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Legal

Our proprietary Croquis tool detects creative works inside AI training datasets and models. Put it to work for your law firm or litigation team, at the scale your cases demand.

Courts are deciding, case by case, how copyright law applies to generative AI. Millions of dollars and your client’s IP are at stake while the evidence you need sits inside terabyte-scale datasets and the models themselves—where traditional discovery often can’t reach. But our custom detection technology can.

Answer: let your clients know if their IP is being used without their knowledge or consent.

Assess: find out whether a claim has a factual footing before you pursue it—or screen a portfolio to find the claims worth bringing.

Prove: once litigation is underway, build documented, reproducible evidence for the specific works at issue.

See sample Croquis findings here.

Whether you’re evaluating a potential claim, screening a portfolio of prospective plaintiffs, or deep in discovery—we’d like to hear about the case.

Features

Assess

Establish the factual basis for plausible allegations before you file—or find the cases worth bringing.

  • Screen a client’s catalog—or an entire prospective class portfolio—against the public datasets used to train AI models, and see whose works appear where.
  • Probe the models themselves for evidence that specific works were absorbed in training, from verbatim reproduction to imitation that traces back to the original with very high likelihood.
  • Separate claims with real factual support from claims built on suspicion—before filing, not after—so the complaint you draft rests on evidence you can point to.
  • Surface patterns across many rights holders and many works: the shared, checkable facts that matter when you’re evaluating a potential class or consolidated action.

Prove

Turn a well-founded suspicion into documented, reproducible evidence—work by work.

  • Per-work evidence reports: where a work appears in training datasets, how the model behaves when probed with it, and what that behavior shows—with the supporting outputs attached.
  • Methodology documented end to end. Every probe is logged and re-runnable, so results can be reproduced and independently verified rather than taken on faith.
  • Grounded in our published research on detecting training-data use—methods your technical experts can examine, cite, and build on.
  • Continuous monitoring as new models and datasets ship, so the record reflects ongoing use—not just a snapshot from the day you filed.

Where the evidence matters

Before filing

A good-faith factual basis

Factual contentions in a complaint need evidentiary support—or a reasonable basis to expect it after discovery. Dataset and model analysis turns “we believe our client’s work was used” into specific, checkable findings you can stand behind at filing.

In discovery

Targeted, informed requests

Knowing which datasets, model versions, and behaviors matter lets you draft discovery that asks for the right things—and independent technical analysis gives you a benchmark to evaluate what the other side produces.

At scale

Class and portfolio actions

When claims span thousands of works or many rights holders, the analysis has to scale with them. Per-work findings across a whole portfolio help show the common patterns that portfolio-wide and class claims are built on.

We provide technical analysis, not legal advice. Our reports and methods are built to support your case team and your experts—the legal strategy stays yours.

How it works

1

Tell us about the matter

The works, the rights holders, and the models or datasets in question—whether you’re screening a potential claim or already in active litigation. Handled confidentially.

2

We run the analysis

We search public training datasets and probe the models across the works at issue, with methodology documented and every result logged for reproduction.

3

You get the evidence

Per-work reports with supporting outputs and reproducible protocols—ready for your case team and experts to assess, cite, and act on.