Use Case: Predictive Maintenance — CNC Machine Failure Detection · Agentic Trinity Architecture
Goal: Predict machine failure within 24h across 200+ CNC machines using IoT sensor telemetry.
Data: 1.2M rows, 15 columns (temp, vibration, pressure, RPM, power, coolant, noise, oil viscosity, service hours)
Impact: Reduce unplanned downtime 60%, save €2.4M/year
Three specialized agents collaborate through proactive discovery, feasibility auditing, and code generation
PII Detection: operator_name → pseudonymized, operator_email → dropped AUTO
Stats: skewness, kurtosis, correlation matrix computed
Technical Possibilities: classification, regression, anomaly_detection, clustering
Quality: 340 dupes removed · Score 94/100
Proactive Discovery — 3 Use Cases Found: NEW
Feasibility Audit: GO — score 0.91, sufficient labels, no class imbalance concern
6 Engineered Features:
⚡ 3 of top 5 SHAP features are LLM-engineered — proactive discovery adds real business signal
3 Models Trained: XGBoost, LightGBM, Random Forest
Winner: XGBoost Classifier (Bayesian-tuned, 20 iter) · n_est=650 depth=8 lr=0.05
Outputs: training_code.py inference_code.py model.joblib SHAP explanations NEW
Training: 42.3s on 960K rows · Serialization: joblib
★ = Engineered by Business Strategist (LLM)
Miss rate: 7.2% · False alarm: 3.0% — maintenance teams prefer over-alerting
v2.1 Extras: inference_code.py generated · model serialized as joblib · full audit log attached
Comprehensive automated machine learning with intelligent agent collaboration, zero-trust data processing, and production-ready code generation.
Autonomous agents analyze your data, recommend optimal ML approaches, execute complex workflows automatically, and learn from previous experiments.
From Linear Regression to Transformers — supporting classification, regression, clustering, and time series tasks with parallel training.
PII auto-detection and pseudonymization, LLM sees metadata only, full audit logging, GDPR compliant by design. Your data never leaves your infrastructure.