The startup that once specialized in creating digital metabolisms for the afterlife is pivoting to pursue a living market opportunity: personal AIs that speak in your voice and share your judgment. The company, now called Uare.ai, announced a personal “AI twin” platform and $10.3 million in seed funding from Mayfield, Boldstart Ventures and others.
Started by LivePerson founder Robert LoCascio, it was conceived as a means to preserve memories and personality for loved ones. The group’s early, heavily publicized work with the customer Michael Bommer, who appeared in some 25 hours of interviews designed to build his digital self and gravestone, had confirmed something unexpected: most inbound interest didn’t focus on memorialization — it was about productivity, scale and presence while you’re still here.
From Digital Legacy to Personal AI at Work Today
Uare.ai’s bet is that the world of most personal value to you isn’t a generic redesign of your chatbot overlord with your face on it, but instead an AI built based only on the training data marathon (past decisions) and style (aesthetic space) fit for kings: yourself. The company refers to this representation as the Human Life Model — a way of capturing your values, life stories and professional heuristics without relying on general-purpose training data to allow the “fill in the gaps.”
That limitation is a strategic differentiator. If the system does not know something about you, it says I don’t know rather than making things up. In a climate where hallucinations can erode brand confidence, a refusal to speculate may be less of a bug and more of a feature.
Inside the Human Life Model for Personal AI Twins
Onboarding opens with a series of guided prompts to canvass formative experiences and decision patterns: childhood anecdotes, early crossroads moments, model figures, personal failures that did (or didn’t) shape your relationship to risk. And then users add professional context — work samples, talk track scripts, bios, FAQ sheets and itinerary slots — along with voice clips and optional visuals to provide tone of voice, cadence notes and nonverbal cues.
(The aim is a functioning doppelgänger, one that sounds like you and acts the way you would.) Uare.ai imagines creators and professionals using its models to write content, process inbound requests, triage customer interactions, host office hours and even complete bounded projects. Unlike other consumer entertainment-first clones, Uare.ai claims to be a subjective force multiplier that can be quantified based on real results.
Business Model and Market Signal for Uare.ai
Uare.ai intends to make money from subscriptions and revenue sharing with customers who sell access to their digital twins. That fits creator economy dynamics — where direct-to-fan experiences and paid knowledge products already support sizable income streams. Goldman Sachs has put the creator economy at $250B or so today with projections extending to $480B inside a few years, and McKinsey has projected trillions in annual value growth, driven by generative AI (i.e., accelerated content and knowledge work).
For single-owner operators — consultants, coaches, educators and niche influencers — the promise is simple: turn time-bound expertise into scalable, always-on engagement without loss of voice or quality. If the unit economics pencil out, a well-trained personal AI will be both a new revenue line and a front door to services.
A Crowded Field and Uare.ai’s Differentiated Angle
Uare.ai enters a competitive arena. Consumer platforms like Character.ai and ChatGPT brought conversational personas into the open, and voice synthesis companies like ElevenLabs have pushed AI voices further into the mainstream. Delphi, which is backed by Sequoia, has lured several prominent names and allows fans to engage with synthetic knowledge through text and voice. Uare.ai’s pitch is more limited and deeper: first-party data only, a rejection of fabrication, and an emphasis on the professional-grade, at-scale use cases that need accuracy and brand fidelity.
That position may appeal to businesses as well. Executives and domain experts are generally reluctant to sign their names to AI-generated content without knowing where it came from or being able to audit its creation. A model that is designed explicitly to prohibit general knowledge backfill and maintain an auditable chain of inputs could help address those concerns.
Privacy, Ethics, and Ownership in Personal AI
Personal AIs that sound and think like you raise troubling governance considerations: consent, control, misuse. Regulators are paying attention. The FTC has begun to home in on AI-enabled impersonation, while the EU’s AI Act highlights transparency around synthetic media. Uare.ai’s strategy — rooting its outputs in first-party data and welcoming “I don’t know” answers — can help stave off confabulations, but it won’t single-handedly mitigate identity abuse risks.
Best-practice safeguards in this case include:
- Obtaining explicit consent and a way to revoke that consent
- Identity validation for the creation of the model
- Watermarking or cryptographic proof of the provenance of outputs
- Rate limiting to help prevent bot farms
- Clear terms around who owns both the data fed in to train you, as well as any models it generates
Expect potential enterprise customers to ask for data residency and SOC 2–grade security from day one.
What to Watch Next as Uare.ai Expands Its Platform
Uare.ai says its platform will open more widely later in the year. Early signs to watch: how many hours of input it takes for a convincing clone, whether the company can hold onto voice fidelity across channels and, most critically, whether users experience real lift in their revenue, response quality and customer satisfaction.
If Uare.ai can demonstrate that a rigorously grounded personal model is better than a generic assistant in accuracy and trust, its shift from immortality to utility could be a tipping point for how we productize our expertise — without losing our voice.