Client context
A vessel owner preparing for CII and charter-party scrutiny needed a credible emissions baseline before deploying advanced voyage optimisation software.
Problem
Historical datasets showed inconsistency between logged fuel use and actual shaft power
Environmental KPIs varied significantly between sister vessels
Risk of deploying AI optimisation on biased or invalidated inputs
Equitus scope
Reconciliation of onboard sensor data with first-principles energy balance models
Statistical outlier detection tied back to physical causation (windage, added resistance, operational transients)
Independent verification framework aligned with IMO DCS / CII logic, but richer in causality
Delivered a “digital trust layer” beneath optimisation algorithms
Outcome
Corrected baseline errors of 3–5% in fuel attribution
Reduced false optimisation signals caused by environmental noise
Enabled confident roll-out of AI-based routing and speed optimisation
Achieved 4–6% additional fuel savings without compromising ETA or safety margins
What's in it for you
Directly addresses the data quality problem that limits AI performance
Positions independent engineering validation as an enabler of better machine learning outcomes
Aligns perfectly with operators' value proposition of trusted performance analytics
We endeavour to answer all enquiries within 24 hours on business days. We are happy to answer your questions.