The data bottleneck
is the AI bottleneck.
DataFramer removes it.
Trusted infrastructure. On our cloud or yours.
From seed data to evaluation set —
see how fast it actually moves.
Your eval suite is thinner than you think.
A handful of real samples doesn't cover distributions, edge cases, or the scenarios your model will actually face in production.
Real data is off the table.
Privacy reviews, compliance constraints, and customer data agreements mean the data you need most is the data you can't use.
Labeling is slow and expensive.
Manual annotation doesn't scale. Neither does waiting two sprints for a dataset your team needs this week.
DataFramer gives your team the data it needs — on its terms.
Built for data that's actually complex
Control the shape
of your data
Analyze seed samples and define exactly what you need — distributions, edge cases, formats, regions, device types, time periods. Your data should reflect your world, not just your history.
Generate more.
Spend less.
Choose your model at each step. Revise outputs automatically. Stop paying human annotators to fix what the pipeline should handle.
Know your data works
before it ships
DataFramer enforces your constraints, structures, and file types at scale. Then lets you validate — compare against expectations or chat directly with your dataset before it touches your model.
The problems DataFramer was built for
Eval datasets that actually
test your model
Expand seed data, generate edge cases, and build evaluation sets that reflect real-world distributions — at the volume your model deserves to be tested against.
When you can't touch
the real data
Anonymize, simulate, or synthesize compliant alternatives without sacrificing the structural fidelity your workflows depend on.
Testing & Training data at the complexity
your model needs
Long-form documents, nested hierarchies, multi-file samples, financial statements, multi-turn conversations, legal contracts — DataFramer handles the data types that generic tools can't.
One platform. Generation, anonymization, transformation, simulation.
High-volume input expansion and high-volume output — not just samples.
Nested structures, multi-format, multi-file. Complex data, handled.
Human review built in — for the workflows that need it.
Your next dataset
shouldn't take a sprint.
DataFramer is built for teams who move fast and need data infrastructure that keeps up.