Energy
Know your assets, networks and customers better
Connect every data source, build models in days and keep the results explainable and on site.
Fast by design
Full network data reconciled in hours, not days.
Data stays put
On-premise or private cloud. Metering, asset and customer data never leave your infrastructure.
Built for scale
Processes billions of records a day on a standard server.
Our approach
Built for what industry actually needs.
Anatella | Data prep engine
Connect to any sources
Meter and sensor data, billing, customer and market files. Anatella reads any sources and formats.
Processes at network scale
Billions of readings on one standard server, with no sampling and no cluster to run.
Keep control of your data
Deploy on-premise, in a private cloud, at the edge or fully air-gapped.
Modeler | Predictive Engine
Predict what matters
Demand, consumption, outages, churn or asset faults. Modeler builds binary, continuous and multi-class models automatically.
Explain every prediction
White-box models show each variable and weight, so you can trace every result.
Watch models in production
The built-in Drift Report flags when a model stops holding as seasons, usage or equipment change.
SOLUTIONS
What it looks like in production.
95% productivity gain on billing
Codensa processes 1.8 billion IoT data points per month on a single server, in real time.
Billing errors found in 3 days, not 2 months
Codensa cut its monthly billing processing from 1,520 to 80 hours, a 19x reduction.
1,400+ sensors modeled per battery stress test
Volvo isolates which variables degrade EV batteries under thermal shock.
Problems solved
What energy companies fix with TIMi.
Data scattered across systems
Metering, operations, billing, customer and market data sit in separate silos. You connect them in one pipeline.
Volumes that outgrow your tools
Meters and sensors generate billions of readings. You process them on one standard server, without sampling or a cluster.
Faults found too late
Asset failures, losses and billing errors often surface after the damage. You flag them early, from your own data.
Forecasts you can’t explain
Operators, auditors and regulators ask why a model decided. You show each variable and its weight.
FAQ
Common questions.
What is TIMi and how does the energy sector use it?
TIMi is a European analytics platform that prepares data, builds predictive models and explains them, all on your own infrastructure. Energy companies use it to process meter and sensor data, predict asset failures and speed up billing, as Enel Codensa and GX Labs do.
Can TIMi predict equipment failures and reduce maintenance costs?
Yes. Modeler builds predictive maintenance models from sensor data. GX Labs predicts pump failures across 5,000 remote oil wells, avoiding €19.6M in repair costs and adding €1.3M in revenue per year.
Can energy companies keep their data on-premise with TIMi?
Yes. TIMi runs on-premise, in a private cloud, at the edge or fully air-gapped. Operational, metering and customer data stay inside your perimeter, with no external cloud dependency.
Are TIMi models explainable enough for regulated environments?
Yes. Modeler is white-box: every variable is explained, every weight is documented and every decision is auditable, in line with GDPR and the AI Act.
How does TIMi handle smart meter and sensor data at scale?
TIMi processes billions of records on a single standard server, without a cluster. Enel Codensa processes 1.8 billion IoT data points per month on one server, in real time.
How long does an analytics project with TIMi take?
A first working model can take days, not months. In other sectors, a pilot ran in 3 days (Minsalud) and a first predictive maintenance model was deployed in under a week (Safran).
SEE FOR YOURSELF
Don’t take our word for it.
Run TIMi on your own data with a free trial or request a live demo.
