Your AI has some explaining to do.
“Done” is a claim. Demand proof.
The Gray Systems checks AI agents’ reported actions against observed outcomes and supplies defensible AI training data with documented provenance.
What did it actually do?
AI behavior verification checks an agent’s reported success against the observed outcome in the destination system.
Inspect the verification approachSee an illustrative findingWhere did it come from?
Defensible AI training data pairs a defined specification with documented origins, production records, and agreed licensing terms.
Explore data & provenanceScope a training datasetWhy we exist.
We started in data arbitrage — sourcing and supplying datasets in a market where provenance was an afterthought and clean rights were the exception, not the rule. We saw what buyers were really inheriting: data with no traceable origin, no defensible license, and a legal question mark that wouldn't surface until diligence, or a lawsuit.
So we started building datasets to specification, with origin declarations, production records, and agreed licensing terms that buyers can inspect. That same discipline now supports our AI behavior verification work: record what happened and state what the evidence supports.
That's the idea behind the name. We operate in the gray so you don't have to.
The data wall has a legal edge.
Training teams need useful data and a clear account of how it was obtained. Public access alone does not establish training rights. The relevant licenses, collection conditions, privacy requirements, and intended use need to be reviewed and documented.
The hardest question in AI right now isn't where do we get more data. It's can you prove where this came from. Most data can't. Ours is built to.
The method changes. The standard doesn't.
What your model needs determines how the data is made. It may be generated to a written specification or performed, recorded, or collected under documented rights. For generated assets, the record names the model, relevant inputs, parameters, and applicable terms. For recorded or collected assets, it documents the source and relevant consent or license. The method changes; the evidence standard stays.
Making the data is the easy part. What makes it usable — and defensible — is everything that travels with it.
Built to your specification.
You describe how your model actually trains. The dataset gets shaped to that — subject, distribution, format, the edge cases that matter to you. Not a stock library you bend to fit.
Documented chain of custody.
Every asset ships with a structured record of its declared origin, relevant production details and licensing, and a content fingerprint. Our open-source verifier, tgs-verify, checks the manifest structure and whether delivered files match their recorded fingerprints. Origin and rights claims still depend on their supporting records.
Licensed on your terms.
Evaluation-only, training-only, non-exclusive, exclusive, full IP buyout, or custom terms. We agree on the rights granted for your intended use. Exclusivity and ownership depend on the rights available and the written delivery agreement.
The record is the product. Whether the data was generated, performed, or collected is just how it got made.
A dataset is only as good as what you can prove about it.
Useful data needs more than usable files. Accurate labels, distribution information, production details, and rights records help your team inspect and evaluate a dataset. Which metadata matters depends on the training task; documentation alone does not establish model performance.
We came from the side of the market that doesn't bother, which is how we know how rare that combination is. If you've bought data before, you already know the difference between a delivery that arrives and a delivery you can stand behind.
A short, honest process.
Scope.
You describe the requirement. We confirm exactly what we can deliver, at what fidelity, and on what timeline — before any commitment.
Sample.
We generate a representative batch so your team can validate quality and provenance against your real pipeline. You see it before you scale it.
Deliver.
We produce to volume, on a cadence that fits your training cycle, with full documentation on every asset.
We tell you what we'll do, and we do it. Or we tell you it isn't the right fit. That's the whole relationship.
Someone is going to ask where your training data came from.
It will probably be a lawyer, and it will probably arrive at the worst available moment — mid-diligence, or three weeks before a disclosure you have already committed to publishing.
They will want supporting records: sources, rights, production details, and the files actually delivered. Records captured at the time are stronger than later recollections. If earlier evidence is missing, identify the gap and distinguish recovered records from reconstructed accounts. Finding those gaps early makes the review more useful.
Tell us what you're working on.
Need to check an AI workflow, source defensible training data, or discuss a special project? Choose the service and outline your requirement. We'll tell you plainly whether we're the right partner for it.
Before you write — how this works.
You'll get a straight answer. We reply to every serious inquiry within one business day — and if we're not the right fit for what you need, that's the answer you'll get, plainly.
You'll be talking to the people doing the work. No sales sequence, no newsletter, no handoff. The reply comes from whoever will actually scope your requirement.
Bring the requirement. For AI verification: the workflow, systems, and expected outcome. For training data: the data type, rough volume, and intended use. Include your timeline. Do not submit passwords, access tokens, or confidential records.