Built from Virtuous Machines:
Towards Artificial General Science.
One engine underneath every feature. It assembles the relevant context, orchestrates a mixture of frontier models, runs them inside agents that think like scientists, reasons across hours of compute, and checks its own conclusions. Jump to autonomous pipeline →
Three featured elements, of many.
Science that stands up to scrutiny is hard, and needs more than a good model. The platform utilises the same architectures behind systems that have run original research autonomously for hundreds of hours.
A mixture of models,
better than any alone.
Many frontier models are orchestrated as one system alongside our Explorer One model. Every sub-task routes to the model that handles it best; every conclusion is cross-checked across model families. One lab's blind spots are caught by another's strengths, so single-model bias is gone by construction.
You never have to choose a model. You get the strongest answer at every step.
Why this matters
Every frontier lab has its own inductive bias. Use one and you inherit it. Orchestrate all of them, and you've built something only an independent lab would.
An in-house science stack scaling discovery.
The full scientific method, running autonomously. Verification is front-loaded where errors are costliest and backed by tools that can prove a claim wrong. Code that runs, sources that check out, novelty tested against the field.
The system finds literature and data outside of its training set. It's not an LLM talking to itself in a closed loop.”
Scientific agents that
think like the field.
Dozens of agents orchestrate behind the scenes, built on how scientists actually reason: abstracting patterns, breaking problems down, checking their own work, collaborating on problems, knowing when they're done. A single task - a review, a novelty check, a full study - draws on the whole ensemble in parallel, and adapts at run-time to the task demands.
Our agents were built to do science - the published foundation is Virtuous Machines: Towards Artificial General Science.
Live decomposition
Five phases, no human in the loop.
A single agentic system runs all five cells. The pipeline forks at Phase 03: either the system builds an instrument and collects fresh data, or it interrogates a dataset you already hold.
Input
A research domain
Phase 01
Question
Phase 02
Design
Phase 03
Collect or interrogate
Phase 04
Analyse
Phase 05
Write
Output
A submittable paper
Calibre. Zero to one hundred.
Every manuscript review ends with a Calibre score, carried as a tier chip, but the number is only the entry point. Beneath it, the review lays out the reasoning criterion-by-criterion: what holds up, what doesn't, and why.
The issues are ranked by criticality, so the biggest problems surface first. Each comes with a specific, actionable fix, turning the score into a roadmap for the next revision. Address the top issues, and the quality lifts measurably.