Monitor, Predict, Diagnose & Optimize, AI for Safer, Quieter Operations
Brains applies AI/ML to your real-time operational data — but unlike generic analytics, every prediction is grounded in the engineering design of your facility. It knows what the sensor is measuring, what equipment it's attached to, and what the design limits are.
Book a DemoBrains Doesn't Just Analyze Data,
It Understands The Engineering Behind It.
Generic platforms treat sensor data as numbers. Brains treats it as engineering — connecting every reading to the equipment it monitors, the process it supports, the safety barriers that protect it, and the design intent behind it.
Self-Monitor. Self-Diagnose. Self-Optimize. Self-Operate.
Automated Operations Monitoring
Continuously ingest alarms and historian signals, learn normal by unit/mode, and detect drift and precursors—streamed to a ranked queue with audit trails.
Alarm Intelligence
Analyze alarm histories and signal patterns, deduplicate bursts, suppress nuisance alarms, and surface a risk-ranked and explainable alert queue.
Dynamic & Explainable RCA With HAZOPs
Correlate multi-source alarms, process signals, HAZOPs data and asset context to isolate root causes in real time, and recommend corrective steps.
Condition-Based Maintenance
Forecast degradation and failures, estimate remaining useful life, and propose condition-based maintenance windows—with risk ranking.
Real Time Soft Sensors & Virtual Meters
Compute virtual measurements (e.g., flow, composition proxies, fouling index) in real time—auto-calibrated to field data with drift monitoring.
Process Optimization Recommendations
Optimize across product, quality, and energy to generate prescriptive adjustments—quantified, explainable, and fully traceable.
Automated Operations Monitoring
Continuously ingest alarms and historian signals, learn normal by unit/mode, and detect drift and precursors—streamed to a ranked queue with audit trails.
Alarm Intelligence
Analyze alarm histories and signal patterns, deduplicate bursts, suppress nuisance alarms, and surface a risk-ranked and explainable alert queue.
Dynamic & Explainable RCA With HAZOPs
Correlate multi-source alarms, process signals, HAZOPs data and asset context to isolate root causes in real time, and recommend corrective steps.
Condition-Based Maintenance
Forecast degradation and failures, estimate remaining useful life, and propose condition-based maintenance windows—with risk ranking.
Real Time Soft Sensors & Virtual Meters
Compute virtual measurements (e.g., flow, composition proxies, fouling index) in real time—auto-calibrated to field data with drift monitoring.
Process Optimization Recommendations
Optimize across product, quality, and energy to generate prescriptive adjustments—quantified, explainable, and fully traceable.
“Brains reads your plant’s patterns, predicts what’s coming, and tells you exactly what to do by connecting the dots across sensors and history so operators act sooner with focused explainable recommendations.”
Frequently Asked Questions
Common questions about Brains' capabilities, deployment, and integration.
Brains connects to historian systems (OSIsoft PI, IP.21), DCS, SCADA, PLC systems, alarm logs, work logs, and cost data. It ingests real-time and historical operational data and layers it on top of the engineering context from the Contextual Graph — connecting every sensor reading to the equipment, process, and safety context that explains it.
Brains delivers the most value when it runs on the Contextual Graph built by Artisan — because the engineering context is what makes predictions meaningful, not just mathematical. However, Brains can connect to operational data independently for basic anomaly detection and alarm intelligence. The full power of engineering-contextualized prediction requires the Graph.
Generic platforms treat sensor data as numbers — they detect statistical anomalies but don't understand what the sensor is measuring, what equipment it's attached to, or what the engineering design limits are. Brains runs on the Contextual Graph, so every prediction is grounded in the actual engineering design of your facility. It knows that Sensor TT-4021 monitors the overhead vapor temperature of Column C-301, which has a design limit of 350°C and is protected by Safety Valve PSV-4022. That context is the difference between an alert and an actionable insight.
ISA 18.2 is the international standard for alarm management in process industries. It defines how alarms should be designed, implemented, monitored, and maintained to prevent alarm floods and ensure operator effectiveness. Brains' alarm intelligence module is built to ISA 18.2 compliance — classifying, prioritizing, and contextualizing alarms against your facility's engineering model to reduce noise and surface the alarms that actually matter.
A typical Brains pilot runs 2-4 months focused on a specific equipment system or operational challenge — rotating asset monitoring, alarm intelligence for a specific unit, or anomaly detection on a process train. ROI is typically demonstrated within the pilot phase. Full fleet-scale deployment takes 6-12 months. The platform delivers value from the first connected data source, not after a lengthy implementation.
Ready to Give Your Facility Predictive Intelligence?
Connect your operational data. See failures predicted, alarms contextualized, and maintenance scheduled by condition — all grounded in the engineering design of your facility.
- Detect equipment anomalies 15+ hours before failure.
- Deploy ISA 18.2 compliant alarm management across your facility.
- Ground every prediction in your facility's engineering context.
- Replace calendar-based maintenance with condition-based maintenance.
- Achieve ROI in less than 12 months.