Mark Saroufim
Founder & CEO
Core Automation
Core Automation provides an AI platform that automates the entire research workflow, enabling small teams to run, evaluate, and iterate experiments without manual effort. Its autonomous agents use proprietary learning algorithms and efficient neural architectures to deliver results that rival large‑scale pretraining and reinforcement learning setups, freeing researchers to focus on creative tasks.
- Artificial Intelligence
- AI Agents
- Developer Tools
Updated 15 days ago
Funding
Funding not disclosed
Funding rounds are not available yet.
Founders
Ehsan Amid
Co‑Founder & CTO
Product
Problem
Conducting advanced AI research typically demands large teams, extensive resources, and lengthy development cycles, limiting the ability of small groups to pursue ambitious projects.
Solution
Core Automation builds AI-driven systems that automate the research workflow itself, allowing compact teams to achieve outcomes that previously required whole organizations. By developing novel learning algorithms that outperform traditional large-scale pretraining and reinforcement learning, and creating architectures that scale more efficiently than transformers, the platform reduces the need for manual experimentation. The lab continuously automates its own processes, using the insights gained to refine its technology and expand automation to new research tasks. This self‑reinforcing loop enables rapid iteration, freeing researchers to focus on higher‑level creative work while the system handles routine experimentation, data collection, and model evaluation.
Target Audience
Primary customers are small AI research teams, startups, and academic labs that need to accelerate development without the overhead of large organizations.
Features
- Autonomous research agents that design, run, and evaluate experiments without human intervention
- Proprietary learning algorithms that surpass conventional pretraining and reinforcement learning performance
- Scalable neural network architectures optimized for efficiency beyond transformer models
- Self‑optimizing pipeline that automates the lab’s own development cycle, feeding improvements back into the system
- Integrated data management and result analysis tools that streamline experiment tracking and reproducibility