PETSOS

Instantly connect panicked pet owners to life-saving guidance and nearby emergency vets—just by speaking.

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Inspiration

This project was inspired by my family friend's loss of their dog during a critical moment of crisis. It shed light on an overlooked issue: emergency pet health service. As a pet owner, it terrifies me that there is no 911 for our loved pets. So with the power of AI - I took on the challenge.

What it does

PET911 is a voice-activated emergency assistant that guides pet owners through life-threatening situations — completely hands-free. It simulates a real 911-style call for pets and responds with calm, intelligent, and timely support. Here's what it does:

🗣️ Understands your voice in real-time Users can call a real phone number and speak naturally. PET911 uses voice streaming through Vapi to transcribe speech instantly and maintain a fluid, multi-turn conversation.

🤖 Provides life-saving guidance using AI Powered by Claude, the assistant triages emergencies by asking crucial questions and delivering context-specific, step-by-step instructions (e.g., performing the Heimlich maneuver for a choking dog based on its breed and weight).

🧠 Remembers what you said The assistant uses memory from the conversation so it doesn’t repeat itself or forget details like the pet’s condition or size — enabling natural, human-like dialogue even in high-stress moments.

🌐 Finds the closest emergency vet clinic PET911 uses your ZIP code or live coordinates to identify and link you to the closest open emergency clinic—ensuring fast, location-aware care when every second counts.

📍 Sends an SMS with directions PET911 identifies a nearby emergency clinic and sends a text message with a direct link to the location. This allows the user to begin navigation immediately — skipping the stress of searching online or fumbling with maps during a crisis.

🔄 Transfers to a live vet If needed, PET911 offers to transfer the call to a pre-set backup assistant, simulating the experience of speaking with a real vet receptionist in the area — for needed escalation.

❤️ Offers emotional support The AI is designed to be calm, empathetic, and reassuring — because pet emergencies are overwhelming, and users need to feel safe and heard.

How we built it

PET911 runs on a modular multi-agent system built with a Flask backend. A voice interface powered by Vapi captures real-time input, which is routed through specialized agents: a Triage Agent for rapid emergency assessment, a Memory Agent for contextual continuity, and a Decision Agent to determine next steps. Claude serves as the core reasoning engine. For location support, we integrated OpenStreetMap, and emergency routing is handled via SMS through Textbelt.

Challenges we ran into

We hit roadblocks integrating Fetch.ai’s Python SDK due to package conflicts, which led us to rethink how we handled real-time vet discovery. Vapi's real-time voice streaming also came with unexpected 404 webhook issues and delayed responses, requiring deep debugging. On top of that, prompting Claude for step-by-step, non-overwhelming instructions demanded careful prompt engineering to simulate a calm, conversational emergency assistant.

Accomplishments that we're proud of

  • Built a fully functional voice AI emergency system from scratch using Vapi and Claude, with natural, multi-turn memory and contextual awareness.
  • Integrated a simulated agent ecosystem combining triage, decision-making, and location support under emergency pressure.
  • Engineered real-time responses that adapt to user input instead of overwhelming them—just like a real 911 operator would.
  • Successfully routed emergency info via SMS, with geolocation-based vet directions and calming follow-ups.
  • Debugged complex streaming issues and webhooks under time pressure and got everything running seamlessly across tools.

What we learned

Building PET911 taught me how to design for urgency, empathy, and clarity under pressure. I deepened my skills in real-time voice interaction, multi-agent system design, and external API integration. I also learned how to troubleshoot complex streaming and webhook errors, all while keeping the user experience as the top priority—especially in life-or-death scenarios.

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