Physical systems AI, ready to try.

Multi-horizon energy demand and peak load forecasting with calibrated uncertainty across all 32 Indian states. The model captures seasonal patterns, industrial activity, and weather-driven consumption dynamics.

Problem

Grid operators need reliable day-ahead and intraday forecasts for both energy demand and peak load. Deterministic forecasts leave operators exposed to volatility, and state-level models fail to generalize across diverse consumption patterns.

Approach

A temporal fusion transformer that encodes static state embeddings, seasonal calendars, and exogenous weather features. Quantile output heads produce calibrated 10-90% prediction intervals for both energy (MU) and peak (MW).

Outcome

R² of 0.984 for both targets across 32 states with 90.4% prediction interval coverage. The model maintains high accuracy even at 24-day horizons.

Capabilities

Time-Series IntelligencePhysics-Informed LearningProbabilistic ReasoningDecision & Optimization

Spatiotemporal models that learn from distributed sensor networks to forecast pollutant concentrations across urban and industrial regions, with calibrated uncertainty.

Problem

Air quality monitoring is sparse and uneven. Static interpolation misses local variation, and weather-driven models fail to capture emission-source dynamics.

Approach

We fuse heterogeneous data sources (ground sensors, satellite columns, traffic and weather) into a unified spatiotemporal architecture with attention over both space and time.

Outcome

City-scale forecasts at neighbourhood resolution, with uncertainty bounds that widen naturally where sensor coverage is thin.

Capabilities

Time-Series IntelligenceSensor FusionProbabilistic ReasoningSpatial Intelligence

Electrochemical model hybrids that forecast battery degradation trajectory and remaining useful life from cycling data, temperature profiles and impedance spectra.

Problem

Battery degradation is nonlinear and cell-specific. Empirical models extrapolate poorly and physics-based models are too expensive to run inline.

Approach

A hybrid architecture that embeds simplified electrochemical constraints into a neural ODE, learning degradation dynamics that generalise across cycling conditions.

Outcome

RUL predictions that remain calibrated across different chemistries and operating conditions, with early detection of accelerated degradation modes.

Capabilities

Time-Series IntelligencePhysics-Informed LearningProbabilistic ReasoningEdge & Real-Time AI

A multimodal sensor-fusion stack that forecasts machine failure windows from vibration, acoustic and thermal streams. Designed for industrial deployment and built for operational environments.

Problem

Unplanned downtime costs millions per incident. Rule-based thresholds fire too late, and black-box models leave maintenance teams in the dark.

Approach

Temporal convolution plus attention over heterogeneous sensor channels. Survival-analysis heads output calibrated remaining-useful-life distributions instead of single-point guesses.

Outcome

Early-warning lead times are measured in days, not minutes. Explanations tie back to specific sensor channels the maintenance crew can actually check.

Capabilities

Time-Series IntelligenceSensor FusionProbabilistic ReasoningDecision & OptimizationEdge & Real-Time AISpatial Intelligence