What Just Happened?
Uare.ai—the startup formerly known as Eternos—raised $10.3M to pivot from memorial “after-life” chatbots to a professional, personal-AI platform that sounds and thinks like you. The company’s core idea is a Human Life Model (HLM): an individualized model trained only on your text, voice, and video—no general internet data. If the model doesn’t know something, it says “I don’t know,” rather than making it up from a general model.
This matters because it flips the usual AI pattern. Most large language models (LLMs) blend your data with vast public corpora. Uare.ai is betting that tighter fidelity to an individual’s life story, values, and decision-making can unlock new forms of business automation—especially for professionals who sell expertise.
The team is positioning HLMs as working “digital twins” that can draft content, answer customer questions, and reflect your judgment. Monetization will come via subscriptions or a revenue share if your model earns income. In short: from legacy preservation to a productivity tool designed for creators, CPAs, and independent professionals who want scale without losing their voice.
Why this is different
Unlike Character.ai or other celebrity-style chatbots, Uare.ai emphasizes ownership, professional use-cases, and strict reliance on your data. It’s not trying to entertain; it’s trying to help you work. That means fewer hallucinations and tighter guardrails—but also knowledge gaps when your data doesn’t cover a question.
Competitively, this puts Uare.ai alongside players like Delphi (which hosts replicas for public figures) while carving out a niche for working pros. Backers include Mayfield and Boldstart Ventures, and the founder, Robert LoCascio (ex-LivePerson), brings deep experience building enterprise-scale conversational systems.
How HLMs get built
The platform will prompt users through structured interviews—text, voice, and video—to capture life story, career, and practical know-how. Think “tell me about a pivotal client moment” plus “how do you triage inbound requests?” Then it blends facts (credentials, processes, preferences) with story and style.
The promise: a model that can write emails, draft posts, or handle routine inquiries in your voice. The trade-off: because it’s trained only on you, it won’t answer outside your dataset. That forces clarity on scope—and potentially better trust.
How This Impacts Your Startup
For early-stage startups
If you’re wearing every hat, a personal model that mirrors your judgment can be a force multiplier. The practical upside is targeted business automation: triage inbound leads, draft proposals, and keep your pipeline warm without diluting your tone. It won’t replace your strategic calls, but it can cover the repetitive 60–70% of communication that burns time.
The constraint is data. HLMs depend on what you feed them. If your processes aren’t documented, you’ll need to invest in capturing playbooks, examples, and edge cases. A lean founder can treat this as “train while you work”: record Looms, save annotated emails, and log answers to recurring questions.
For creators and individual pros
Creators, coaches, CPAs, lawyers, consultants—anyone selling expertise—can use an HLM to scale interactions without outsourcing their voice. Think: a digital twin that drafts your newsletter, answers FAQs, and books clients following your rules. Because it’s trained on your words, the tone and boundaries should hold better than generic AI assistants.
Monetization is a twist here. Uare.ai plans to support revenue sharing if your digital twin earns income—say via paid Q&A or private advisory access. This mirrors the “cameo for knowledge” model we’ve seen from Delphi, but with deeper fidelity to your work style and decision heuristics.
For SMB operators and teams
HLMs could become internal knowledge hubs for onboarding and handoffs. Imagine new hires querying your senior PM’s model: “How do we scope a change order for Acme Corp?” You get continuity when people rotate off projects, and training costs shrink.
Customer-facing, a named “replica” of your head of support could handle first-line questions in their tone, escalate exceptions, and log insights in your CRM. The catch: you’ll need tight integration—email, CRM, help desk—and clear policies on what the model can and can’t do.
Competitive landscape changes
We’re moving from generic copilots to owned, personal AI that reflects a person or brand. That’s a differentiation play: your competitor’s chatbot sounds like a bot; yours sounds like you—and respects your judgment. For categories where trust and tone drive conversion (advisory services, boutique agencies, premium creators), this matters.
Expect incumbents—Character.ai, Delphi, and mainstream LLM vendors—to push into this “work twin” territory. The edge for Uare.ai is the “your data only” stance and monetization pathways. The risk: coverage gaps compared with hybrids that quietly fall back to general knowledge.
Practical risks and considerations
Coverage vs. fidelity: An HLM’s strength (staying in your lane) is also its weakness. If your content is sparse, answers will be thin. Plan for periodic “knowledge sprints” to enrich the model with real client scenarios, decisions, and outcomes.
Privacy and consent: Training on emails, client notes, or recordings can raise data rights issues. Create a consent checklist: client approvals, data minimization, and a deletion policy. If you operate in regulated domains, loop in counsel early.
Cost and workflow: High-fidelity capture (voice, video, interviews) takes time. Budget 10–25 hours up front and recurring updates. Also, map where the twin plugs into your stack—Gmail, Slack, CRM, help desk—and who approves its outputs.
Hallucination control: The “I don’t know” behavior is good. Maintain it. Avoid silent fallbacks to general LLMs without labeling. If you later add retrieval-augmented generation (RAG) from your knowledge base, keep clear provenance: what came from you vs. sourced docs.
IP ownership and monetization: Uare.ai says you own the model. Get that in writing. Understand how revenue shares work, what metrics are tracked, and what happens if you leave the platform.
What founders should do now
Define a narrow job for your twin: “Draft client updates and triage inbound,” not “be me.” Narrow scope means faster value and fewer mistakes. Expand gradually.
Curate training data: Collect 20–30 great emails, 5–10 process write-ups, 3–5 recorded calls with commentary. Add “why I chose X over Y” notes—those encode your judgment.
Set guardrails: Create a style and decision guide: tone, refusal cases, pricing boundaries, escalation triggers. Your HLM is only as good as its rules.
Measure impact: Track time saved, lead response times, CSAT, and error rates. Treat the model like a junior team member with KPIs.
Plan the blend: Consider combining the HLM with your company knowledge via labeled RAG so the model stays personal but has depth. Make the blend explicit to users.
A realistic outlook
This is not magic. A 25-hour interview won’t capture your entire “life model,” and a personal-only dataset will leave holes. But for many startups, the goal isn’t omniscience—it’s trusted, on-brand automation for routine work.
If Uare.ai delivers on ownership, voice fidelity, and monetization options, it could become a practical alternative to generic copilots. The winners will be founders who treat their digital twin as a system: curated data in, clear scope, measured outputs, and continuous improvement. Done that way, an HLM can give you hours back every week—and keep your business sounding unmistakably like you.
