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EV Charging Diagnostic AI Assistant
Title: AI-Powered Diagnostic Assistant for EV Charging Infrastructure
Problem: Ecoplug Energy needed a way to diagnose EV charger faults in real-time and let users trigger remote actions (like initiating charging) via natural language — without needing a human support agent on every call.
My approach: I built a Generative AI–powered diagnostic assistant with intent-driven conversational workflows for real-time fault detection, plus a WhatsApp interface that translates natural-language commands into backend actions. The core was an async FastAPI decision-engine with priority-based routing (Diagnostic → Action → Intent → AI), combining regex extraction, fuzzy matching, and O(1) indexed lookup across 598+ diagnostic codes.
Tools used: FastAPI, Python, AWS (EC2, S3, Lambda), Terraform, MongoDB, Pydantic, async architecture
Result: Shipped a production-grade, scalable REST platform with confidence scoring and text normalization, deployed via Render Blueprints with a live GitHub Pages demo — giving users instant fault diagnosis and remote charging control through a conversational interface.
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Built on Bubble AI generated foundation and taken the rest of the way by hand:
→ Started with a single prompt to Bubble AI, shaped by Claude;
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🎬 See full process walkthrough and comparison between Bubble's initial output and my design in the comments.
🔗 Try Aura
Process walkthrough:
A place to post difficult decisions, get confidence-weighted advice from the community, and receive an AI Decision Brief that summarizes the best arguments before you decide.
Built from a Bubble AI-generated foundation and expanded into a fully functional app.
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What do you think about this layout? Feel free to share your feedback or ideas to make it better!
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