Asset intelligence
Model any reporting equipment with its own streams, signals, location, dashboards, alerts, and operating context.
Asset intelligence · battery-native
Know every asset.
Go deeper on batteries.
QuantaFlare gives every reporting asset an AI harness: continuous monitoring, contextual analysis, and generative data tools—plus cycle-accurate health and remaining-life intelligence for batteries.
QSense or your hardware
Live + historical context
Fleet to single asset
EV FLEET / VEHICLE 17
Healthy
TL75EV-1B
State of charge
78.3%
Pack48.2 V
Temperature31.4 °C
Power6.1 kW
Energy today
24 hours
FlareWatchNo active risks
Ask this asset…
One platform, two kinds of depth
Broad enough for the fleet.
Deep enough for the pack.
Battery-native intelligence
Go beyond BMS gauges with cycle-aware health, equivalent full cycles, degradation, efficiency, thermal behavior, and remaining useful life.
From signal to decision
One operating context, end to end.
Connect with our edge hardware or your own. QuantaCloud keeps telemetry, asset identity, calculations, alerts, and AI in the same context.
01
Connect
QSense, CAN, Modbus, REST API, or direct streams. Bring our edge or bring yours.
QSense + Direct API 02Understand
Live views, history, maps, smart summaries, custom dashboards, and battery calculators.
QuantaCloud 03Respond
Monitor with FlareWatch, investigate and generate with FlareBot, then move toward governed action with FlarePilot.
AI harness FlareBot capability · Experimental
Inside the Flare AI harness
FlareBot can build the analysis.
Inside the asset context.
On-Demand Analysis is the generative workspace inside FlareBot. An operator can ask for it directly, or FlareBot can use it during an investigation. The result is a live plot, table, or analysis surface inside QuantaCloud.
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One harness, shared contextThe active unit, stream catalog, signal names, units, and recent summary travel with every investigation.
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Operator- or bot-initiatedAsk directly when exploring, or let FlareBot generate the missing view while it works through a question.
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Working artifacts, not just chatRefine the time window, add a signal, compare periods, or repair the analysis without starting over.
Bulk telemetry stays in the platform data path; the model works from catalog and summary context.
Electric Car Kito
QF Production
Fleet Demo
9 keys / 2 streams
Data catalog
Plot the last 24 hours of voltage with temperature
Voltage + temperature
SOC histogram
Current correlation
Voltage & temperatureLast 24 hours · 5-minute average
VoltageTemperature
Temperature followed the afternoon discharge peak with a 22-minute lag. No excursion crossed the configured operating range.
BATTERY HEALTH / PACK 17
high confidence
92.4%State of health
Estimated remaining life3.8 yearsor 1,420 equivalent cycles
Equivalent full cycles684.7
Roundtrip efficiency94.1%
Cell spread18 mV
Thermal delta3.2 °C
SOH trendCycle-aware degradation
The battery advantage
Derived from raw.
Explained in context.
Most platforms stop at voltage, current, and the SOC reported by the BMS. QuantaFlare builds cycle history and health intelligence from the telemetry underneath it.
SOH + remaining lifeCapacity trend, degradation rate, confidence, RUL in time and cycles.
Cycle-accurate historyFull, partial, and equivalent full cycles reconstructed from raw data.
Efficiency + stressCharge/discharge efficiency, depth of discharge, temperature, and cell balance.
Recompute LensInspect how raw windows become calculated health and cycle outcomes.
The Flare AI harness
Observe. Understand. Act.
One asset context flows from continuous monitoring through analysis and generation—and, with FlarePilot, toward governed action.
Always on
Monitor
FlareWatch
Continuously evaluates real-time data for threshold breaches, missing signals, and battery-specific anomalies.
- Configurable rules and persistence windows
- Severity-scored alerts
- Fleet and unit-level workflows
Available now
Analyze + generate
FlareBot
Moves with the operator through QuantaCloud, carrying the current unit and stream into every investigation and follow-up.
Experimental
On-Demand Analysis
Operator- or bot-initiated plots, tables, and live analysis artifacts.
- Contextual analysis cards
- Natural-language investigation
- Generative analysis workspace
Future
Act
FlarePilot
The future action layer: turning findings into recommended, governed workflows with people in control.
- Assisted recommendations
- Human-approved actions
- Traceable operating history
Built around the asset
Different equipment.
The same path to an answer.
17vehicle
EV fleets
Energy, movement, and health in one view.
Connect vehicle location and pack telemetry to trip energy, efficiency, charge behavior, alerts, and remaining life.
Energy storage
Know the pack behind the site KPI.
Track charge and discharge, roundtrip efficiency, thermal behavior, cell balance, and long-horizon degradation.
CANAPIMODBUS
Industrial + robotics
Model the equipment you actually built.
Create custom streams for motors, actuators, environment, and power systems—then monitor them beside the battery.
Deploy your way
A complete path, or the pieces you need.
Start at the edge, connect through the API, operate in the cloud, and keep the fleet in reach on mobile.
QSenseEdge telemetry hardware
QuantaCloudOperations + analytics
Direct APIBring your own hardware
MobileYour fleet in the field
QuantaFlare Mobile
The operating picture travels with you.
See live assets, open a unit, track battery health, and receive FlareWatch alerts from the field.
Get it onGoogle Play
Bring us the messy data
Show us the asset.
We’ll show you the intelligence.
Tell us what reports today, what you wish you knew, and where the team loses time. We’ll walk through the shortest path from telemetry to a useful operating view.
Emailinfo@quantaflare.com
LocationLos Altos, California
BOOK A PLATFORM WALKTHROUGH