Generate datasets tailored to your model

Helixing helps ML teams simulate rare scenarios, target model failure cases and iterate on datasets based on evaluation results.

Real-world data rarely covers what models actually struggle with

ML teams often train on incomplete datasets. Rare scenarios are underrepresented, failure cases remain unresolved, and collecting additional data is slow, expensive or impossible.

Missing rare scenarios

Coverage gaps in training data

Slow iteration after model evaluation

A faster way to improve dataset coverage

Target failure cases

Generate datasets that focus on the scenarios where your model currently breaks.

Simulate rare scenarios

Create edge cases and long-tail examples that are difficult to capture in real-world data.

Iterate from model results

Use evaluation outcomes to improve the next dataset version.

Request Early Access

We're currently giving early access to a small number of teams working on real ML training and evaluation workflows.

Built for early conversations with ML teams

Helixing is currently in early access. We're speaking with a small number of teams to understand where dataset generation and iteration can create the most value.