GangoAI watches anything against its own baseline and surfaces drift the moment it begins. No training data. No failure labels. No retraining. The decision stays with you.
Every result on this page was produced blind — the system never saw the outcome in advance. Every result is independently auditable.
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. It learns what normal looks like for each asset, person, or process — and surfaces the moment it stops being normal. The interpretation is yours.
Each drive watched against its own baseline. The system flagged drives drifting toward failure without ever being shown what failure looks like — beating the best published result on this fleet, which used trained models with labelled failure history. We used none.
Backblaze production fleet data · 92.3% precision · 39-day average lead time · independently auditable
Each turbine watched against its own recent behaviour. Drift surfaced weeks before breakdown across two independent operators in two different countries — without any prior knowledge of how this turbine type fails.
Onshore wind farms in Portugal and Norway · 19–29 day lead time. Additional run on a Greek wind farm produced a 24-hour lead time on a different fault profile.
Each cell's own early behaviour set its baseline. The drift score rose as the cell degraded — the system never needed to see a failed cell to recognise one.
Across 21 cells from the NASA battery dataset · -0.919 average correlation with remaining useful life
Each engine measured against its own baseline. The drift trace captured the degradation curve from first deviation, with no failure labels at any point.
NASA turbofan run-to-failure data · -0.903 correlation with the degradation curve
Drift signal identified the failure window three days before it happened.
Public water infrastructure dataset · 73-hour lead on a confirmed failure
Healthy baseline established first, then drift surfaced before failure. Validated against three confirmed failure events with consistent pre-failure lead time.
Metro Porto rail compressor data · 20–38 hour lead time
Fast-moving events. The system cleanly surfaced abnormal wells as events began to develop — no thresholds, no rule-tuning, no prior knowledge of failure modes.
Subsurface safety valve dataset · 40 wells · 1,119 events scored
Every person measured against their own baseline. The system does not decide whether someone is "drunk" or "fatigued" — it surfaces that they are behaving differently from how they normally behave. You decide what to do with that. No biometric storage required.
Malmesbury Boxing Club study · 41 participants · 92.7% accuracy · zero false positives across two days · Innovate UK funded
Drift detected against individual baseline in real operational conditions. Statistically significant separation between alert and fatigued states — the system surfaces the change, the operator interprets it.
Applied Automation, Plymouth · Innovate UK funded
Wearable signals scored against each person's own baseline. Strong classification without any population-level training — meaning the system does not compare you to a generic person, it compares you to you.
Wearable sensor dataset · 84.8% classification accuracy
Uninjured side establishes the baseline. Injured side is monitored for drift back toward it. The system surfaces whether recovery is on track — the clinical decision stays with the physiotherapist.
Cliff Villages Physiotherapy · live deployment
The system learns how each driver performs at their best — before the shift and during it. The moment something shifts, the operator sees it. No driver scoring, no league tables — drift against the driver's own baseline.
UK fleet operator · pilot in progress
Each patient watched against their own physiology from admission. The system surfaces patients drifting from their own stable state — the clinical decision stays with the clinician. The system is an earlier deterioration signal, not a treatment recommender.
MIMIC-IV ICU data, Beth Israel Deaconess Medical Center · 76,579 stays.
Each participant tracked against their own earlier visits. The system does not diagnose dementia — it surfaces who's drifting from their own cognitive baseline. The clinical interpretation stays with the clinician. Works with sparse, irregular visit data — the kind real clinics actually collect.
National Alzheimer's Coordinating Center cohort · 49,150 participants. High-specificity tier surfaced future converters a median of 28.5 months before formal conversion at 91.9% specificity. Broader surveillance tier reached 53 months median lead.
Every unit scored against its own baseline. The operator gets a ranked list of which assets are drifting hardest — without waiting for a fault to be recorded.
Solar plant dataset · 44 inverters scored independently · no cross-unit comparison required
Each site established its own baseline. The system surfaced drift across the whole portfolio without any cross-site comparison or domain tuning — meaning the same algorithm runs on a 5-site portfolio or a 5,000-site one.
HVAC building portfolio · 1,574 sites
Each thing watched against its own baseline. Drift surfaced before it became obvious. The decision — repair, intervene, escalate, ignore — stays with the people closest to the operation.
We do not predict the future. We catch the moment normal ended.
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