Intelligent automation of remote assistance for charging infrastructure: improving user experience and reducing operational load

Context: ETECNIC is a technology company specialized in electric vehicle charging infrastructure, combining proprietary software, remote operations, and data management to operate thousands of charging points across different environments and countries.

A significant portion of support interactions occurs because many end users are facing the use of a charging station for the first time. Electric mobility is still a relatively new environment for a substantial segment of users, who interact with a combination of physical and digital elements (charger, cables, payment terminal, mobile application, voice), and who require clear, contextual, and real-time guidance to successfully complete a charging session.

In this context, the growth of the sector and the installed base implies a progressive increase in support interactions. To maintain an excellent and scalable service, ETECNIC aims to evolve its remote assistance model by incorporating automation and applied intelligence in a structural way, focusing on improving user experience as the primary lever to reduce friction and scale the service.

The Challenge: We are looking for an applied technological solution that improves the user experience in the use of charging infrastructure and, as a direct consequence, automates and scales first- and second-level remote assistance, significantly reducing the operational load associated with:

• recurrent operational questions,

• known and repetitive technical incidents,

• non-critical queries that currently require human intervention.

The solution must filter, classify, and resolve a relevant portion of these interactions before they reach a human agent, escalating to humans only truly critical or exceptional cases, always providing prior context.

Specific Functional Scope: The solution should cover the following capabilities:

• Automatic classification of support interactions
  (urgency, impact, recurrence, type of incident).

• Guided and intelligent self-service for resolving known questions and incidents, adapted to the user’s real context and the physical-digital charging environment.

• Management and learning from historical tickets and calls, reducing future recurrences.

• Intelligent prioritization and routing to human agents only when necessary, with prior information and technical context.

• Ability to integrate with technical platforms and operational data sources.

• Scalability and adaptability to new use cases, brands, models, and future contexts.

• Expected Impact

The success of the challenge will be primarily measured by:

• A clear and measurable improvement in end-user experience, facilitating the use of charging infrastructure in real situations.

• Reduction of friction and doubts during the charging process through guided, contextual, and accessible assistance.

• Decrease in the volume of avoidable calls and tickets, as a direct result of improved user experience.

• Optimization of the support team’s operational workload, reserving human intervention for higher-value cases.

• Reduction of unnecessary interruptions outside business hours.

• The system’s ability to learn and improve through use.

Solutions Sought: We particularly value solutions that:

• are already in use in real operational environments,

• go beyond a generic chatbot or automated FAQ,

• are capable of working with technical, contextual, and real usage data,

• allow for progressive and controlled implementation,

• are designed to enhance human work and improve experience, not replace people.

What We Are Not Looking For:

• Basic chatbots or automated FAQs.

• Purely conceptual or demonstration-only solutions.

• Proposals without real technical integration capability.

• Approaches that shift the burden to the user without solving the problem.

• Projects without measurable operational impact.

Type of Collaboration: This challenge is proposed as a first step toward a potential medium- to long-term technological collaboration, with opportunities for pilots, progressive deployment, and joint evolution if the fit and impact are real.