Today, we’re excited to announce our pre-seed investment in Plastic Labs.

The rise of LLM apps is causing unprecedented demand for personalization in software. These apps rely on natural language, which changes depending on who we’re speaking to. Think about how you would explain a math concept to your grandparent compared to your parent or your child. You instinctively adjust your explanation based on the listener. LLM apps must similarly understand who they’re talking to in order to deliver more effective, tailored experiences. Whether it’s a therapist agent, a legal assistant, or a shopping companion, these apps must develop an understanding of their users to be truly useful.

Despite how essential personalization is, there’s currently no out-of-the-box solution for it for LLM apps. Developers are forced to build ad hoc systems that store user data — typically as session logs — and then retrieve that data as needed. This leads to every LLM app redundantly solving the same core problem of building infrastructure to manage user state. Worse yet, many of these solutions, such as storing user interactions in a vector database and running RAG over them, aren’t actually capturing the user — they’re capturing the user’s interactions. While helpful for recalling past conversations, these approaches offer little meaningful insight into who the user actually is: their interests, communication preferences, tone sensitivities, and more.

Plastic Labs solves this. The team has built Honcho, a plug-and-play platform that lets developers personalize any LLM app effortlessly. Instead of reinventing user modeling from scratch, developers can integrate Honcho to instantly gain access to rich, persistent user representations. These representations are far more nuanced than anything possible with traditional methods, thanks to techniques borrowed from cognitive science. They are also fully queryable via natural language, allowing LLMs to flexibly adapt behavior based on this underlying user representation.

By abstracting away the complexity of user state management, Honcho enables a new level of hyper-personalized experiences for LLM apps. But the implications go even further. Honcho’s ability to generate rich, abstract representations of users also unlocks the long-elusive promise of a shared user data layer.

Shared user data layers have historically failed for two main reasons, both of which Honcho addresses:

1. Lack of Interoperability: Traditional user data is context-specific and not portable across apps. For instance, X might model you based on who you follow, but that data isn’t useful to who you are professionally connected to on LinkedIn. Honcho, by contrast, captures a higher-order and universally relevant representation of the user that works seamlessly with any app that uses an LLM. For example, if a tutoring app discovers that you learn best with analogies, your therapist agent could use that same insight to communicate more effectively, even though these apps have completely different contexts.
   

2. Lack of Immediate Utility: Previous attempts at shared data layers struggled to bootstrap because they didn’t provide value to early adopter apps, which are where useful user data is generated. Honcho solves this by being directly useful to individual apps, even in “single-player mode.” Apps adopt Honcho to manage user state — a first-order problem — and will eventually be able to contribute to a shared layer. Once enough apps contribute to the layer, the network effect kicks in: newer apps will integrate not just for personalization, but also to tap into the collective intelligence of shared user representations, solving cold-start problems and enhancing UX from day one.
   

Honcho already has hundreds of apps on its closed beta waitlist — from sobriety coaches and educational companions to reading assistants and e-commerce tools. The team’s strategy is to focus first on solving the core challenge of user state management for apps (the first-order problem), then roll out the shared data layer for interoperability (the second-order problem) for those that opt-in. This layer will use crypto to align incentives: early apps that contribute to the system will receive ownership in the layer and, therefore, upside in it. Crypto also makes the system trustless, addressing fears that a central authority could extract value or build competing products.

We believe Plastic Labs is the team to tackle the challenge of user representation in LLM-powered software. Vince (CEO), Courtland (COO), and Vineeth (CTO) discovered the problem by experiencing it directly while building personalized experiences into their chatbot tutoring app, Bloom. Their students weren’t learning effectively because the app did not fundamentally understand who the students were and how they learned. Honcho was born from that insight, and it’s solving a pain point that every LLM app developer will face.

If you’re building with LLMs and want to deliver deeply personalized experiences, you can sign up for early access to the hosted Honcho platform here. The team is also hiring — especially full-stack and ML engineers.

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