Predictive maintenance · production line
Multi-sensor + LLM correlation. Unplanned downtime fell 31% and maintenance team moved from firefighting to scheduled work.
Applied AI for operations, manufacturing.
Predictive maintenance, visual quality control and supply chain optimization. From the floor to the DC.
Overview
What we deliver in this vertical.
Operations · Supply chain · Quality
Manufacturing
Predictive maintenance, visual quality control and supply chain optimization. From the floor to the DC.
−31%unplanned downtime
+18%OEE
−22%scrap
Applications
- 01Multi-sensor predictive maintenance
- 02Computer-vision inspection
- 03Demand and inventory forecast
- 04Shop-floor copilots
- 05Energy optimization
Positioning
Industrial AI isn't a trend — it's manufacturing capability.
Anyone still debating whether AI "makes sense" on the shop floor is a decade behind. The real conversation is: what data already exists in the plant, what sensor is missing, and how much each minute of additional uptime is worth. The answer comes from diagnostic — not from a vendor deck.
By the numbers
AI's impact on the production line.
−0%unplanned downtime with predictive maintenance
+0%OEE in automotive plants with visual inspection
−0%forecast error in CPG with combined model
−0%scrap on the line with computer vision at 3 points
Under the hood
A stack that runs without stable internet.
Industrial plants have variable connectivity and legacy systems. The stack below is what shows up in brownfield deployments where SCADA, MES and old PLCs coexist with the new pipeline.
Computer vision
- YOLOv8 / Detectron2On-edge inference on industrial cameras.
- ONNX RuntimeCloud-trained, edge-executed.
- OpenCVPre-processing + image quality.
Time series
- Prophet / NeuralProphetDemand forecasting with seasonality.
- TimeGPT / ChronosFoundation models for forecasting.
- Proprietary models (LSTM/Transformer)When proprietary data is the differentiator.
Edge & integration
- OPC-UA gatewayStandard for SCADA and PLC reads.
- Kepware / IgnitionIndustrial bridge.
- Native ERP (SAP, Totvs, Plex)API + custom middleware.
Operations
- MLflow / DVCModel + data versioning.
- Grafana + PrometheusReal-time operational metrics.
- Custom drift detectionFor raw material or shift change.
In production
Results in manufacturing.
OEE · automotive plant
Computer vision inspection at 3 line points. Scrap fell, OEE rose 18% with no downstream rework increase.
Demand forecast · CPG
Model combining history, seasonality and market signals. Average forecast error fell 22%, directly impacting inventory and working capital.
04 · LA AI Method
From hypothesis to operation.
Five steps that prevent eternal pilots. Each stage has deliverables, metrics and decision gates.
M1
Diagnostic
Opportunity map, estimated value, data readiness.
2–3 wksM2
Strategy
Prioritized roadmap, reference architecture, governance.
2 wksM3
Proof of Value
POC with production criteria and business metric defined.
4–6 wksM4
Deployment
Model, integrations, security, observability and UX.
6–12 wksM5
Continuous Operations
Evolution, fine-tuning, eval suite, cost per use, new features.
RecurringFrequently asked
About AI in manufacturing.
Does it work in plants without stable internet?
Yes. For latency- or connectivity-critical cases we run on-prem inference with periodic cloud sync. Model trained in cloud, executed at the edge.
Does it integrate with existing SCADA/MES?
Yes. OPC-UA for SCADA, native APIs for MES (SAP, Plex, etc.) or custom webhook.
How long until predictive maintenance pays off?
3–6 months for a model trained on customer-specific data. Before that, baseline with public models to estimate value.
Does it work in brownfield plants with old sensors?
Yes. We work with what's there. If a critical sensor is missing, we help spec the minimum addition needed for the model to work.