Pigeon

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Pigeon_Dashboard
Snowflake_Database
Ticket_Sample_Output

Inspiration

EV charging downtime is frustrating for everyone. Nearly 1 in 5 sessions fails due to frozen screens or stuck cables. Our team, with backgrounds in automation and transportation AI, saw a clear gap: chargers don’t have the self-diagnosis and recovery intelligence that modern infrastructure deserves. We set out to change that.

What it does

Data Pigeon adds an AI reliability layer to EV charging networks. It combines a self-healing software system that automatically detects and resolves issues with an AI voice agent that assists drivers in real-time rebooting chargers, guiding troubleshooting, and escalating only when needed.

How we built it

We used Whisper AI for live transcription, LLM-based reasoning + RAG for intelligent triage, and Python + Snowflake + Supabase for backend integration. A lightweight OCPP simulation tests remote actions like reboots and resets, while a React dashboard provides operator visibility.

Challenges we ran into

Access to authentic OCPP logs was a tough hurdle, as most operators restrict them for security reasons. And so, we had to synthesize our own logs, which may not be as accurate or large enough to train the model to an accuracy in prediction.

Accomplishments that we're proud of

We built a working prototype that triages driver issues end-to-end, from voice intake to backend action. We’ve also conducted 20+ interviews with CPOs, aligning our roadmap with real operational pain points, and secured spots in multiple accelerators and grants.

What we learned

EV reliability isn’t just a data problem. Drivers need empathy; operators need automation they can trust. Blending conversational AI with infrastructure intelligence creates that bridge.

What's next for Pigeon

Next, we’re launching pilot integrations with CPO partners to access real-world OCPP data, refining our AI models for predictive maintenance, and deepening hardware integrations for remote resets, moving closer to truly self-healing EV charging networks.