Cognitive Maintenance is an advanced AI-driven approach that moves beyond simple alerts. While traditional systems might signal a problem, Cognitive Maintenance diagnoses the specific fault, explains the root cause of why a failure is imminent, and provides actionable repair recommendations. By telling teams exactly what is wrong and how to fix it, it empowers engineers to make faster, data-backed maintenance decisions.

While predictive maintenance uses historical patterns to forecast failures, Cognitive Maintenance uses agentic AI that actively learns from live sensor data, adapts to changing conditions, and provides root cause analysis, not just alerts. It gets smarter over time and requires less manual rule-tuning.

Clients typically see a 20-40% reduction in maintenance costs, a meaningful improvement in equipment uptime, and measurable increases in asset lifespan within the first year of deployment.

GINA (Groundup Intelligent Neural Agent) is our proprietary agentic AI engine that powers Cognitive Maintenance. It continuously analyses vibration, temperature, and other sensor data to detect anomalies with 94% accuracy and surface actionable root cause insights.

The Asset Library is our continuously expanding knowledge base of machine behaviour patterns, anomaly signatures, and failure modes, built from over 800,000 machine data hours. It enables GINA AI to match your asset's real-time signals against a global library, delivering root cause insights 3x faster.

Our solution supports 1,000+ machine types including motors, pumps, compressors, HVAC units, conveyor systems, generators, and more, across manufacturing, maritime, defence, and critical infrastructure environments.

GINA AI is specifically trained on industrial-grade sensor data and uses advanced signal processing to filter noise, ensuring accurate anomaly detection even in high-vibration and high-temperature environments.

Any organisation that relies on critical rotating or process equipment, from food and beverage manufacturers to maritime operators, defence contractors, and critical infrastructure providers.

Typical deployment takes 4-8 weeks from sensor installation to full operational intelligence, depending on the number of assets and site complexity. A pilot on a single production line can be operational in as little as 2 weeks.

No. Groundup.ai integrates with your existing CMMS, ERP, and SCADA systems via open APIs. We enhance your current stack rather than replacing it.

Minimal. Once deployed, the platform runs autonomously. Engineers spend roughly 15-30 minutes daily reviewing prioritised alerts and recommended actions on the dashboard, replacing hours of manual rounds and data analysis.

Every prediction is logged and auditable. When an engineer disagrees with a recommendation, their feedback is fed back into the model as a learning signal. GINA AI continuously improves through this human-in-the-loop process, reducing false positives over time.

We offer on-premise and edge deployment options for air-gapped environments. All data is encrypted in transit and at rest, and our platform meets enterprise security standards for defence and critical infrastructure clients.

GINA AI can detect early-stage anomalies as far as 2-6 weeks before a failure occurs, depending on the failure mode and asset type.

Absolutely. We offer a structured pilot programme that lets you validate ROI on a subset of assets before committing to a full-scale deployment. Most pilots deliver measurable results within 60 days.

Start with a consultation call where we assess your asset landscape and maintenance challenges. From there we design a pilot scope, install sensors within 1-2 weeks, and begin collecting data. Full operational intelligence is typically achieved within 4-8 weeks.