Batteries bring the flexibility modern grids depend on. We ensure they scale profitably.

Enterprise AI for BESS profitability

Custom made for traceability and explainability

Built for large scale assets with full lifecycle support

For The BESS Ecosystem

  • Developers
  • Owners
  • Operators
  • Financiers
  • Insurers
Teammates AI Harness demo screenshot
odessa-bess-200mw · q2 outlook · post rtc+b
Will we hit Q2 proforma?
Objective: hold Q2 EBITDA at proforma, availability above 97%
CEO 8m ago
@reliability How certain are we about meeting Q2 proforma post RTC+B?
@reliability AI 5m ago
+0s pulled SCADA telemetry, last 90 days
+1m cross-checked NOAA weather forecast, Apr 28 to 30
+3m modeled C7 thermal derate against ERCOT load
Two swing days at risk: Apr 28 and Apr 30. ERCOT peak load coincides with C7 thermal derate trend. Availability projects to 95.8% versus 97% guarantee. LD exposure ~$38k. Routing to @engineering with the maintenance brief.
Sources: SCADA, ERCOT, NOAA → Amperical BESS AI model
@engineering AI just now
Acknowledged. Pulling C7 HVAC service forward to Apr 26, blocking dispatch in that window. Handing to @qse to re-bid Apr 28 and 30 conservatively.
Sources: OEM schedule, SCADA, dispatch logs → Amperical BESS AI model

The Problem

The asset doesn't read the proforma

Markets, weather, and cells write the actual P&L.

Where problems

Location and product mix

  • Origination and interconnection dictate which markets the asset can reach
  • Winning product mix shifts as grids evolve
  • Regulations and incentives shape the proforma

What problems

Specs and design

  • OEM, sizing, augmentation cadence locked at design
  • Specs lock in the trajectory for life
  • Hard to reverse, choices compound for 20 years

How problems

Operations

  • Unplanned maintenance erodes availability
  • Value stream mix shifts realtime across energy, ancillary, capacity
  • Shifting supply-demand dynamics compress spreads

Product

Intelligence for BESS Proforma Reconciliation

Simulate pre-COD, then track proforma vs actual for the full lifetime.

Origination

  • Grid congestion screening
  • Value stack per market
  • Incentives and tariffs mapping

Simulations

  • Pre-bid P&L scenarios
  • True cost discovery
  • Sensitivity analysis

Tracking

  • Live proforma vs actual
  • Augmentation and availability tracking
  • Early red flag alerts

How It Works

Battery trained AI models

Modeling Approach

Machine learning trained on real battery operations.

Spreadsheets and MATLAB lack machine learning. Generic AI lacks domain. We sit where deep BESS expertise meets modern AI.

ML + AI → BESS expertise → Spreadsheets MATLAB Energy analytics Generic AI Amperical
Neural network architecture: customer telemetry, location, design and specs flow through battery trained ML layers to produce actionable intelligence

Training Pipeline

Pre-trained, fine-tuned, expert-validated

Pre-training
Lab cycling data, simulated conditions
→
Fine-tuning
Historical telemetry, per-project
→
Expert Validation
Domain experts + engineering guardrails

Team

Combining deep energy + AI expertise

Rachana Vidhi, PhD

Co-founder & President

  • 10 US patents in batteries and renewables enabling 1,000MW+ of BESS deployments
  • PhD Chemical Engineering (USF), Master's in Management (Harvard), IIT Kharagpur
  • Former Director, NextEra Energy
Rachana in Florida Inventors Hall of Fame →

Indresh Kumar

Co-founder & CEO

  • Pioneered BESS analytics at Ion Energy: Amazon backed, scaled to 600MWh+ AUM
  • Developed battery fleet operations AI/ML models at VoltUp
  • Technical co-founder: AI, ML and data models
Indresh on ~600MWh BESS analytics in California →