Claygent, @clay’s AI agent for GTM research, has processed over 5 billion queries.Claygent doesn’t need to use frontier models for every query. Smaller alternatives can be more cost effective and lower latency. We trained and served a specialized router to pick the right model for each task, cutting inference costs by 47% with no impact on answer quality.

Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.

There is a large delta between what models can do and what they deliver in company-specific workflows. We bridge that gap through forward deployment. In a given week, our engineers might build eval frameworks from scratch, deploy a large-scale context ingestion engine, and present results to F500 leadership. We fine-tune models on proprietary data no frontier lab has seen and optimize agent performance against real-world outcomes. We're excited by engineers with rigor, high customer empathy, and a bias toward action in ambiguity. appliedcompute.com/blog/unlo…

Generalists are useful, but it’s not enough to be smart. Advances come from specialists, whether human or machine. To have an edge, agents need specific expertise, within specific companies, built on models trained on specific data. We call this Specific Intelligence. It's what we're building at Applied Compute. We unlock the latent knowledge inside a company, use it to train custom models, and deploy an in-house agent workforce that reports to your team. We work with sophisticated companies that have already captured early gains from general models, like @cognition, @DoorDash, and @mercor. They’re pulling even further ahead with proprietary in-house agents that don’t need to wait for the next public model release. Together, we are building and validating models and agents in days instead of months, achieving state-of-the-art performance on customer evals. Our team has high density and low latency. Our founders all worked on different parts of this problem while they were researchers at OpenAI — @ypatil125 as a key member on the agentic software engineer effort (Codex), @rhythmrg as a core contributor to the first RL-trained reasoning model (o1), and @lindensli as a core contributor on ML systems and infrastructure for RL training. Two-thirds of the team are former founders, and everyone brings a deep technical background, from top AI researchers to Math Olympiad winners. We are backed by $80M in funding from Benchmark, Sequoia, Lux, Elad Gil, Victor Lazarte, Omri Casspi, and others. With their support, we are growing the team, scaling deployments, and bringing to market the first generation of agent workforces built on specific models. In short: 1. We are building Specific Intelligence for specific work at specific companies. 2. That will power in-house agent workforces to support their human bosses. 3. That in turn will unlock AI’s full potential through humanity’s greatest engine of progress: thriving corporations in a free market.