Reliable AI starts with the data behind your evals.
Rockfish helps you generate the high-coverage data your evals need to surface how AI agents and models will fail — before your customers do.
Two ways Rockfish makes your AI more reliable.
The same platform powers both — generated from your schema or a sample of your data, with the scenarios your evals actually need.
Your agent passes benchmarks. Does it answer your users' actual questions?
Rockfish generates domain-specific eval suites — realistic time-series datasets and aligned Q&A pairs — so you find exactly where your agent breaks before you ship.
Agent evaluation →Real production data doesn't have enough anomalies to stress-test your model.
Rockfish generates labeled time-series data with the failure patterns your model needs — anomaly spikes, cascades, drifts — on demand, without waiting for incidents to occur.
ML testing →High-coverage data for eval-ready output.
Start with your schema, a sample of your data, or a production export. Rockfish builds a realistic dataset version that preserves your domain's real patterns — then adds the scenarios you need.
Start from your schema or data
Bring a schema, a sample dataset, or an export from production. No custom pipelines needed. Rockfish preserves temporal structure and multivariate correlations from the start.
Add the scenarios you need
Inject anomalies, rare incidents, cascading failures, traffic bursts, and domain-specific edge cases — with accurate labels and full metadata alignment. In natural language.
Use the output in your pipeline
The generated dataset drops into your Model testing, agent evaluation, or data-sharing workflow — without touching or exposing real production data.
From your data to a domain-specific eval-ready dataset — in four steps.
Bring your data or schema
Schema, sample dataset, or production export. No custom pipelines required.
Generate a realistic baseline
Rockfish builds a realistic dataset that preserves temporal structure, multivariate correlations, and domain behavior — without touching real data.
Add the scenarios you care about
Inject anomalies, incidents, drifts, spikes, or edge cases with full label and metadata alignment.
Evaluate
Use the output for ML model testing, agent evaluation, regression testing, or privacy-safe data sharing.
The incidents that break your agents or models don't happen on a schedule.
With Rockfish, you don't have to wait for them.