Milo! smart talking toys

Flow chart
inside of dino

Problem

Today, schools use over 2,500 EdTech tools, up from just 300 in 2017. Yet we're facing a crisis in early childhood development. EdTech lacks human touch, parents are desperate for alternatives to pure screen time, and current solutions miss crucial developmental needs.

The research is clear: infants and toddlers learn through social cues and physical interaction. Digital media alone fails because, quote, "babies and toddlers seldom learn from digital media exposure." This is why we created MILO - combining physical touch with AI responsiveness to match how children naturally learn.

Solution

MILO is a revolutionary AI companion that combines hardware and software to create a safe, interactive learning environment. At its core is a secure Raspberry Pi system that handles real-time voice, video, and audio interactions with your child. Parents maintain full control through a dashboard where they can set learning topics, customize instructions, and monitor progress. All processing happens locally - your child's data never leaves the device.

We're at a critical inflection point. Children now encounter technology as early as 4 months old, compared to age 4 in the 1970s. This represents a massive untapped market for infant and toddler educational AI, with parents actively seeking solutions in a growing EdTech market with proven demand.

Teck stack

Clerk for authetication and user login
Elven Labs for Voice agent
Vercel for hosting
- Lovable to prototype frontend
- Rasberry Pi connected to microphone streaming input, speaker streaming output, camera streaming video

Team members

Selali Adobor: 14 YoE as a SWE, coding since I was 12, self-taught, past technical lead @ Zoox and Peloton where he designed human machine interfaces for SOTA AI applications in autonomous vehicles and consumer products

Abel Seba: Autistic, grew up in farm in the Amazon making wood toys for kids with my dad, 3 yrs of experience teaching kids. CS and Business major. MIT Sandbox winner, Stanford Blockchain Accelerator '23. Ex-Microsoft worked on enterprise and consumer AI products

Marco Qin: CMU Dropout, MLE at Uber, former HackMIT, HackHarvard finalist, researcher at NSF and NASA. Serial Entrepreneur