DataFramer is now a Validated Databricks partner.
Compounding accuracy for your AI workflows.
Turn AI product data into a reusable, governed quality loop that learns from every review, failure, and fix.
Why DataFramer exists
Production AI quality is becoming the bottleneck.
Teams can launch pilots, but once AI touches real workflows, quality work 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 tools reset with every project. DataFramer compounds.
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. 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 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 failures automatically, groups recurring patterns, and diagnoses them to the source: prompt, retrieval, context, tool call, model behavior, or workflow step.
Review
Route failures to expert review
Send the right traces to domain experts with the surrounding context, failure collection, 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 failures and 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.
The DataFramer difference
The full AI quality loop, not another point tool.
DataFramer learns from each review cycle and carries that forward.
Observability shows what happened. DataFramer helps teams decide what matters and what to do next.
Evals test known cases. DataFramer turns real failures and expert feedback into new evals and regression suites.
Review tools capture feedback. DataFramer turns expert judgment into reusable quality intelligence.
LLMs provide model intelligence. DataFramer builds quality intelligence specific to your workflows from traces, reviews, rubrics, and fixes, and carries it forward so each new project starts from what the last one learned.
Enterprise clarity with startup voltage.