Private AI Model Builder

Your Proprietary AI — Built and Deployed in a Day

Off-the-shelf AI models are trained on public data that knows nothing about your turbine degradation patterns, your grid's unique load signature, your hydrogen production facility's operating envelope, or your customer base's consumption behaviors. Energy Planet's Private AI Model Builder lets you fine-tune and deploy models trained exclusively on your proprietary data — in a fully isolated, air-gapped environment where your data never leaves your control.

The builder uses a guided pipeline that takes your labeled datasets through automated preprocessing, model selection, fine-tuning, evaluation, and deployment — with no ML engineering team required. Choose from a curated library of pre-trained foundation models optimized for energy use cases: time-series prediction, fault classification, anomaly detection, document understanding, and natural language interfaces. Fine-tune them on your data, validate against your metrics, and deploy via a secure API endpoint that only your applications can call.

The deployment layer is production-grade from day one. Auto-scaling inference, model versioning, A/B testing between model versions, drift detection monitoring, and one-click rollback mean your AI systems stay accurate and available even as conditions change. Every model gets a full model card documenting training data provenance, performance benchmarks, known limitations, and intended use — critical for regulatory defensibility in the energy sector.

For energy companies sitting on years of operational data that's never been fully exploited, this is a transformational unlock. Your SCADA historian, your maintenance records, your smart meter readings, your trading desk notes — all of it can become a proprietary intelligence asset that compounds in value over time. Start with a single use case. Scale to a dozen. Own the AI advantage in your market.

Builder Capabilities

• Guided no-code fine-tuning pipeline for custom model training
• Pre-built foundation models for energy time-series, NLP, and classification
• Air-gapped, fully isolated training and inference environments
• Model versioning, A/B testing, and one-click rollback
• Automated drift detection and retraining triggers
• Model cards with provenance, benchmarks, and compliance documentation