Data
Available
Structured Extraction
Turn unstructured text into clean, schema-valid JSON.
CSV / JSONL→ JSONL
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Test before you scale
Test your AI workload
before you run it at scale.
Race a representative sample across models and providers. Compare real outputs, cost, and reliability, then run the full dataset with the configuration you choose.
Measure on a sample. Decide with evidence. Execute the full workload.
Sample comparison
CSV
VALIDproduct_descriptions.csv
128,402 rows · 24.8 MB
Recipe
Structured Extraction
Configurations
3 model + provider pairs
Projected full cost
$18.42
Illustrative sample result. Choose only after reviewing outputs and execution evidence.
Templates
What can you run?
Text
Available
Text Classification
Assign consistent labels across thousands or millions of rows.
CSV / JSONL→ CSV
↗
Generation
Coming soon
Synthetic Data
Generate structured records from seeds, examples and constraints.
CSV / JSONL→ JSONL
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Commerce
Coming soon
Product Catalog Matching
Match equivalent commercial products across two catalogs.
Two CSV files→ Product map
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How it works
Upload
Add a CSV or JSONL dataset.
Define the task
Set the prompt, output structure, and evaluation method.
Compare a sample
Run the same inputs across configurations and inspect the evidence.
Run the full dataset
Choose a configuration and scale it to the full workload.
Built for batch
Every response doesn’t need to arrive instantly.
If a workload can be split into independent tasks, it can probably be a RunBatch.
10,000 records
100,000 prompts
5M descriptions
2M images
Have a dataset to process?