One industrial AI. Any industry with operational data.

What it catches. Watches each patient’s vital signs against their own baseline — heart rate, blood pressure, oxygen, breathing, temperature. Catches the quiet multi‑signal drift toward deterioration hours before any single alarm would fire, so the care team can act early.

Setting:

A 62-year-old post-surgical patient in a 400-bed hospital showed no individual vital sign alarms, but heart rate had risen 12 bpm over 6 hours while temperature crept up 0.4°C and respiratory rate increased by 3 breaths/min.

Detected — and why:

The AI recognised the multi-parameter drift pattern as consistent with early-stage sepsis — 8 hours before the patient met standard SIRS criteria — and flagged the care team with a 91% confidence alert.

Outcome:

Early blood cultures confirmed sepsis. Antibiotics started 6 hours earlier than standard protocol would have triggered, reducing ICU stay by an estimated 3 days.

• · Patient deterioration early warning
• · Vital sign anomaly detection
• · Sepsis and adverse event prediction
• · Clinical decision support for care teams

What it catches. Watches every sensor on GMP‑critical equipment and tells normal drift from dangerous drift. Catches the slow problems — a pH probe losing sensitivity, a freeze‑dryer compressor drawing more current — before a batch goes out of spec.

Setting:

A sterile injectables facility ran 4 production-scale freeze dryers in continuous rotation. Dryer-2's primary drying phase was gradually taking 22 minutes longer per cycle than the fleet average.

Detected — and why:

The AI identified compressor discharge pressure declining 0.8 psi/week — the condenser was losing refrigerant through a micro-leak in the brazed joint.

Outcome:

Joint repaired during scheduled CIP cycle — zero batch deviations, $1.4M worth of product protected.

• · Batch release quality assurance
• · GMP compliance automation
• · Yield prediction and optimisation
• · Cross-contamination prevention

What it catches. Watches motor current, belt tension, robot cycle times and vibration at every station. Catches degradation days ahead and recommends the maintenance window that costs the least production.

Setting:

An electronics manufacturer ran a 400-meter assembly line with 12 conveyor sections feeding SMT pick-and-place machines. Section 7 was a known bottleneck.

Detected — and why:

The AI detected gearbox oil temperature on Section 7 rising 0.6°C/shift while motor current increased 4.2% — gear tooth wear was creating friction losses.

Outcome:

Gearbox swapped during a 90-minute shift changeover — avoided a 14-hour line shutdown that would have cost $1.1M in delayed shipments.

• · OEE optimisation
• · Defect prediction
• · Tool wear monitoring
• · Digital twin simulation

What it catches. Watches tonnage signatures, ram velocity and hydraulic pressure on every press stroke. Catches die‑set wear before forming quality drops below spec, so die changes land in planned breaks.

Setting:

A Tier-1 body shop ran 6 transfer presses producing door inners, fenders, and quarter panels at 12 strokes/minute.

Detected — and why:

The AI detected Press-3's cushion pressure settling time increasing by 8ms per week — the nitrogen pre-charge in the cushion cylinders was leaking, causing wrinkling risk on deep-draw panels.

Outcome:

Cushion cylinders re-charged during weekend TPM — zero quality escapes, zero line stops, $180K in scrap and rework avoided.

• · Line stop prevention
• · Press tonnage monitoring
• · TPM optimisation
• · Quality assurance

What it catches. Watches heat‑exchanger fouling, filler valve response and mixer torque continuously. Catches a pasteuriser plate losing efficiency or a fill valve starting to over‑fill — before product leaves the line out of spec.

Setting:

A dairy processor ran 3 pasteuriser lines processing 120,000 litres/day of UHT milk. Line 2 was CIP'd every 8 hours on a fixed schedule.

Detected — and why:

The AI detected Line 2's outlet temperature variance increasing from ±0.1°C to ±0.4°C between CIP cycles — protein fouling on plates was reducing heat transfer efficiency faster than the other two lines.

Outcome:

CIP frequency adjusted to every 6 hours for Line 2, and gaskets replaced during scheduled maintenance — maintained food safety compliance, prevented a potential $8M recall event.

• · HACCP compliance assurance
• · CIP schedule optimisation
• · Contamination prevention
• · Temperature and pressure control

What it catches. Watches heat‑transfer coefficients, pressure drops and conversion rates in real time. Catches fouling and catalyst deactivation early and finds the cleaning or replacement window that balances lost production against degradation.

Setting:

An ethylene plant ran a C2 splitter column with 120 trays separating ethylene from ethane at -25°C. Tray efficiency gradually declined between turnarounds.

Detected — and why:

The AI detected column differential pressure increasing 0.15 psi/week faster than the historical baseline — polymer fouling on trays 40-55 was reducing separation efficiency and increasing reboiler duty by 6%.

Outcome:

Targeted tray cleaning performed during a 36-hour mini-turnaround instead of waiting for the full 4-year turnaround — recovered $340K/year in energy costs and maintained product purity.

• · Reaction optimisation
• · Fouling rate prediction
• · Safety monitoring
• · Emissions control

What it catches. Watches thermocouple arrays, mould level and ladle weight heat by heat. Catches refractory thinning, nozzle clogging and slag carryover before they reach steel quality.

Setting:

An integrated steel mill cast 2.5M tonnes/year of slab through a 2-strand continuous caster. Surface crack defects had been increasing 0.3%/month.

Detected — and why:

The AI identified Strand 1's mold oscillation amplitude drifting 0.12mm from setpoint — a hydraulic servo valve was losing response accuracy, causing inconsistent mold flux entrainment.

Outcome:

Servo valve replaced during a grade change stop — surface crack rate dropped from 4.1% to 1.2%, recovering $890K/year in downgrade losses.

• · Temperature control and prediction
• · Composition and quality grading
• · Casting defect prevention
• · Energy optimisation

What it catches. Watches felt vacuum, press nip pressure, headbox consistency and cross‑direction moisture. Catches the conditions that precede a web break and times felt changes on wear, not the calendar.

Setting:

A linerboard mill ran a 7-meter-wide paper machine at 850 m/min producing 1,200 tonnes/day. Felt changes were scheduled every 21 days.

Detected — and why:

The AI detected the 3rd press felt's vacuum differential increasing at 2x the normal rate, with sheet moisture after the press section rising from 52% to 54% — felt conditioning showers were not fully removing filler deposits.

Outcome:

Felt conditioning adjusted and felt changed 4 days early — avoided 3 projected web breaks worth $120K in lost production.

• · Paper break prediction
• · Moisture control optimisation
• · Basis weight consistency
• · Chemical dosing optimisation

What it catches. Watches flow meters, pressure zones and pump motor current. Catches leaks as flow imbalances between zones and pump failures before they happen, prioritised by water loss and service impact.

Setting:

A municipal water utility operated 45 booster pump stations serving 280,000 customers. Most stations were unmanned with only SCADA pressure and flow monitoring.

Detected — and why:

The AI detected Station 14's main pump drawing 7.3% more current than the identically-sized backup pump at the same flow rate — motor bearing resistance was increasing from contaminated lubricant.

Outcome:

Bearing replaced during a low-demand period — avoided a pump failure that would have triggered a boil-water advisory for 12,000 customers.

• · Demand forecasting
• · Leak detection and location
• · Regulatory compliance
• · Asset lifecycle management