighten confidence, expand valid ranges, and keep the model aligned with how the field actually behaves.