Trial
Start in just a few minutes
Upload your first CSV and work directly in the browser.
⚡ Up to 25% lower charging costs
Understand charging windows better. And avoid peaks more deliberately.
Is your fleet charging exactly when site load and electricity prices are already high? Analyze charging, load, and timing data to see faster which windows, routines, and peak patterns are creating avoidable cost.
14-day trialRuns in the browserCSV instead of an integration project
Up to
25%
potential electricity cost savings when charging windows and peak patterns are compared with data instead of being managed purely by habit.
Before
- • Charging sessions collide with already high site load
- • Favorable and unfavorable windows are not compared cleanly
- • Price and peak patterns stay hard to see day to day
After
- • See when charging power collides with site load
- • Compare charging windows by cost and utilization
- • Derive a better charging strategy from real data
Setup
No local installation
Your team can test dAIve without a rollout or IT project.
Input
CSV from your existing tool
Export data from your current stack and get started immediately.
Support
Personal support via email
support@daive.de
Target✓ Logistics companies with E-fleets ✓ Industry with E-vehicles ✓ Fleet managers ✓ Energy managers
Perfect for
Problem
The Problem
When charging times are set operationally instead of with data, site peaks, expensive windows, and avoidable pressure on connection capacity become normal.
Solution1 2 3
The Solution
dAIve turns charging and load data into a better decision base: expose peaks, compare time windows, and derive a more grounded charging strategy.
See when charging power collides with site load
Identify windows where parallel charging creates peaks and avoidable costs.
Compare charging windows by cost and utilization
Review which routines perform better under similar operating conditions.
Derive a better charging strategy from real data
Use patterns and anomalies to plan charging times more deliberately and explain changes internally.
Next step
Fewer peaks. More clarity for charging decisions.
potential electricity cost savings when charging windows and peak patterns are compared with data instead of being managed purely by habit.
Up to
25%
Workflow1 2 3 4
How it works
From CSV to first actionable insights in under 20 minutes.
2 min
Export charging data
Charging times, load profiles, electricity prices.
1 min
Perform analysis
dAIve detects load patterns, peaks, and unusual time windows.
5-15 min
Compare windows
See which charging periods behave better under similar conditions.
instant
Derive strategy
Use the results for more grounded charging decisions.
FAQ
Use case FAQ
The key questions before you start your trial with your first CSV.
Can I use CSV exports from meters, BMS, or existing energy systems?
Yes. As long as you can export values as CSV, you can load them into dAIve and analyze directly.
How quickly do I see first savings opportunities?
Usually during the trial itself, within minutes after upload, target definition, and model training.
Do I need an integration project for the trial?
No. Start in the browser with a CSV export from your current setup.
Next step
Every unchecked charging peak pushes cost and load upward.
Start your 14-day trial and use your first CSV to see which charging windows deserve review first.
14-day trialRuns in the browserCSV instead of an integration project