Applied Compute

Applied Compute builds Specific Intelligence for enterprises: custom models trained on the latent knowledge inside a company and deployed as in-house agent workforces. Rather than supplying general-purpose systems, the firm tailors models to each client's own data and competitive advantages. Its work spans reinforcement learning, post-training, ML systems infrastructure, and agentic AI, with results reported to reach state-of-the-art performance on customer evaluations.

Training stacks, agent platforms, and supporting tools are all built in-house, and engineers embed directly within client teams rather than delegating or outsourcing work. This arrangement is credited with compressing model validation and deployment from months into days, and with allowing continuous iteration on live systems. Applied Compute reports powering agent deployments at Fortune 50 companies, with industry verticals including food delivery and on-demand logistics, talent marketplaces, and enterprise software.

The company has raised $80 million in funding. Two-thirds of the team are former founders, and the engineering ranks include top AI researchers, International Math Olympiad winners, and alumni of OpenAI. The stated mission is to build the first generation of agent workforces powered by specific models tailored to each client.