Proof from the control room.
Industrial AI buyers need evidence to de-risk adoption. Champions need artifacts they can share with leadership and IT/OT. Every story below names the operational outcome — concretely, with a timeframe and a caveat.
Named operational outcomes.
Aeration energy down 9% while holding effluent limits.
Aeration ran on fixed setpoints sized for worst-case load.
"We finally trust the recommendation enough to leave it on overnight."
First-pass yield up 2.1pp on the dryer line.
Moisture was confirmed at the lab — two hours after the batch left the dryer.
"The moisture estimate is in front of the operator before the batch is half-dry."
Heat rate cut 3.1% across a full season at a waste-to-energy plant.
Excess-air margin protected boiler stability but burned efficiency.
"We're running the same boiler, with the same crew, just better tuned to what's actually in the bunker."
+4.8% tonnes-per-hour on the constraint line.
Operators tuned the mill reactively as ore hardness shifted between feeds.
"We used to chase the mill. Now we get a 20-minute heads-up."
71% fewer false alarms by replacing thresholds with one anomaly model.
Alarm flood drowned the real signal — operators acknowledged and moved on.
"When the alarm goes now, the crew actually walks to the cell."
Compressor train uptime +12% over twelve months.
Trips on the compressor cost more than every defect on the line combined.
"We schedule the intervention. We don't get woken up at 2am for it anymore."
Named operators, not anonymous endorsements.
Build the business case.
Representative inputs and payback ranges by use case. Illustrative — not a quote.
Talk to an expert about a similar use case.
Tell us your KPI and one constrained unit. We'll come back with the closest comparable customer rollout and what data you'd need to start.