Built to close the adoption gap
EverydayAI exists because most AI training fails. It's generic, surface-level, and disconnected from the actual complexity of professional work. We built something different.
Milind Bhat
Milind Bhat spent 4 years at NVIDIA shipping AI-powered automotive platforms and led a product at Ford Credit that scaled nationally. He built an AI platform for small businesses at Genesis AI, and has founded two companies from scratch. Now he helps owner-operated teams adopt AI without needing an IT department — through Everyday AI's Set Up, Train, Sustain model.
MS in Computer Science, University of Southern California · Ex-NVIDIA · Ex-Ford Credit · Avatar Ignite 2025
Principles that shape every engagement
Domain first, tools second
We start with your work — what you actually do, what decisions you make, what constraints you operate within. The AI tools come later, after we understand the domain.
Application over instruction
We structure every lab so you apply what you learn to real work between sessions. Instruction is the start — application is where learning sticks.
Honest about what AI can't do
We won't oversell AI's capabilities to your team. Building accurate mental models — including limitations — is how you avoid the costly mistakes.
Small enough to matter
Every lab is capped at 7 participants. Every team program is run directly by Milind. We keep engagements small enough to be genuinely useful.
The adoption gap is a translation problem
The AI tools exist. The access is there. The problem is that nobody has taught most professionals how to apply these tools to the nuanced, judgment-intensive work they actually do.
Generic AI training produces generic AI users. What professionals need is domain-specific frameworks — mental models for when AI helps and when it doesn't, prompting patterns that account for the standards of their field, and workflows that integrate AI without creating new failure modes.
That's what EverydayAI builds. Not another introduction to ChatGPT. A real competency that holds up under the pressure of actual professional work.
Ready to close the gap?
Start with a conversation. Tell us what you're working on and where AI is letting you down.