The AI co-scientist,
for rigorous research.
By the lab that built Virtuous Machines: Towards Artificial General Science→
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Trusted by researchers at
From the people who shape science.
I've found this to be the best for identifying statistical and methodological issues.
A remarkable review of a complicated paper, especially noteworthy for the depth of the methodological comments. I do not often see reviews by statisticians that are as detailed.
I've tried other AI tools aimed at improving science, but this was my first experience with something like this, and it feels truly different… it actually challenges you and helps you grow as a scientist.
90%of our users rate the outputs as equal to or better than human peers.
An evaluation for science.
Most quality signals come too late to use. Calibre does not. Get a number out of 100, the weak points behind it, and the specifics to correct them before a reviewer.
What a Calibre score measures.
Design aligned to the question.
How fully your design answers the question it asks.
Statistical and analytical soundness.
How correctly you did the statistics, models, and reasoning behind your results.
Conclusions sized to the evidence.
How fully your claims agree with the data behind them, against your field's standards.
Could reach 96
Three unique elements.
A mixture of models, better than any one.
All frontier models, orchestrated: Claude, GPT, Gemini, Mistral, Grok, and our Explorer One. The design removes single-model bias.
An in-house science stack that scales discovery.
The scientific method, automated with durable memory, verification at the source, and external data. It is not an LLM that speaks to itself.
Scientific agents that think as the field does.
Principles from human cognition drive them. Tools specialise them, and the system sizes each one to its task. We built them to do science, and proved it.
The architecture of our tools does science autonomously.See research →
Start for free. Pay for what you need.
Your first project is free: a full review, then refine it with Rosa. Upgrade for more projects and tools: Researcher from $99 each month, Pro $199, Institution by conversation.
FAQ
How is this different from an analysis of my paper by ChatGPT or Claude?
A general-purpose LLM reads your work in one pass, with the knowledge it holds at its training cut-off. It does not look for the current literature. It does not verify references. It does not score against field-specific standards. It does not check its own work. It cannot overcome its own bias.
Explore Science does all of these things, in a multi-phase architecture we developed for autonomous scientific research. We orchestrate each frontier model (Claude, GPT, Gemini, Mistral, Grok) with our own Explorer One. We route each sub-task to the model that performs best on it. We verify each citation live to a DOI. We hold your manuscript in context across hours of analysis, not one two-minute pass. The depth is the difference. You see it in the nuance and insight of our feedback.
What models does Explore Science use?
A mixture. The system selects it task by task. Models differ by sub-task, by reviewer role, and by scientific field. The orchestrator routes each step to the model that fits it best.
The current mixture includes (but is not limited to) Claude, ChatGPT, Gemini, Mistral, and Grok, with our own in-house Explorer One. You do not select a model. You get the strongest answer at each step: a consensus across models that cross-checks and removes single-model bias.
Do you use my manuscript to train AI models?
No. We do not train models on user manuscripts. Your work is yours.
Is it better than human peer review?
90% of users rank Explore Science's output as equal to or better than human peer review.
Human peer review is unpaid and often rushed. At times the reviewer can be a non-specialist in your exact topic. Explore Science gives consistent rigour, subject-matter depth, and a careful read to each submission. You get it in hours, not months.
The goal is that your paper reaches a human reviewer in the strongest form you can send.