Four layers.
One intelligence chain.

Physics before ML. ML before Intelligence. Intelligence before Audience. A raw 30-second CAN signal enters. A stakeholder decision emerges. Every face is auditable.

Physics → ML → Intelligence → Audience · the sequence is the methodology
01
Lattice
6 sensor streams · unified field intelligence
02
Cube
Physics → ML → Intelligence → Audience
03
Beehive
Network effect · compounds with every asset
04
enerlystAI
5 agentic processes · decision delivered
01
Lattice
BMS · IoT · GPS · Service · Weather
6 sensor streams unified
Physics first
02
Cube
DEKF · PELT · CUSUM · SHAP · Cox PH
Physics → ML → Intelligence
Federated
03
Beehive
Cross-fleet · cohort · survival curves
Every asset makes all smarter
Agent
04
enerlystAI
OEM · Fleet · NBFC · Driver · Regulator
One decision per asset per week
01
Lattice
Data connection

Connects every stakeholder silo. BMS telemetry, IoT, GPS, service records, weather — unified into one field intelligence layer.

02
Cube
Conversion engine

Five transformation faces. Data → Physics → ML → Intelligence → Audience. Physics before ML, always. A raw CAN signal enters. A decision emerges.

03
Beehive
Network effect

Federated learning. Every battery that joins makes every other smarter. No single OEM or NBFC can build this — the intelligence requires every node.

04
enerlystAI
Agent layer

Five agentic processes. One action per stakeholder, in the right language, at the right moment. Not a chatbot. It delivers.

The Network Effect

No single stakeholder can build this. That is the point.

Beehive: every node contributes signal. Every node receives intelligence they cannot generate alone.

You have pack data.

Your lab tested both designs to spec. The field disagrees with one. You cannot see this without operator data — charging patterns, route intensity, thermal exposure. The data lives in a different silo.

The data lives in a different silo.

The operator has behaviour data.

They know which drivers are hard on batteries. They do not have your cell chemistry or commissioning baselines. Attribution requires both sides.

Attribution requires both sides.

The NBFC has outcome data.

They know when batteries failed relative to loan tenure. That is the ground truth that calibrates survival curves. enerlytik connects all three.

enerlytik connects all three.
enerlystAI · Five Processes

From data to decision. Automatically.

01 · NBFC
NPA Management
Weekly agent run across entire portfolio → battery health grade → NPA probability 6–8 weeks before missed EMI → credit committee report. Zero manual intervention.
02 · NBFC
Loan Management
Weekly asset status per loan. Collections prioritisation: Grade C recovering vs declining. Early intervention triggers. Collateral mark-to-market.
03 · NBFC
Loan Origination
Cohort actuarial priors for new applicants. Risk-adjusted tenure recommendations. Operator behaviour profile from telemetry. Grade before disbursement.
04 · OEM
Service + Warranty
Predictive service alerts 4–6 weeks ahead. Warranty attribution in 90 seconds. NPD brief from real field stress. Born-weak detection at Week 4.
05 · OEM → Customer
Range + Second Life
Physics-corrected range today. Remaining useful life in plain language. Charging recommendations. Second life value for grid storage redeployment.

If this is your problem, let's talk.

gaurav@enerlytik.com · Gaurav Mehra · Co-Founder
Talk to us →