SaaS Charge operates a comprehensive white-label EV charging platform serving multiple charging networks across Europe, North America, and Southeast Asia. With a database of around 30,000 EV drivers and partnerships across global markets, SaaS Charge provides critical infrastructure management and customer support services for EV drivers and site hosts.
Problem: scaling EV driver support across multiple partner networks
Before implementing an AI customer support chatbot, SaaS Charge relied on traditional call centers for first-level EV driver support. Agents had limited access to technical documentation, partner workflows, and user context such as charging box details and location data.
This led to slow issue resolution, high operational costs, inconsistent support quality, and inefficient escalations. As the company expanded across regions and partner networks, manual support became harder to scale. The MVP supports English as the primary language. The system architecture is designed to allow future expansion into additional languages such as French, German, and Spanish.
Solution: an AI-driven EV charging support assistant
GoML designed and delivered an MVP AI customer support chatbot for EV drivers across multiple charging network partners.
The solution runs on an Agentic AI boilerplate that enables goal driven reasoning, contextual troubleshooting, documentation retrieval, and automated escalation to human support when needed. Instead of fixed scripts, the system dynamically selects actions based on user intent and session context.
Built with WebSocket and REST API support, the chatbot is integration ready for partner applications. This architecture reduces reliance on call centers and provides a scalable foundation for automated support.
Conversational support assistant
- EV drivers can interact with the AI customer support chatbot through chat within partner applications.
- The chatbot handles common support requests such as charging session failures, connector compatibility issues, authentication problems, and general EV charging FAQs.
- The MVP supports English initially, with readiness for expansion into French, German, and Spanish.
Partner identification and context injection
- The AI customer support chatbot automatically identifies the charging network partner associated with the user.
- It injects partner-specific workflows, branding context, and user metadata such as name, charging box ID, and location.
- This ensures responses remain accurate and aligned with the correct partner escalation pathways.
Vector-based technical documentation retrieval
- The AI customer support chatbot retrieves troubleshooting information using semantic search powered by AWS OpenSearch, enabling efficient vector-based document retrieval for contextual responses.
- It processes charging station manuals, process documents, Visio troubleshooting diagrams, vehicle compatibility guides, and partner-specific FAQs.
- This enables accurate, documentation-grounded responses instead of generic chatbot outputs.
Comprehensive interaction logging and analytics
- Every conversation with the AI customer support chatbot is logged for continuous improvement.
- Captured metadata includes timestamp, session ID, partner identifier, charging location, user ID, issue category, resolution outcome, and escalation effectiveness.
- This creates a strong data foundation for analytics and future refinement.
Backend infrastructure and integration
- The MVP includes scalable backend infrastructure for user profiles, partner configurations, and conversation transcript storage.
- It provides secure APIs for escalation workflows and a lightweight chat UI for validation and testing.
- The AI customer support chatbot is deployed on AWS using modern serverless components.
Impact
- 50–60% reduction in first-level support workload for call center agents
- 30–40% faster issue resolution through partner-aware troubleshooting
- 70% improvement in documentation accessibility using vector-based retrieval
About
Before Gen AI and after Gen AI
“With SaaS Charge’s AI customer support chatbot, we transformed first-level EV driver support into a contextual, scalable system that delivers faster troubleshooting and smarter escalation across partner networks.”
Prashanna Rao, Head of Engineering, GoML
Key takeaways for EV charging operators
Common challenges
- Call center support does not scale with network growth
- Partner-specific workflows create complexity
- Lack of documentation access slows resolution
- Manual escalation increases operational costs
Practical guidance
- Build an AI customer support chatbot as the first-level support layer
- Combine partner identification with contextual user metadata
- Use vector search for accurate technical troubleshooting
- Log interactions to continuously improve resolution quality
Ready to modernize EV charging customer support?
Partner with GoML to accelerate the development of scalable EV charging support with AI Matic.
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