SageCore

Machine learning for operating data.

Fleets · Industry · Energy

The Engine

Every machine,
compared with itself.

Fleets and plants already have telematics, cameras, sensors and a historian. The data is checked against fixed limits, so alarms come too late or too often. SageCore learns how each vehicle and each machine behaves in every operating state and warns when it drifts from its own normal, before it turns into downtime, an accident or a cost.

In production since December 2024.

0vehicles and machines live
0records a day
0tracker and camera protocols
0watching, without pause

Platforms

One engine,
three products.

The same core runs real-time ingestion, the models, the alerts and the AI agents that bring the answer to whoever decides.

Live

FleetSage

Fleet intelligence. Telematics, onboard cameras with AI, driver behaviour, routes and maintenance by actual use, with a digital twin for every truck.

fleetsage.ai
Live

CANAi

Condition monitoring for heavy rotating machinery. Turbines, gearboxes and generators with wireless sensors, a 3D twin and a health score per machine.

canai.com.br
Live

TribeOps

AI agents on WhatsApp that answer customers and operations teams with the real data of the operation, every hour of every day.

tribeops.ai

FleetSage DNA

A digital twin
for every truck.

Component health and predicted maintenance, drawn on the vehicle itself. Telemetry, camera events and checklists feed one living profile per truck.

The real truck
the vehicle
Truck data layers
its data
The digital twin
its twin
FleetSage DNA showing a truck with component health

Digital twins · CANAi

A twin that knows
each machine's normal.

Each asset gets a digital twin that is a model, not a drawing: it learns how that machine runs at every load and condition, carries its sensors and its limits, and flags the reading that does not fit. Real screens from a sugar and ethanol mill in north-east Brazil, live since October 2026.

3D digital twins of a gearbox, a motor and a steam turbine
Each twin carries the asset's sensors, its health score and its trend
Bearing vibration of a steam turbine over 7 days
What the twin reads: turbine bearings, 7 days, per axis, against that machine's own limits
to the minutestops detected by the twins matched the plant's official log
0points requested one week after installation

Machine learning in production

Not in a lab.
In the field.

01

State

Detects on its own whether a machine is stopped, starting, at partial or at full load.

02

Signature

Learns what every variable should read in that state, given load and ambient conditions. What is left over is what matters.

03

Correlation

Groups deviations that move together across variables and machines, by likely failure mode.

04

Decision

A 0 to 100 health score, the stage of the deviation and a maintenance window. Never a fake countdown to failure.

In production with

Companies that run
on our technology.

Backed by

Google for Startups

Selected for Google for Startups: cloud credits for AI on the same infrastructure that runs the engine.

NVIDIA Inception

Member of NVIDIA Inception, the programme for startups building with AI and accelerated computing.

Hydroelectric generator hall with data flowing from each unit

Next

Power generation.

The same engine, reading the historian a generator already has. Read only, one signature per operating regime, and a sensor only where a signal is missing. From steam turbines to hydro, solar and wind.

Millions of signals.
One early warning.

Most tools compare a signal with a limit.
SageCore compares each machine with itself.

Contact

Let's look at
your data.

Fleets, plants, power generation or partnerships. It reaches a human.