Investor-Ready AI Device Prototype
Budget: $750 – $1,500 AUD
I’m gearing up to pitch an all-in-one AI device to investors, and I need a rock-solid prototype that runs smoothly on embedded hardware. The centerpiece of this build is advanced natural language processing: the unit has to understand complex queries, keep a fluid dialog, and return meaningful answers in real time. On top of that, secure voice authentication must be baked in from day one—no shortcuts there.
Here’s the flow I envision: you flash the firmware onto a compact board (Raspberry Pi, Jetson Nano or similar), connect a mic-speaker array, and the prototype boots straight into an NLP engine capable of offline or hybrid cloud processing. Once powered, it authenticates the speaker by voiceprint, then continues the conversation just like any top-tier virtual assistant.
I’ll share the full feature roadmap once we sign an NDA, but for now the mission is clear:
• A working embedded build demonstrating robust NLP
• Voice authentication tied into the same pipeline
• Clean, well-commented code and flashing instructions
• A concise demo video or live session proving the above
If you’re comfortable wrangling TensorFlow Lite, PyTorch, or comparable frameworks on resource-constrained boards—and you like pushing the envelope on AI gadgets—let’s talk.
Here’s the flow I envision: you flash the firmware onto a compact board (Raspberry Pi, Jetson Nano or similar), connect a mic-speaker array, and the prototype boots straight into an NLP engine capable of offline or hybrid cloud processing. Once powered, it authenticates the speaker by voiceprint, then continues the conversation just like any top-tier virtual assistant.
I’ll share the full feature roadmap once we sign an NDA, but for now the mission is clear:
• A working embedded build demonstrating robust NLP
• Voice authentication tied into the same pipeline
• Clean, well-commented code and flashing instructions
• A concise demo video or live session proving the above
If you’re comfortable wrangling TensorFlow Lite, PyTorch, or comparable frameworks on resource-constrained boards—and you like pushing the envelope on AI gadgets—let’s talk.