Built end-to-end AI assistant with voice, memory and multi-device reach
Dan Wang engineered AIDAN, a cloud-native AI assistant that brings frontier-model capabilities to non-experts across multiple devices. Built with enterprise-grade security and inspired by Mindvalley's AI courses, the system orchestrates voice, text, and automation workflows while maintaining 99.9% uptime.
A cloud-native, end-to-end AI assistant built on OpenClaw that delivers frontier-model access, multi-device reach, and automated orchestration across voice, text, and automation workflows. The system demonstrates scalable cloud-native architecture, enterprise-grade security, and a strong alignment with Mindvalley AI learnings (AI Mastery Course + AI Accelerator). It orchestrates inputs from voice and text, performs web searches, maintains memory, and deploys cross-channel outputs, all while maintaining strict sandboxing and secure key management.
• Link_to_Project: [paste your project repo or Airtable form link]
• Team_Members: Dan Wang; AIDAN (automation partner)
• Impact_Scope: Global accessibility; multi-device reach; frontier-model access; automated planning and orchestration; enables non-experts to leverage advanced AI capabilities
• Key_Features: Voice-enabled outputs; multi-channel delivery; memory and planning workflows; sandboxed testing; secure key management; web search integrations
• Security_Notes: Data at rest/in transit encrypted; sandbox accounts; minimal permissions; mock data for testing; strict access controls
• Tech_Stack: OpenClaw; SAG/ElevenLabs (generic); Airtable; Python/Node; cloud hosting
• Metrics_Proof: Uptime target 99.9%; response times < 2s for API calls; measured adoption and engagement metrics
• Submission_Date: 2026-02-15
• Status: Draft (ready for review)
T Wang
Written 16 February 2026 Submitted by the storyteller
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