Your AI teams are ready. Their data isn't.
Take your own data further — generate, anonymize, and simulate diverse datasets for testing, evals, and fine-tuning.
or
create them in the platform...
Works from your data — adding diversity while preserving structure and constraints.
DataFramer starts from your real samples and extends them faithfully — respecting the shape, rules, and relationships your models depend on.
What's blocking your AI team?
Your data isn't enough?
Generate diverse, scaled datasets without starting from scratch.
G — GenerateYour real data is off-limits?
Anonymize or Augment it — structure intact, sensitive content removed, transformed to your needs.
A — Anonymize, AugmentYour data doesn't cover what your model will face?
Simulate the edge cases and scenarios your real data never captured.
S — SimulateYOUR AI Data Layer. Deployed On our cloud or yours.
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 cost-efficient models 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.