AI for manufacturing & industrial

Custom AI/ML solutions for manufacturing & industrial industry

Custom AI and ML for plant operators, OEMs, and industrial groups. Predictive maintenance, quality inspection, process optimization, and demand sensing — built for the shop floor, not the showroom.

Book a Manufacturing AI Strategy Session

30–50%

Reduction in unplanned downtime with predictive-maintenance ML on sensor + CMMS history.

$691K/line

Average annual labor savings per production line from AI visual inspection.

99% vs 87%

Computer-vision defect-detection accuracy vs human-inspector baseline.

Achieve immediate, organization-wide results

Six measurable outcomes across underwriting, claims, and actuarial functions — deployed in months, not years.

Predictive Maintenance

Asset-failure prediction from sensor + CMMS history. 30–50% less unplanned downtime, typical payback 6–9 months.

Computer Vision Inspection

99% defect-detection accuracy vs 87% human baseline. Plugs directly into your existing camera + PLC infrastructure.

Process Yield Optimization

Closed-loop control with RL / MPC to push yield, OEE, and energy efficiency without retrofitting equipment.

Quality Root-Cause Analysis

Multivariate models that find the upstream cause of out-of-spec batches in hours, not weeks.

Supply & Demand Sensing

Real-time demand forecasting fused with supplier-health signals for inventory, capacity planning, and S&OP.

Patent & R&D Research AI

RAG-grounded research-AI over USPTO / EPO / WIPO patents and your internal materials, process, and product research library. For OEM engineering, innovation teams, and IP strategy.

Capabilities across the manufacturing & industrial value chain

Predictive Maintenance & Reliability

Quality & Visual Inspection

Process Optimization & Yield

Supply Chain & Demand Sensing

From the playbook

How a $400M industrial OEM cut unplanned downtime 42% and saved $6.8M annually

A mid-size industrial-pump OEM with 14 production lines was losing 1,800 hours/year to unplanned downtime — gearbox and motor failures topping the list. We instrumented the existing PLC + vibration sensor network, built a remaining-useful-life model per asset class, and integrated work-order generation into their CMMS. Unplanned downtime dropped 42% within 9 months, parts inventory dropped 18% (better forecasting + targeted ordering), and annual recovered productive hours hit $6.8M. Payback under 8 months on the full instrumentation + ML platform spend.

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Speak with a manufacturing AI expert

A 45-minute scoping call. We’ll come prepared with your appetite, your loss-cost benchmarks, and a directional read on which models move the needle on your line of business.

Ask us about

Predictive maintenance for legacy and new equipment
Computer vision defect detection at line speed
Process yield and energy optimization
Multivariate root-cause analysis for quality excursions
Demand sensing and S&OP scenario modeling
Patent landscaping and R&D research-AI over USPTO/EPO + private corpora

Frequently asked questions

Both. Most clients run a mix of legacy PLCs (Siemens, Rockwell, Mitsubishi) alongside newer IIoT stacks. We pull telemetry through existing historians (OSIsoft PI, AVEVA, Aspen) and add inexpensive vibration / thermal / acoustic sensors only where the analytical value is clear. We never push a full retrofit if the existing data gets you 80% of the model performance.

Either. For latency-critical inspection (>30fps), we deploy quantized models to edge GPUs / NPUs (NVIDIA Jetson, Hailo, Coral) so inference happens in milliseconds at the line. For analytics-only workloads, cloud inference is cheaper. We always run the same model in shadow on the edge before flipping it into a control loop.

Up front. Every project starts with a 2-week data audit: existing tag taxonomy, missing-data patterns, sensor drift, and label noise. We fix the sensors and pipelines before training — bad data with high-end ML always loses to clean data with simple ML. Most of the savings come from this discipline.

Yes. The underlying ML platform (sensor ingestion, feature store, model registry, monitoring) is reusable across all three. Domain models differ — discrete focuses on cycle-time and vision, process on yield/quality and energy, hybrid on both — but most multi-modal manufacturers run one shared stack with multiple model families.