Manufacturing.
Fewer surprises on the factory floor.
AI that earns its keep on the shop floor. Less downtime, fewer recalls, sharper forecasts — wired into the systems you already run.
Predictive maintenance, vision QA and supply-chain forecasting for plants that can't afford to stop.
What gets in the way
Unplanned downtime
One stuck conveyor cascades into a $40K shift. Maintenance teams react instead of predict.
Inconsistent quality
Human visual inspection drifts after lunch. Returns and recalls eat margin.
Brittle supply chain
Tier-2 supplier delays surface too late. Safety stock balloons, working capital sinks.
Where AI moves the needle
Predictive maintenance
Vibration, current and temperature signatures forecast failures 7–14 days out.
Vision-based QA
Real-time defect detection on the line, with active-learning loops for new SKUs.
Demand forecasting
Forecast SKU demand at store-day granularity, blending POS, weather and macro signals.
Energy analytics
Identify the 5% of assets driving 40% of energy use; act on it.
Digital work instructions
Multimodal assistants that walk operators through procedures — in their language.
How we delivered
Vision QA on 6 lines without retrofitting the PLCs
We shipped a vision QA layer that runs on commodity edge boxes, integrates with the existing MES, and pushes defects to a continuously-learning model. False rejects dropped 60% in 8 weeks.
How we work
Discovery
4–6 hour workshop. Goals, customer segments, JTBD, user flows, AI proposal, 6-month roadmap.
Architecture
Data audit, model selection, integration plan, evaluation harness, governance.
Build
2-week iterations. Demo Fridays. Built-in observability from day one.
Scale
Production rollout, change management, continuous fine-tuning and cost monitoring.
Ready to put this on the roadmap?
We run a focused discovery in 2 weeks. You leave with a working prototype and a defensible ROI case.