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Research

Generative Science

Accelerating hypothesis generation, not replacing judgement.

The bottleneck in research is rarely computation. It is the number of good hypotheses a team can generate and rule out per week. Generative models are unusually well suited to the first half of that, and unusually badly suited to the second, which is what shapes how we apply them.

Generative Science

What makes this hard

  • Generative models produce plausible answers whether or not they are true, so the verification step has to be built, not assumed.
  • The interesting hypotheses are the ones outside the training distribution.
  • Results have to be reproducible by someone who does not trust the model.

How we approach it

  • Candidate generation

    Propose structures, materials or mechanisms worth testing, ranked by how cheaply they can be falsified.

  • Grounded validation

    Check every proposal against the deterministic tools that already exist, so the model never gets the last word.

  • Reproducible trails

    Record what was proposed, what was checked and what survived. The record is the deliverable.

What you should expect

  • More hypotheses reaching the point of being testable, per unit of researcher attention.
  • A trail that a sceptical reviewer can follow end to end.

Where this shows up

Guido

The agentic orchestrator the other products run on.

Guido is the workspace where the agents, tools and data behind every Trazup product live. Discovery, analysis, planning and reporting sit in one place, with the same permissions, datasets and audit trail underneath.