Sandy Carter (ex-AWS enterprise AI, ex-IBM Watson, now CEO of EQUS) just launched EQUS X beta — a personal AI assistant with a critical architectural difference: your data never leaves your control.
The core problem she's solving: most agents learn your preferences ("you like pepperoni"), but modern LLMs are learning your decision-making patterns, negotiation style, problem-solving logic, and trust networks. That's incredibly powerful for productivity but catastrophic if that training data leaks or gets repurposed.
Real example: her friend with cancer uploaded Mayo Clinic medical reports to parse treatment options. Useful? Absolutely. But that's exactly the data you don't want in someone else's training corpus or ad targeting pipeline.
EQUS X's architecture: full local data sovereignty. You control sharing permissions, can revoke access, and the model doesn't feed back into centralized training. Same agent convenience (booking trips, managing returns) without turning your behavioral patterns into someone else's dataset.
They're also tackling agent reputation systems — still an unsolved problem. If your agent goes rogue and orders 500 boxes of Kleenex then defaults on payment, who's liable? You or the agent? EQUS has patents around this. Cloudflare engineers apparently told her reputation is one of the hardest infrastructure problems in the agent stack.
She's writing a paper on the ROI of data ownership vs. privacy trade-offs. Her thesis: privacy doesn't have to kill UX. She's studying Stripe's early growth playbook — iterate with a small set of power users instead of shipping half-baked products to everyone.
Demo video in comments if you want to see the actual product architecture.
The core problem she's solving: most agents learn your preferences ("you like pepperoni"), but modern LLMs are learning your decision-making patterns, negotiation style, problem-solving logic, and trust networks. That's incredibly powerful for productivity but catastrophic if that training data leaks or gets repurposed.
Real example: her friend with cancer uploaded Mayo Clinic medical reports to parse treatment options. Useful? Absolutely. But that's exactly the data you don't want in someone else's training corpus or ad targeting pipeline.
EQUS X's architecture: full local data sovereignty. You control sharing permissions, can revoke access, and the model doesn't feed back into centralized training. Same agent convenience (booking trips, managing returns) without turning your behavioral patterns into someone else's dataset.
They're also tackling agent reputation systems — still an unsolved problem. If your agent goes rogue and orders 500 boxes of Kleenex then defaults on payment, who's liable? You or the agent? EQUS has patents around this. Cloudflare engineers apparently told her reputation is one of the hardest infrastructure problems in the agent stack.
She's writing a paper on the ROI of data ownership vs. privacy trade-offs. Her thesis: privacy doesn't have to kill UX. She's studying Stripe's early growth playbook — iterate with a small set of power users instead of shipping half-baked products to everyone.
Demo video in comments if you want to see the actual product architecture.