Get started with Groundtruth

Tell us what your AI should get right.

Start with source-grounded evaluation data. Tell us what your AI does, where mistakes matter, and which sources you trust. We will scope supported verification, failure-focused data and a separate validation plan before promising coverage.

Diagnostic and proof evals

Start from a suspected model weakness. We build a small verified diagnostic set, then a larger fresh proof evaluation when authoritative sources support it. Each row carries provenance and validation status.

Remediation and training data

For confirmed weaknesses, we generate separate training-oriented data with reproducible seeds, provenance, validation checks, deduplication, and train/dev/test separation. We do not train on the final held-out evaluation.

Improvement measurement

We compare baseline and post-training runs on held-out examples, report uncertainty where useful, and check regressions across other capabilities instead of treating one improved score as an unconditional win.

Use the form below for any of these. Include the target model, capability, sources you trust, and whether you need evaluation data, remediation data, or both.