Orchestra Research
Orchestra Research is building an AI-native research platform and AI co-scientist that helps researchers move from idea to publication by handling literature work, experiment design, code execution, GPU compute, analysis, and writing. Its stated mission is to eliminate infrastructure and information friction and democratize rigorous scientific discovery.
At a Glance
- Academic and industrial AI/ML researchers
- Data scientists and research engineers
- Students and citizen scientists
- Startups and small research teams without dedicated infrastructure staff
- +2 more
AI Tools by Orchestra Research
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AI Research Skills Library
Research Skills for Coding Agents
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Latest News
The Human Last Read Paper: Agent-Native Research Artifact
The Golden Age of Research Starts Now
Introducing the New Orchestra — The First AI-Native Research IDE
Intro to AI Research
Products & Services
A browser-based research workspace and AI co-scientist for literature review, brainstorming, experiment planning, code generation, GPU execution, monitoring, analysis, visualization, and publication drafting. It is designed for persistent, open-ended research trajectories rather than one-shot autonomous answers.
An MIT-licensed open-source library of modular, invocable knowledge packages that give coding agents expert workflows, examples, references, and troubleshooting guidance for AI research engineering. The official skills page currently presents 98 skills across 23 categories; the repository supports installation for agents such as Claude Code, Codex, and Gemini.
A research-artifact protocol/workflow for turning papers and other research inputs into structured, machine-executable packages so agents can understand, reproduce, and extend the work.
Market Position
Orchestra positions itself as a human-in-the-loop, agent-native research IDE and persistent research companion rather than a black-box autonomous AI scientist. Its differentiation is the integration of literature, ideation, experiment execution, compute, analysis, and writing in one workflow, plus an open skills layer for coding agents. The company's own comparisons frame it against autonomous AI-scientist systems such as Curie, Kosmos, and The AI Scientist, emphasizing researcher control, transparency, session persistence, and verifiability.
Leadership
Founders
Zechen Zhang
Founder and Ph.D. in Physics. His background spans philosophy and physics, theoretical physics, evolutionary dynamics, statistical mechanics of neural networks, deep-learning theory, AI safety, and research on how language models integrate new knowledge through fine-tuning. He worked with Haim Sompolinsky at Harvard and has published on neural networks, interpretability, fine-tuning, and AI agents for science.
Jiachen (Amber) Liu
Credited by Orchestra as a contributor to its early work. Liu's public profile describes her as working on Orchestra, AI for science, and agent-native research artifacts; she is also associated with the Sci-Reasoning research and the Agent-Native Research Artifact work.
Executive Team
Zechen Zhang
Founder
Physics Ph.D., Harvard researcher associated with Haim Sompolinsky; scientist and builder working across theoretical physics, neural-network theory, LLM fine-tuning, AI safety, and AI scientific assistants.
Founding Story
The founders say they started Orchestra after seeing scientists and engineers spend most of their time fighting infrastructure, debugging code, searching literature, and babysitting jobs rather than doing science. The initial vision was an AI research companion that automates repetitive and complex work while keeping humans in control of questions, judgment, and scientific direction, making world-class research capability available beyond elite institutions.
Business Model
Revenue Model
Subscription plans priced in monthly research credits, with included project/agent and GPU-access allowances; the platform also sells non-expiring one-time credit packs.
Pricing Tiers
2,000 credits/month; up to 5 research projects; up to 5 AI agents; basic T4/A10G GPU access; core experiment tracking; community support.
6,000 credits/month; unlimited projects and agents; advanced A100/H100 GPU access; full experiment tracking and analytics; priority email support.
24,000 credits/month; Pro features plus priority access during high traffic, dedicated support, and early beta access.
120,000 credits/month; Max features plus priority H100/B200 queue, dedicated Slack/Discord support, and early experimental features.
1,500 credits; credits do not expire.
4,200 credits; credits do not expire.
9,500 credits; credits do not expire.
Target Markets
- Academic and industrial AI/ML researchers
- Data scientists and research engineers
- Students and citizen scientists
- Startups and small research teams without dedicated infrastructure staff
- Cross-disciplinary researchers and researchers in institutions with limited compute access
- Teams working in machine learning, NLP, computer vision, bioinformatics, genomics, economics, and social science
- Literature reviews and research-landscape mapping
- Brainstorming and developing novel research directions
- Designing and running machine-learning experiments
- Fine-tuning, reinforcement learning, evaluation, interpretability, and model serving research
- Reproducing published results and testing ideas outside a researcher's primary specialty
- Long-horizon, human-in-the-loop AI research workflows