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Proof
Each entry below represents a real environment and a result produced without domain expertise, historical failure data, or retraining.
Why this matters
Most AI systems are trained on failure examples. They need to know what went wrong before they can watch for it. GangoAI never sees the outcome in advance. Every result on this page was produced blind.
Detecting intoxication before anyone else sees it.
Behavioural drift measured against each person’s own baseline. Zero false positives across both days of testing.
Malmesbury Boxing Club · 41 participants · 92.7% accuracy · Innovate UK funded
Flagging fatigue in a live industrial workplace.
Drift detected against individual baseline in real operational conditions. Statistically significant separation between alert and fatigued states.
Applied Automation, Plymouth · Innovate UK funded
Classifying stress from wearable signals.
Physiological signals scored against each person’s own baseline. Strong classification without population-level training.
Wearable sensor study · 84.8% classification accuracy
Tracking patient movement recovery in real time.
Uninjured side establishes the baseline. Injured side is monitored for drift. The algorithm needed no modification from any other domain.
Cliff Villages Physiotherapy · Live deployment
Predicting battery cell end-of-life without being told what failure looks like.
Each cell’s own early behaviour sets the baseline. Drift score tracks degradation continuously. Strong correlation with remaining useful life.
NASA battery cells · 21 cells · mean ρ -0.919
Detecting engine degradation from sensor streams alone.
No failure examples used. Drift from each engine’s own baseline captures the degradation curve from first deviation.
NASA turbofan programme · 100 engines · ρ -0.903
Predicting hard drive failure across a live production fleet.
No failure labels used during scoring. The result exceeded the best published figure on this fleet — which used trained models with labelled failures.
Backblaze production fleet · 9,096 drives · 92.3% precision · 39.5 day average lead time
Warning of wind turbine failure up to 29 days early.
Two independent wind farms. Failure flagged weeks before breakdown. No knowledge of failure modes required.
Wind farms · Portugal and Norway · 15 turbines · 19–29 day lead time
Detecting turbine anomalies 24 hours before fault.
Each turbine’s recent behaviour sets its own baseline. Early warning produced without any prior knowledge of how this type of turbine fails.
Wind farm · Greece · ~24h lead time
Monitoring solar inverter health across an entire plant.
All 44 inverters scored against their own baselines. Prioritisation of which units need attention — without waiting for a fault to be recorded.
Solar plant · 44 inverters scored
Identifying building HVAC anomalies at scale.
Each building establishes its own baseline. Drift detected across a large portfolio without any cross-building comparison or domain tuning.
Building portfolio · 1,574 sites
Detecting pump failure 73 hours before it happened.
Longest failure lead time achieved across all GangoAI validations. The drift signal identified the failure window days in advance.
Water infrastructure · 73h failure lead
Flagging oil and gas valve deterioration within minutes of it beginning.
Fast-moving events across 40 wells. Drift clearly identified which well was in an abnormal state as events began to develop.
Subsurface safety valves · 40 wells · 1,119 events
Predicting rail compressor failure up to 38 hours early.
Healthy baseline established first. Pre-failure drift detected and validated against three confirmed failure events.
Metro rail compressor · Porto · 20–38h lead time
Detecting ICU patient deterioration earlier than existing clinical tools.
Each patient’s own early physiology sets the baseline. Trajectory from that baseline outperformed an established clinical early warning score on this cohort.
ICU · 106 patient stays · Beth Israel Deaconess Medical Center
Knowing when a driver is no longer themselves. Coming soon.
GangoAI learns how each driver performs when they’re at their best. Before the shift. During it. The moment something shifts — you know.
UK fleet operator · Pilot commencing Q2 2026
Find out what GangoAI sees in yours.
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