AI agents are expected to soon start making autonomous purchasing and scheduling decisions on behalf of humans.
But Michael Fanous, a UC Berkeley computer science graduate and former machine learning engineer at CareRev, argues that these agents are currently missing a critical piece of the puzzle: the full context required to truly understand the people they are programmed to serve.
Fanous claims that machines currently struggle to discern whether a person’s professional profile on LinkedIn, their activity on Instagram, and their public government records all belong to the same human being.
To solve this, he teamed up with his father, Emad Fanous, a veteran CTO, to build Nyne, a startup aiming to become the intelligence layer that helps agents understand humans across their entire digital footprint.
On Friday, Nyne announced it raised $5.3 million in seed funding led by Wischoff Ventures and South Park Commons, with participation from several angel investors, including Gil Elbaz, the co-founder of Applied Semantics and a pioneer of Google AdSense.
While it may seem that Nyne is tackling an issue already solved by classic machine learning—given how effective Google’s ad targeting is at identifying its users—CEO Michael Fanous argues otherwise. Google’s “secret sauce” is its exclusive access to users’ search histories and cross-platform activity, a data advantage the tech giant won’t ever share with external agents, he said.
For everyone else, “this is an oddly hard problem to solve,” explained Nichole Wischoff, founder of the solo VC fund Wischoff Ventures, which backed the deal.
Michael told TechCrunch that Nyne tackles the problem by deploying millions of agents across the internet to analyze public digital footprints and then applying machine learning techniques to that data.
Nyne can triangulate information about a person …