Proactive Voice Safety Assistant
This challenge focuses on developing a modern, proactive voice-enabled AI assistant designed to provide safety and well-being checks for individuals living alone. Unlike a simple button press, your solution should leverage advanced conversational AI, real-time voice synthesis/recognition, and agentic workflows to personalize interactions and proactively detect potential issues. The assistant must maintain long-term memory of user preferences and integrate with external alert systems, all while prioritizing user privacy and a natural conversational experience.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on developing a modern, proactive voice-enabled AI assistant designed to provide safety and well-being checks for individuals living alone. Unlike a simple button press, your solution should leverage advanced conversational AI, real-time voice synthesis/recognition, and agentic workflows to personalize interactions and proactively detect potential issues. The assistant must maintain long-term memory of user preferences and integrate with external alert systems, all while prioritizing user privacy and a natural conversational experience.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master real-time voice AI integration using Bland AI for seamless, natural language interactions with low latency.
Implement sophisticated conversational logic and personalized responses with Claude Sonnet, managing complex dialogue states and intent recognition.
Design proactive agentic workflows using Mindset AI to schedule check-ins, detect anomalies from conversation patterns, and trigger configurable alerts.
Integrate WebAI to allow the voice assistant to interact with web services (e.g., weather updates, news summaries, emergency contact services) based on user requests or proactive needs.
Build a persistent, personalized long-term memory using ChromaDB to store user preferences, conversation history, and key safety information, enabling highly contextual and empathetic interactions.
How this agent runs
The assistant will be evaluated on its ability to conduct natural, personalized conversations, successfully perform scheduled check-ins, detect predefined 'distress' indicators in speech/text, and accurately trigger a...
Challenge input
{'user_id': 'string', 'initial_prompt': 'string', 'memory_state': {'last_interaction_topic': 'string', 'preferred_greeting': 'string'}}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'transcript': [{'speaker': 'user|assistant', 'text': 'string'}], 'alert_triggered': 'boolean'}
- Ensures the average turn-taking latency (user speech end to assistant speech start) is below 1 second.
- Verifies that alerts are triggered correctly for distress scenarios and not for normal interactions.
- PersonalizationScore target: 0.8
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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