A flat-price model struggles to compete
With new regulation, the need for location- and time-based dynamic pricing has become a serious priority for charge point operators. The classic flat-price model cannot respond to occupancy, demand and competitor moves, so it falls behind in the market.
A decision layer that proposes dynamic prices
The EV Charging Dynamic Pricing Agent is a decision layer that combines transaction patterns, occupancy telemetry and external variables to propose dynamic prices. Regulatory bounds are coded into the system — the recommendation always stays within the permitted range.
From telemetry to price, one pipeline
Telemetry integration
Station occupancy, session duration and energy draw data flow into the decision layer in real time.
Demand forecasting
Hourly and location-based demand forecasts price in peak and off-peak periods ahead of time.
Competitor price monitoring
Price moves at nearby stations are tracked; positioning decisions are backed by data.
Price recommendation engine
All signals come together to produce a compliance-guaranteed price recommendation held within regulatory bounds.
Ideal for independent charge point operators, the charging business units of energy companies, and mall and hotel charging stations.
The sector where this agent is deployed or directly applicable.
Frequently asked questions
Quick answers to the most common questions.
Can a recommendation exceed the regulatory bound?
No. Regulatory bounds are coded into the system; the recommendation always stays within the bound.
Does it integrate with our existing station infrastructure?
Yes. It integrates with your existing charging network through occupancy telemetry and transaction data.
How long until a working POC?
1–2 weeks. We scope on a 30-minute call and build a working POC on your own data.
Let’s start with a working POC in 1–2 weeks
A 30-minute discovery call. We listen — not pitch. In the following week, we design a working POC on your data.