Personal AI

Setting the Scene

Imagine a morning routine where pre-drafted texts and emails await your approval, requiring just a tap to send.
- The best part: These responses sound like they were transcribed from your mind. 🤔
Current AI, powered by large language models (LLMs), is trained by big tech on public data, which results in generic responses and privacy concerns.
This week's company develops personal language models (PLMs) trained on personal data, resulting in AI tailored to the individual.🤯

Personal AI enables individuals to build, own, and operate AI models trained on their data.

Build: Users train their models by prompting messages and integrating their websites, texts, documents, emails, and more.
Own: Individuals own and control their models, building privacy and combatting biases.
Operate: Users interact with their model through the desktop and mobile apps or connect to text, email, and socials to auto-draft responses to others.

Personal AI is like a personalized version of ChatGPT, trained to think, learn, and respond like you. 💬

Headquarters: New York, NY
Employee Count: 5
Investors:

Differential Ventures, Supernode Global, BBGV, Jane Street, Village Global, Keshif Ventures, Good Friends, a16z Scout Fund, Beni VC
Funding amount: $8.4m
Business model: Freemium subscription with tiered pricing
Early traction: 50k PLMs created, 11 million memories created, 3.3x daily sessions per subscriber

👤 Tailored to the individual: While large language models are accelerating productivity, Personal AI has the potential to advance this disruption with a quicker-to-train model that better understands the user.
📈 End market: Personal AI targets a broad end user, including small business owners, C-suite executives, authors, and professionals with personal brands who readily invest in tools to manage their knowledge and communication.
- Plus, PLMs are smaller and more cost-effective to develop, which will help the company as it expands.
😄 Retention: The platform demonstrates increasing user engagement with Day 60 retention surpassing Day 30 retention, suggesting that the company re-engages users as their Personal AI gets smarter.

🏋️‍♀️ Personal training: The tool is most useful when the individual has spent enough time training their model by prompting the chatbot and connecting their data.
🥷🏻 Adoption hesitancy: While the company protects user privacy, individuals may be hesitant to create an AI model trained on their own data.
💰 Costly development: AI competitors have required significant funding to develop their tech, and Personal AI may require the same as it scales and finds product-market fit.

Suman Kanuganti, CEO: Previously founded and served as CEO at Aira and worked as an engineering manager at Intuit.
Sharon Zhang, CTO: Previously worked as a software engineer at Nuance and LinkedIn and as Head of AI/ML at Glint.
Kristie Kaiser, Head of UX and Design: Previously served as a product designer at Aira and several other startups.

Rewind: Backed by a16z, First Round, and Vela Partners.
Character.ai: Backed by a16z, Nat Friedman, Elad Gil, and others.
Perplexity: Backed by NEA, Databricks Ventures, Nat Friedman, and others.
Inflection: Backed by Microsoft, Reid Hoffman, Bill Gates, Nvidia, and others.

By enabling individuals to build, own, and operate a personal AI, along with impressive retention metrics and user reviews, this company's business model is promising.