Subconscious said it has raised $5.1 million in funds to launch an inference platform designed for long-running agents.
It’s build on this notion: MIT researchers discovered a way to dynamically compress 95% of an AI agent’s context and make it much more efficient and accurate.
Now, they’re turning that core technology into an opinionated inference platform to power agents that run faster, for longer, at a lower cost with no changes to the underlying hardware, AI models, or apps that use them.
MassVentures led the round, with participation from Foothill Ventures, Underscore VC, E14 Fund, and the Agent Fund among others. The inference platform is available today for developers, with an on-prem deployment package available for enterprises.
Language models are used in many ways: as a chatbot, as a classifier, as a document writer, or as an AI agent. Among all these uses, agents are extremely computationally intensive and require processing thousands of times as many tokens over long periods of time. Agents are the most expensive way to use AI models, but they generate the most valuable work. Already they’ve transformed software engineering, and they’re growing in usage among salespeople, lawyers, scientists, marketers, consultants, and all kinds of knowledge work. Subconscious believes agents will make up virtually all inference in the very near future.
Subconscious built an inference platform to take advantage of the unique challenges in powering long-running agents. Born out of MIT research into inference, the system uses dynamic context compression and highly efficient caching for a stepwise gain in performance.
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