Catch and fix what's broken in your time-series AI
before your users do
Rockfish generates domain-specific synthetic time-series data so
your analytics agents are tested on the right questions and
your ML models train on the right patterns.
Trusted by enterprises and research institutions
Two ways Rockfish makes your
time-series AI more reliable
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.
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Real production data doesn't have enough anomalies to train on.
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.
One platform - Custom synthetic data
for eval-ready output.
Start with your schema, a sample of your data, or a production export. Rockfish builds a synthetic version that preserves your domain's real patterns — then adds the scenarios you need.
Data & Schema Fuel
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.
Scenario Studio
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.
ML & Agent Ops Pipeline
Use the output in your pipeline
The synthetic dataset drops into your ML training, model testing, agent evaluation, or data-sharing workflow — without touching or exposing real production data.
From your data to a domain-specific
synthetic dataset — in four steps
1
Bring your data
Schema, sample dataset, or production export. No custom pipelines required.
2
Generate a realistic baseline
Rockfish builds a synthetic dataset that preserves temporal structure, multivariate correlations, and domain behavior — without touching real data.
3
Add the scenarios you care about
Inject anomalies, incidents, drifts, spikes, or edge cases with full label and metadata alignment.
4
Train, test, or evaluate
Use the output for ML model training, agent evaluation, regression testing, or privacy-safe data sharing.
The incidents that break your models
don't happen on a schedule.
With Rockfish, you don't have to wait for them.