Manufacturing AI Transformation
40% less unplanned downtime
A predictive maintenance + computer vision platform that watches every line in real time.
Where the old way was failing
Unplanned line stoppages were costing the operator an estimated $1.2M per quarter, and maintenance was purely reactive — teams found out about failing equipment when it failed.
Quality control relied on manual spot checks. Defective batches regularly reached packaging before anyone caught them, driving rework cost and customer escalations.
A previous vendor had spent 14 months on a pilot that never left the lab. Leadership was skeptical that AI could survive contact with a real factory floor.
What we built
Predictive maintenance engine
We instrumented critical rotating equipment with vibration and thermal telemetry, then trained failure-prediction models on six months of historical SCADA data. The system now forecasts component degradation 2–3 weeks ahead with confidence scoring.
Computer-vision quality gates
Edge cameras at four checkpoints per line run defect-detection models at full line speed. Defects are flagged, photographed, and routed to the MES in under 200ms — before product reaches the next station.
Line copilot for operators
A plain-language copilot lets floor operators ask 'why did line 3 slow down this shift?' and get answers grounded in live telemetry, maintenance logs, and shift notes.
What changed for the business
Unplanned downtime fell 40% within the first two quarters of operation.
Escaped defects dropped 62%, cutting rework and customer credits.
The platform paid for itself in 8 months; rollout to the remaining two plants is underway.
Maintenance planning moved from reactive firefighting to scheduled, parts-on-hand interventions.
“The previous vendor gave us a 14-month pilot that never left the lab. Sharpkraft had models running on a live line in eleven weeks.”
Want results like these?
Book a strategy session and we'll map the same AI Engineering Framework onto your product, your data, and your industry.