From BMS Telemetry to True BESS Intelligence.

Standard BMS software tracks raw signals, but it can’t model non-linear degradation across complex BESS installations. Sphere replaces guesswork with a physics-informed AI model that calculates true State of Health—from individual cells up to multi-megawatt fleets. Deployed on-site or in the cloud, VETTA provides the audit-grade visibility needed to protect battery warranties, predict End-of-Life, and maximize grid asset revenue.

How VETTA Powers BESS Degradation Modeling

01
Pre-training

VETTA is pre-trained on over 4,000 real-world battery cycling datasets, 100 million material configurations, and physics-based Pseudo-2D models. Instead of relying on standard empirical curve fitting, our foundation AI masters the underlying electrochemistry of cell degradation, thermal stress, and non-linear capacity fade.

02
Fine-tuning

We calibrate the base model using your installation historical BMS telemetry including voltage, current, thermal behavior, and grid dispatch profiles. Tailored to your specific cell chemistry, rack architecture, and local operating environment, the model deploys on cloud or local edge hardware while keeping 100 percent of your IP and data secure.

03
Real-Time Inference

In live operation, VETTA continuously processes field telemetry to generate audit-grade State of Health estimates and a 10-year degradation forecast with p5/p95 confidence bands. Automated SHAP explainability isolates the exact driver of capacity fade, whether thermal stress, C-rate abuse, depth of discharge patterns, or cell manufacturing outliers.

One Model. Full Hierarchy: Cell to Fleet Visibility.

Our Solutions
Hierarchical Cell-to-Site Visibility
Physics-Informed SOH Modeling
SHAP-Driven Root-Cause Diagnostics

Track battery health seamlessly across every level of your energy storage facility. VETTA monitors individual cells, battery modules, racks, energy storage containers, and full multi-megawatt fleets within a single unified platform. Identify weak cells or underperforming racks instantly before localized imbalances degrade site capacity.

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Move beyond basic BMS coulomb counting. VETTA combines electrochemical physics with Pseudo-2D simulations to model true capacity fade and internal resistance changes. By mathematically isolating degradation mechanisms like SEI layer growth, active material loss, and lithium plating, the platform delivers audit-grade State of Health accuracy for total contract compliance.

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Eliminate guesswork when assets degrade faster than site averages. Powered by explainable AI, VETTA uses SHAP attribution to pinpoint the exact operational stressor driving capacity loss. Instantly evaluate the impact of ambient operating temperatures, depth of discharge habits, C-rate spikes, calendar aging, or low-temperature charging events.

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See VETTA Model a Live BESS Fleet

From raw site telemetry to cell-level State of Health and 10-year degradation forecasts. Take a two-minute guided tour of our end-to-end inference pipeline and see how physics-informed AI transforms battery asset management.

Use Cases and Platform Benefits

  • Warranty Tracking and Claims Protection

    Track per-component health continuously against contractual warranty guarantees, such as 80 percent State of Health thresholds. Generate an independent, tamper-proof audit trail so warranty claims never default to the manufacturer BMS reporting.

  • Edge Deployment and OT Cybersecurity

    Run 100 percent of diagnostic models on local hardware inside your site secure DMZ. Keep raw telemetry entirely within your site boundary with zero cloud exposure, full data sovereignty, and complete air-gap compatibility.

  • Predictive Maintenance and EOL Planning

    Generate 10-year State of Health projections per unit featuring p5/p95 confidence bands and exact breach date estimates. Schedule targeted rack replacements proactively before capacity fade impacts dispatch schedules, PPA compliance, or revenue.

  • Site Benchmarking and Anomaly Detection

    Identify racks or modules degrading faster than the site average instantly. SHAP explainability pinpoints the exact root cause of fade, whether driven by thermal stress, depth of discharge patterns, C-rate abuse, or manufacturing outliers.

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