Coordinal Research builds automation tools to accelerate AI safety and alignment research. The organization develops AI-powered scaffolds and workflows that help researchers conduct alignment experiments faster and at greater scale.
Coordinal Research builds automation tools to accelerate AI safety and alignment research. The organization develops AI-powered scaffolds and workflows that help researchers conduct alignment experiments faster and at greater scale.
People– no linked people
Updated 04/02/26Funding Details
Updated 04/02/26- Annual Budget
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- Current Runway
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- Funding Goal
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- Funding Raised to Date
- $110,000
Org Details
Updated 04/02/26Coordinal Research is an organization building research automation infrastructure for AI safety and alignment. It was formed through the merger of Lyra Research (led by Jacques Thibodeau) and Vectis AI (led by Ronak Mehta), with the two teams coming together during the Catalyze Impact AI safety incubator program in London. The organization is co-founded by Ronak Mehta, an ML/CS PhD graduate from the University of Wisconsin-Madison and MATS 6.0 alumnus whose research focuses on provably safe AI and formally verified code generation, and Jacques Thibodeau, a data scientist, MATS scholar (2022), and independent alignment researcher who previously created the Alignment Research Literature Dataset used by major AI labs. Coordinal's core technical contribution is an automated research scaffold that accepts any research plan or task and autonomously conducts background research, implements software, evaluates experimental results, and writes research reports. They have also developed Seed, a structured AI-aided research workflow that helps researchers refine ideas and create detailed project specifications. The team has curated over 400 open questions in AI safety for direct use with their system. The organization describes its motivating insight as: the bottleneck in research is not intelligence but taste and verification at scale. Their systems are designed as taste accelerators that distinguish breakthrough discoveries from noise better than traditional peer review, producing research that directly converts into usable products and infrastructure. Coordinal Research, Inc. is incorporated as a for-profit company, with governance structures (such as a Public Benefit Corporation structure) intended to keep it aligned with its mission. They secured approximately $110,000 in seed funding through their Seed Funding Circle and raised additional funds through Manifund, where they signed an MFN SAFE for investor contributions.
Theory of Change
Updated 04/02/26Coordinal believes the primary bottleneck to progress in AI safety is not a shortage of intelligence or ideas, but the inability to distinguish high-quality research from noise at scale (what they call a lack of taste and verification). By building automated research scaffolds and taste accelerator tools, they aim to dramatically increase the throughput and quality of alignment research output. If AI safety research can be accelerated and automated, the gap between AI capabilities research and safety research can be closed, increasing the probability that advanced AI systems are safe and beneficial. Their causal chain runs: better automation tools -> more alignment experiments conducted per researcher-hour -> faster progress on key open problems -> safer AI systems at deployment time.
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Updated 04/02/26Projects– no linked projects
Updated 04/02/26Discussion
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