Your data doesn’t cover what your models will face.
Turn your limited seed data into eval sets with supporting documents and ground-truth labels, fine-tuning datasets, and privacy-safe data.
DataFramer solves what Claude Code and GPTs can't.
Raw GPTs fall into mode collapse, style drift, and length shrinkage. Read more →
Co-located golden labels
The artifacts (PDF, XML, JSON, etc.) and ground-truth labels are generated together. Not a separate labeling job you schedule later.
Scale
Thousands of samples, multi-file packages, and 50K-token documents. Not one-at-a-time generation with manual review.
Distribution control
Define exactly what varies and how often via a reusable spec. GPT only follows a prompt. DataFramer enforces a distribution.
Quality enforcement
Revision cycles, conformance filtering, and tool validators for consistency. Built in, not wired together yourself.
Structured output guaranteed
CSV, JSONL, PDF, multi-folder. Schema-validated every time. No prompt engineering needed to keep outputs consistent.
Reproducibility
The same spec produces the same distribution across runs. Regression suites stay stable. GPT output is non-deterministic by default.
Workflow integration
MCP server, Python SDK, Databricks connector — DataFramer lives inside your existing AI workflow.
Cost
DataFramer optimizes generations and data analysis to keep your costs low yet supporting complex, high-scale generation
YOUR AI Dataset Layer. Deployed On our cloud or yours.
Python SDK / API · MCP · UI
The dataset layer for evals, fine-tuning, and model reliability
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.
DataFramer generates ground-truth labels alongside your data, so you're not running a separate labeling pass for what the pipeline already produces. Choose cost-efficient models at each step and revise outputs automatically.
Know how 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 with golden labels,
built from your data
Go from a handful of hand-labeled examples to a full eval set, with golden labels, contextual data, and targeted edge cases generated alongside each sample. Upload production interactions as seeds and grow your regression suite without a manual labeling pass.
When you can't touch
the real data
Anonymize, simulate, or synthesize compliant alternatives without sacrificing the structural fidelity your workflows depend on.
Training datasets with
the labels already there
Generate balanced, annotated training datasets for fine-tuning and post-training. Introduce new fraud concepts not in your seed, control class distributions, and produce the rare failure modes your production data never captured, all with annotation labels attached.
DataFramer sits inside your
eval and improvement loop
Not a one-time data dump. A dataset layer you return to as your model improves and your failure modes change.
The dataset layer for
eval, fine-tuning, and model reliability teams.
Stop hand-labeling. Stop blocking on data. DataFramer generates the datasets, including their ground-truth labels, exactly the way your workflows need them.