Your AI teams are ready. Their data isn't.
Take your own data further — generate, anonymize, and simulate diverse datasets for LLM fine-tuning, eval calibration, RAG testing, and agent evaluation. Starting from your own samples. Diverse, distribution-tuned datasets.
What's blocking your AI team?
Your seed data isn't enough to train or evaluate on.
Generate diverse, scaled datasets from your own samples — instruction sets, dialogue examples, domain-specific records — at the volume your model actually needs.
G — GenerateYour eval sets don't cover what your model will actually face.
Simulate edge cases, adversarial inputs, demographic slices, and failure modes — including scenarios your real data never captured.
S — SimulateYour production data is too sensitive to use directly.
Anonymize or transform it — structure intact, PII removed. Use real observability data to seed more realistic synthetic datasets.
A — Anonymize, AugmentDiverse, distribution-tuned datasets.
DataFramer starts from your real samples and extends them faithfully — preserving schema, distributions, and constraints. The outputs behave like your data because they were built from it.
Why schema fidelity and distribution control matter
Generic data generation tools produce outputs that are statistically plausible but contextually wrong — the right shape, the wrong behavior. Models trained or evaluated on that data perform well in testing and fail in production.
DataFramer starts from your real samples. It analyzes the structure, value ranges, relationships, and constraints in your seed data — then generates diverse outputs that stay within those boundaries. You define the distributions. You control edge case density, scenario weighting, and output volume. And before anything touches your pipeline, you compare expected vs generated distributions to catch drift early.
Why AI teams are blocked
| Challenge | Description |
|---|---|
| Evaluation Blind Spots | Models fail silently on edge cases, adversarial inputs, and demographic slices that aren't well-represented in test sets. |
| Training Data Bottlenecks | Quality labeled data is expensive and slow to collect. Public datasets are overused, and scraping raises legal and ethical concerns. |
| Red-Teaming at Scale | Manual red-teaming doesn't scale. Teams need systematic ways to probe for jailbreaks, hallucinations, and harmful outputs. |
| Reproducibility & Versioning | Training runs are hard to reproduce when data sources change or disappear. Synthetic pipelines offer deterministic, versionable datasets. |
| Data Licensing & IP Risk | Using scraped or licensed data creates legal exposure. Synthetic alternatives sidestep these issues entirely. |
Works from your data — adding diversity while preserving structure and constraints.
Diverse, distribution-tuned datasets. DataFramer starts from your real samples — instruction datasets, dialogue logs, structured outputs, domain-specific records — and extends them faithfully. Every output respects the schema, value distributions, and structural relationships your models depend on. Compare expected vs generated distributions before anything touches your pipeline.
Any textual dataset. Multi-turn conversations, nested JSON, structured outputs, function-calling examples, RAG document corpora, agent interaction logs — any format, any complexity.
How DataFramer solves it
Each solution starts from your own samples — no random generation, no fabricated inputs that don't reflect your actual data distribution.
| Solution | Description |
|---|---|
| Eval Suite Builder | Expand sparse seed datasets into diverse calibration sets for LLM judges. Compare generated distributions against expectations before your eval pipeline runs. |
| Edge Case & Adversarial Simulation | Generate adversarial prompts, jailbreak attempts, demographic slices, and rare failure modes systematically — covering scenarios your real data never captured. |
| RAG & Retrieval Testing | Create synthetic document corpora and query sets seeded from your real documents — structurally faithful, diverse, and privacy-safe. |
| Agent & Tool-Use Testing | Generate multi-step interaction scenarios to test AI agents across complex workflows — including hypothetical and demo scenarios you don't have real data for yet. |
| LLM Fine-Tuning Data | Generate instruction-following datasets, function-calling examples, and domain-specific training data — seeded from your own examples, faithful to your schema and distributions. |
When your LLM judges don't align with human labels
LLM-based evaluation systems start with a calibration problem: judges trained or prompted on sparse seed datasets don't reliably align with human labels. The fix isn't more prompting — it's more diverse, structurally faithful calibration data.
DataFramer expands sparse seed datasets into the volume and diversity your judges need to calibrate reliably — without waiting months for real user interactions to accumulate. Teams using DataFramer for eval calibration report faster judge alignment and reduced dependence on slow human annotation cycles.
When production signals should inform your training and eval data
The most realistic synthetic datasets aren't built from scratch — they're seeded from real production behavior. DataFramer supports a closed-loop workflow: real observability data from your production environment seeds the generation process, producing synthetic datasets that reflect actual usage patterns rather than idealized assumptions. As your production signals evolve, your training and eval data can evolve with them.
When your RAG pipeline needs more than a handful of test documents
RAG evaluation requires diverse, realistic document corpora — varied in content, structure, and retrieval difficulty. Building that test set manually takes weeks. DataFramer generates diverse document corpora seeded from your real documents, expanding coverage across topics, formats, and retrieval scenarios without fabricating content that doesn't reflect your actual knowledge base.
