DataFramer is now a Validated Databricks partner.
AI quality that compounds
with your business context.
Find hidden failures, structure expert review, validate fixes, and turn every lesson into reusable business context for faster AI rollouts.
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. Failures are hard to find, expert review is hard to structure, and lessons rarely carry into the next rollout.
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
Find failures. Know why. Reuse what you learn.
DataFramer turns scattered quality work into a connected operating loop, from hidden production failures to expert review, validated fixes, and reusable business context.
Find failures nobody's looking for.
Surface known and unknown problems across production traces, agent behavior, RAG outputs, and AI workflows before users find them.
Discover
Know exactly where it broke.
Narrow failures across prompts, retrieval, context, tool calls, workflow logic, judges, and model behavior.
Diagnose
Turn expert review into a repeatable workflow.
Route the right traces to domain experts with shared context and structured rubrics engineering can act on.
Review
Fix it, then prove it worked.
Turn expert feedback into eval datasets, regression suites, and judge calibration tied to real failures.
Fix & validate
Make every workflow smarter than the last.
The reviews, rubrics, failure patterns, and validated fixes from one project carry into the next. When a new workflow starts, DataFramer already knows your quality standards: failure patterns are tagged, rubrics exist, judges are calibrated. Customers told us this was one of the clearest ways DataFramer paid back over time.
Reuse
The DataFramer difference
The full AI quality loop, not another point tool.
DataFramer learns from each review cycle and carries that forward. When the second project starts, the failure patterns from the first are already tagged, your reviewers' rubrics are already written, and judges are calibrated from real labels rather than from scratch. Customers told us the second workflow moved noticeably faster than the first, not because the team worked harder but because less had to be rebuilt.
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.