DataFramer is now a Validated Databricks partner.
The accuracy intelligence platform for AI workflows.
Turn quality signals, user feedback, and expert judgment into reusable tests, calibrated judges, and business context for improving every AI workflow.
Why DataFramer exists
When AI touches real workflows, accuracy management becomes manual, fragmented, and haphazard.
Finding accuracy failures is manual.
Failure detectionBad answers can look successful on the surface: wrong, incomplete, unsupported, too generic, or subtly off in the domain.
Root cause analysis is hard.
Root causeA failure could come from prompts, retrieval, context, tool calls, workflow logic, model behavior, or the judge itself.
Human review is slow and unstructured.
Expert reviewDomain experts know what good looks like, but their feedback gets trapped in spreadsheets, tickets, and one-off reviews.
Optimizations feel like risks.
Fixes & evalsLLM judges need calibration. QA datasets miss messy edge cases. Fixes can introduce regressions.
Continuous improvement is not continuous.
The loopReviews, evals, fixes, and rollout are stitched across tools. Lessons do not compound into reusable business context.
The quality loop
AI quality improves when human judgment becomes reusable.
DataFramer turns scattered quality work into a connected operating loop.
Ingest
Works above your existing stack, or use our SDK
Keep your traces in Langfuse, LangSmith, or wherever they already live. Send usage and behaviour data. DataFramer connects above your stack without replacing anything. If you'd rather send data directly, the SDK handles traces, user feedback, corrections, ratings, and any product event you want to capture.
Discover + Diagnose
Find hidden accuracy failures and diagnose why they happened
Production AI fails silently: wrong answers look normal, incomplete reasoning gets through, retrieval misses go unnoticed. DataFramer surfaces these accuracy failures automatically, groups recurring patterns, and diagnoses them to the source: prompt, retrieval, context, tool call, model behavior, or workflow step.
Review
Send accuracy failures to expert review
Send the right traces to domain experts with the surrounding context, Tracked Findings, and rubric attached. Reviewers score what happened, explain what good should look like, and capture judgment in a structured form engineering can use.
Standardize
Turn expert judgment into standards
DataFramer unlocks reusable rubrics, calibrated judge prompts, regression datasets, and multi-reviewer submissions. Human judgment becomes a repeatable quality system, not a one-time annotation exercise.
Validate
Prove the fix worked
DataFramer turns real accuracy failures and human expert feedback into eval and regression datasets. Before a fix ships, you can test it against the production cases that caused the problem.
Compound
Build quality memory
The rubrics, failure patterns, and fixes from one project carry into the next. Each new AI workflow starts with what the last one taught the system. One of the clearest ways DataFramer pays back over time.
Enterprise clarity with startup voltage.