Why not build it yourself?
You can. But accurate distribution control, schema-faithful generation, automatic revision loops, multi-format support, and distribution comparison tooling take months to build and maintain. DataFramer lets your team use that time on the model, not the data pipeline.
How DataFramer compares to using LLMs directly
Using an LLM directly to generate training or eval data is a common starting point — and it works for simple cases. The limitations appear quickly: outputs don't preserve your schema, distributions drift from your real data, there's no validation layer, and at scale the cost and inconsistency compound. DataFramer wraps the generation process with distribution control, automatic revision loops, schema enforcement, and distribution comparison — so the outputs are reliable enough to ship with, not just to explore with.
Use Cases
| Use Case | Description |
|---|---|
| LLM Fine-Tuning | Generate instruction-following datasets, function-calling examples, and domain-specific training data |
| Targeted Evaluation | Spotted an issue in production? Generate test cases for that specific failure mode in minutes, not weeks of data collection |
| Red-Teaming & Safety | Systematically probe for jailbreaks, prompt injections, and harmful outputs |
| RAG & Search Testing | Create synthetic document corpora and query sets to evaluate retrieval pipelines |
| Agent & Tool-Use Testing | Generate multi-step scenarios to test AI agents with tool access and complex workflows |
| LLM Judge Calibration | Expand sparse seed datasets into diverse calibration sets so LLM judges align with human labels — without waiting for thousands of real user interactions |
| Observability-Driven Generation | Seed synthetic datasets from real production observability data to create a tighter loop between production signals, evaluation, and training |
| Complex and Domain-Specific Data Formats | Generate and anonymize datasets in complex, domain-specific formats — nested JSON, XML variants like mzML, multi-file packages, high-token documents, time series, and instrument-specific schemas. DataFramer preserves structural constraints and domain-specific value ranges that generic tools ignore. |
Key Benefits
| Benefit | Description |
|---|---|
| Starts from your data | Diverse, distribution-tuned datasets. Seed-based generation preserves your schema, distributions, and constraints — outputs behave like your data because they were built from it. |
| Distribution control | Define exactly what you need — edge case density, demographic splits, scenario weighting, output volume. Your eval set reflects your world, not a generic one. |
| Verify before it touches your model | Compare expected vs generated distributions. Chat with your dataset. Catch distribution drift before it reaches your pipeline. |
| Ship faster | Unblock training and eval pipelines in hours, not sprints. No waiting on data collection, labeling, or legal review. |
| Lower cost per sample | Choose your model at each generation step — OSS, small, or large LLMs. Revision loops reduce human labeling costs. Optimized generation runs at a fraction of alternatives. |
| Reproducible and versionable | Deterministic generation makes runs comparable and auditable. No dependency on external data sources that change or disappear. |
Common questions from AI and ML teams
How is DataFramer different from using Faker or an LLM directly?
Faker generates random values with no awareness of your data's structure, relationships, or domain constraints. LLMs generate plausible-sounding outputs that drift from your actual distributions. DataFramer starts from your real seed samples, analyzes the structure and constraints, and generates diverse outputs that stay faithful to what your data actually looks like — with built-in distribution comparison to verify before anything touches your pipeline.
Does DataFramer preserve schema and data structure in the outputs?
Yes. DataFramer analyzes your seed samples and enforces schema, value ranges, nested relationships, and domain-specific constraints in every output. You define the distributions. The outputs behave like your data because they were built from it.
Can we use DataFramer for LLM eval dataset generation and judge calibration?
Yes. DataFramer expands sparse seed datasets into diverse calibration sets for LLM judges — covering the distribution of examples your judges need to align reliably with human labels, without waiting for real user interactions to accumulate.
Does DataFramer support on-premise deployment?
Yes. DataFramer deploys inside your own environment — Databricks, AWS, or your own cloud infrastructure. Your data never has to leave. This is particularly relevant for teams working with proprietary models, sensitive production data, or strict data governance requirements.
Can DataFramer handle complex nested data formats?
Yes. DataFramer supports nested JSON, XML variants, multi-file packages, high-token documents, time series, and domain-specific structured formats. The more complex and context-sensitive your data, the more the seed-based approach matters — generic tools produce structurally invalid outputs for complex formats. DataFramer preserves the constraints that make the data usable.
How do we validate that the generated data is actually useful?
DataFramer includes built-in distribution comparison — compare expected vs generated distributions before anything touches your model or pipeline. You can also chat directly with your generated dataset to inspect and validate outputs interactively.
"Companies prefer buying synthetic data because of the hidden costs of building it yourself."
See what DataFramer does with your data.
Send us a sample dataset — instruction pairs, dialogue logs, structured records — and we'll show you diverse, faithful outputs in your schema and format.