Why We’re Investing in EON
Venture Guides is excited to announce a seed investment in EON (geteon.ai), an AI inference layer that reuses prior work to make agents faster, more predictable, and cheaper without sacrificing accuracy.
One of the benefits of AI is its ability to automate workflows, but not every piece of an enterprise workflow requires AI. Some segments of the workflow (e.g., returning a result to a database or CRM) are repeatable tasks agents execute multiple times across an enterprise. Today, enterprises must choose between recomputing the entire workflow, or trying to leverage cached outputs, without being able to verify if the cached outputs accurately match what the model would have produced on its own.
Instead of caching the output, EON caches the behavior of the model. Created by two co-founders with deep expertise in systems layers and trust and verification technologies, EON is able to cache and replay how the model acts on the data, instead of the output itself. That cuts latency by roughly 97% and cost by over 90% on recognized work, with savings that compound as an agentic fleet’s workload patterns become more familiar.
Every shortcut EON takes is verified against live model behavior before it's served, and anything that drifts automatically falls back to full inference. This means that teams aren't trading reliability for speed and savings. Paired with strict per-tenant data isolation and a research foundation grounded in peer-reviewed, patent-pending work on collaborative and distributed inference, EON is built to clear the trust bar enterprises will set before they hand over their agent traffic.
The team reflects that combination directly. Co-founder and CEO Ki-Youn Jung brings a background architecting semiconductor systems and leading AI platform strategy inside a $20B business, while co-founder and CTO Abhishek Singh's research runs deep in decentralized and collaborative AI. It's a rare pairing of hardware-systems rigor and frontier AI research, aimed at a problem that only gets more urgent as agentic AI scales from pilots to production.
Every platform shift moves through waves of adoption and reckoning. The public cloud ran through four: early adopters, wholesale lift-and-shift, cloud costs cannibalizing enterprise budgets, and finally right-sizing workloads at scale. Generative AI is in the third of those waves. We are past the early adopters and through enterprise-wide adoption of agentic workflows; token costs are now eating entire budgets. We believe that EON is the layer that helps enterprises right size their generative AI platform usage so that the right workload is matched with the right execution path, probabilistically computed or deterministically replayed. We're excited to back Ki-Youn, Abhishek, and the team as they build that layer.