Electric vehicle charging
Use Inveniam’s computer vision solutions to help redefine the future of EV charging. Improve the install experience and achieve Right First Time installations with our innovative AI technologies.
Relevant for private and public charging points
Pre-installation audits can increase Right First Time installations and help avoid multiple visits. Use visual automation to determine the suitability of residential or business environments for the EV charging equipment and identify any specific requirements the engineer should be aware of prior to the install.
Seamless EV station installations
Our visual automation solution helps to guarantee Right First Time EV station installations for home and public charging stations. Work with Inveniam to ensure that key installation tasks are completed to the required standard, and automate compliance checks. Detect and resolve issues when the engineer is on-site, eliminating re-work and roll-out delays.
Manage queues more efficiently by predicting the charging duration for each vehicle with our AI-powered solution. This enables operators to streamline the flow of vehicles, reduce waiting time, and improve the overall user experience.
Autonomous vehicle charging
With the transition to autonomous vehicle charging, there is an opportunity for EV charging specialists to incorporate computer vision into their charging solutions. This can help provide the most efficient and best possible experience for customers when charging their vehicles.
How our AI model works
See the benefits for yourself
Share visual data for review and align on use cases, detections, data structure, frequency of analysis and success measures.
Start uploading data to our platform where you will be able to track progress across the data pipeline, review dashboards and key results.
We use human annotators to start analysing images if there is no pre-trained model. In parallel, we kick-off model exploration and training for each detection case.
Transition to a fully automated model, with human-in-the-loop intervention used for edge cases and where 100% accuracy levels are required.