The DiagAI Platform

LSTM autoencoder trained on 10,000+ industrial readings. Detects deviations in air temperature, torque, RPM, tool wear and 9 other signals in under 2 seconds.

• Real-time z-score inference
• LSTM + Random Forest ensemble
• Configurable thresholds
• Multi-sensor fusion

4 specialized LangGraph agents collaborate to diagnose every fault — from initial symptom detection through root cause identification to remediation planning.

• Diagnostic, Reasoning, Planning & Learning agents
• Groq Llama 3.3 70B reasoning
• Parallel agent execution
• Confidence-scored outputs

A semantic OWL ontology maps equipment components, failure modes, environmental factors and causal relationships — enabling contextual reasoning no threshold can match.

• OWL/SWRL ontology
• 50+ equipment entity types
• Causal relationship mapping
• Cross-domain failure patterns

Equipment health scores update in real time as anomalies accumulate. Trend forecasting flags degradation trajectories before they reach failure thresholds.

• 0.6×LSTM + 0.4×RF ensemble
• Health score decay model
• Severity classification (4 levels)
• Fleet-wide dashboards