Servamind lets AI teams run any model at 10x the scale. The company builds the layer underneath AI models — a data layer and a compute layer that make training, fine-tuning, and inference cheaper on the hardware teams already run.

What Servamind will use the funds for

The money goes to three things.

The data layer. The Serva Encoder is live in beta. It converts any set of files into a .serva file, a single multimodal format that replaces the per-model preprocessing pipeline. The funding takes it from beta to general availability, with the first terabyte free.

The compute layer. Chimera trains and serves models on .serva directly, without unpacking the data first. Alpha opens soon.

The proof. Servamind is preparing its first benchmark paper with reproduction kits. Anyone will be able to run the results without an account.

Who backed Servamind

Davidovs Venture Collective (DVC) led the round. DVC is an AI-focused venture firm founded by Marina Davidova and Nick Davidov, whose portfolio includes Perplexity, Thinking Machines, Etched, and Mem0. Arrington Capital, Dark Horse Venture Partners, Psalion, Frontier Capital, and Edge Ventures also participated, along with two additional investors.

What the founders and backers say

"Every AI team is bounded by the same thing: how much compute it can afford. We think that constraint is a software problem, not a hardware one. This round lets us prove it in public, with a paper and a reproduction kit, rather than asking anyone to take our word for it." — Rachel St. Clair, Co-Founder and CEO, Servamind

"Compute is the biggest bottleneck in the market right now and solving model efficiency without sacrificing results offers one of the biggest prizes in the AI economy. We believe that Servamind has a strong research foundation to achieve that and we can't wait to cut our own AI bills by 30%, or rather, get even more done for the same amount." — Marina Davidova, Managing Partner and Founder, Davidovs Venture Collective

“Psalion decided to invest in Servamind for three reasons: (i) the technology is closer to revolutionary than evolutionary (an order of magnitude increase in efficiency is a tremendous achievement); (ii) the background, experience, attitude and drive of the founders; and (iii) the early stage of the investment (and corresponding valuation) relative to the potential upside. Such a favorable risk-reward balance is indeed rare.” – Tim Enneking, Managing Partner, Psalion Venture Capital

"At Edge Ventures, we backed Servamind because we believe one of the biggest constraints on AI is no longer just intelligence, but the infrastructure required to scale it. Servamind is fundamentally rethinking that layer, combining deep research with a clear economic advantage that could allow far more intelligence to run on the same hardware. What excited us is that this isn't simply an efficiency play, the team has a genuine technical edge and the ambition to build something transformational for how AI is developed and deployed." — Mona Tiesler, Partner, Edge Ventures

"I have known Rachel for close to four years and watched her research move from theory to something you can run in production. She is the rare founder who combines a genuine scientific vision with the discipline to ship products that pay for themselves today. Servamind is solving one of the most fundamental problems in AI infrastructure, how information is represented before a model ever sees it, and we believe that layer will matter more than any single model built on top of it." – Marcus Joernsen, Partner, Frontier Capital

What is Servamind?

Servamind is a research company with one mission: more mind per machine. Servamind built a new space for information to live and be computed, so training, fine-tuning, and inference get dramatically cheaper for any model using the stack you already run. Servamind is working toward a world where intelligence is unbounded by compute. The company was founded by Rachel St. Clair and Peter Sutor, Jr.

Read more about what Servamind is and what it builds, or meet our team.

Start with the Serva Encoder beta at serva.servamind.com, or join the Servamind community on Discord.