Industry AI Platform

Enterprise AI handles documents, emails, and business processes. Industry AI operates in the physical world — vibration signatures from a 40-ton press, thermal profiles of a reactor vessel, visual defects on a part moving at 2 meters per second. The models are different (time-series and physics-informed, not NLP). The latency requirements are different (milliseconds, not seconds). The deployment environment is different (OT networks behind firewalls, not cloud APIs). Generic enterprise AI platforms from Salesforce or Microsoft fail in manufacturing because they were not built for the shop floor. Industry AI is purpose-built for OT data, industrial protocols, and the physics of production.

Twelve weeks for a single-plant deployment covering 2-3 use cases. Weeks 1-3: OT connectivity and data ingestion. Weeks 4-8: model training on 6-12 months of historical data, validated against known events. Weeks 9-12: production deployment, operator training, and CMMS integration. Some value shows up in week 6 when the models start flagging anomalies during validation. Full ROI — typically 3-5x the deployment cost — materializes within 6-9 months as prevented downtime and quality improvements compound.

No. The platform reads from your existing infrastructure via OPC-UA, Modbus TCP, and MQTT. Your Siemens S7-1500s, Allen-Bradley ControlLogix, Aveva historians, and OSIsoft PI servers stay exactly where they are. We add an intelligence layer on top — not a replacement underneath. The only new hardware is edge compute for inference, and even that often runs on existing industrial PCs with a GPU added.

This is the most common objection, and it is valid. If a critical pump has failed twice in five years, you do not have enough failure examples for supervised learning. The workaround: unsupervised anomaly detection. Models learn what normal looks like from months of healthy operation data, then flag deviations. No labeled failures required. As failures do occur, the models transition to supervised approaches with higher specificity. Shreeng AI's Predictive Analytics platform provides the statistical modeling backbone — Industry AI adds the OT-specific signal processing and domain knowledge.

They share a technical foundation. AI Video Intelligence handles security, safety, and operational monitoring across general-purpose cameras. Industry AI's visual inspection module is purpose-built for production line cameras — higher frame rates, tighter defect taxonomies, and integration with reject mechanisms. In practice, manufacturers deploy both: AI Video Intelligence for plant-wide safety and security monitoring, and Industry AI's inspection module on the production line. The camera infrastructure often overlaps, and both systems feed into a unified operational dashboard.

Correct concern — and the one most vendors hand-wave away. Our architecture uses a unidirectional data diode pattern where possible: data flows from OT to the analytics layer, but no control commands flow back unless explicitly configured and approved by your controls engineering team. Edge nodes sit inside the OT network boundary. They do not require internet connectivity. When closed-loop control is enabled, every command validates against safety limits defined in the PLC, and every action logs to a tamper-evident audit trail. We comply with IEC 62443 and support network segmentation per the Purdue Model.

Prevented downtime is the largest line item — typically $500K-$5M annually for a mid-size plant, depending on industry. Quality improvements add $200K-$2M through reduced scrap, rework, and warranty claims. Energy optimization contributes $150K-$1M. We measure against baselines established during the first 90 days: mean time between failures, first-pass yield, OEE, energy per unit produced. Forrester's Total Economic Impact studies for industrial AI consistently show 150-300% three-year ROI. Our deployments track to the upper end because we focus on the highest-value failure modes first.

That is exactly the environment this was designed for. Model deployment and updates happen via rolling edge updates — no production interruption. Sensor connectivity uses passive taps on existing networks, not inline devices that require shutdown to install. The initial data ingestion phase reads from historians, not live systems. When closed-loop control is activated, set-point changes are bounded by operator-defined limits — the AI cannot push a reactor beyond parameters your process engineers have approved. Continuous operation is the design constraint, not an afterthought.