Plasma AI
Plasma builds infrastructure for intelligence at scale: systems that make collaboration between people and AI agents reliable at scale. Its operating principles are coordinated intelligence, governed execution, persistent memory, and visibility into how results are produced.
At a Glance
- Software-development teams
- Organizations coordinating multiple AI agents
- Enterprise teams requiring governed, auditable agent execution
- Researchers and technical teams working on large repositories
- +1 more
AI Tools by Plasma AI
(2)fractal
Hierarchical Agent Loop Orchestrator
Radio by Plasma
AI Agent Messaging Channels
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Latest News
Opening Linear A: Plasma released a corpus, tools, and evidence for a proposed Semitic reading of the Minoan script.
Plasma opened erdosproblems.ai and its open-source Erdős repository, including a formal proof for Problem 809.
Plasma introduced Radio, a real-time meeting and communication surface for people and AI agents.
Fractal v1 was released as an Apache-2.0 open-source hierarchical agent-loop framework.
Products & Services
Open-source hierarchical agent-loop and orchestration framework. Autonomous agent nodes arrange themselves in a tree, work in isolated Git worktrees, spawn children for separable subtasks, and record lifecycle, budgets, messages, and costs in a local SQLite database.
A real-time shared communication channel for people and agents. It supports team, cross-functional, and external meetings across machines and agent providers, with the conversation retained as a record of the work.
Open-source indexed knowledge-base CLI that gives agents a compact tree map before they read only the pages relevant to a task. It stores plain Markdown files, keeps generated indexes and links synchronized, supports regex search, and provides a merge driver for multi-agent/worktree collaboration.
An open record of results on Erdős problems, generated from the open-source erdos repository, with problem pages, evidence-linked claims, sources, proof records, and Lean-verification tiers.
Market Position
Plasma positions itself as infrastructure for agent organizations rather than a single model or single-purpose chatbot. Its differentiation is the combination of hierarchical recursive delegation, model-per-task routing, budgets and permissions, persistent memory, real-time supervision, and an auditable record of execution; Fractal is the orchestration layer, Radio the agent-to-agent communication primitive, and Wiki the indexed memory primitive.
Leadership
Founders
Nicholas Diao
Co-founder and CTO of Coursedog and co-founder of Omega Strategies; studied physics at Columbia University and the University of Oxford.
Andrew Turner
Co-founder of Omega Strategies, where he built the firm's automated trading platform; studied pure mathematics at MIT.
Executive Team
Nicholas Diao
Co-founder & CEO
Co-founder and CTO of Coursedog and co-founder of Omega Strategies; studied physics at Columbia University and the University of Oxford.
Andrew Turner
Co-founder & CTO
Co-founder of Omega Strategies and builder of its automated trading platform; studied pure mathematics at MIT.
Founding Story
Before the agent era, the founders built Omega Strategies, described by Plasma as a fully autonomous commodities hedge fund. Making autonomous trading reliable required infrastructure for data integrity, risk boundaries, budgets, continuous performance evaluation, and recording every decision. Plasma was born from asking what Omega would look like if rebuilt with agents, and applying those operating principles to open-ended agent systems doing meaningful work.
Business Model
Revenue Model
Plasma presents an enterprise product and invites users to request a demo. Fractal Cloud is described as forthcoming with early access, while the Fractal, Radio, and Wiki primitives are open-source or available as open tools.
Target Markets
- Software-development teams
- Organizations coordinating multiple AI agents
- Enterprise teams requiring governed, auditable agent execution
- Researchers and technical teams working on large repositories
- Mathematics and formal-verification communities
- Delegating substantial software-engineering work to autonomous coding agents
- Parallel coding, testing, review, and debugging across multiple agents
- Cross-machine or cross-provider agent collaboration
- Long-running research projects with persistent project memory
- Research reproducibility, evidence management, and formal mathematics
- Enterprise workflows requiring budgets, permissions, audit trails, and human oversight