AI quality tools reset with every project. DataFramer compounds.
DataFramer covers the full loop: discovery, expert review, evals, and regression.
Works above your existing observability stack · Fully-featured
The full quality 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.
Capabilities
Failure discovery & collections
DiscoveryDataFramer finds failures with 83%+ accuracy and groups similar ones into collections. You can track recurring patterns over time or search by failure type and custom prompts.
Root cause diagnosis
DiagnosisTrace each failure to its source: prompt, retrieval, context, tool call, model behavior, workflow step, schema, or missing business context.
Expert review workflow
ReviewAssign traces to domain experts with the context they need. Capture structured feedback through rubrics and scores and turn their judgment into something engineering can act on.
Rubrics & review standards
StandardsDefine what good looks like per workflow, attach real examples, and update rubrics as new failure modes show up. The same rubrics guide both human reviewers and LLM judges.
LLM judge creation & calibration
JudgesBuild judges from expert feedback and measure how well they agree with human reviewers before trusting them at scale.
Eval & regression datasets
EvaluationConvert real failures and expert corrections into test cases, generate edge cases from known failure patterns, and test every change against real problems before it ships.
Fix & regression memory
MemoryLink failures to the root causes and fixes that resolved them. Track quality before and after. When a similar issue shows up in another workflow, you already know what worked.
Cross-project quality intelligence
ScaleFailure patterns, rubrics, expert judgment, and validated fixes carry across projects. Each AI rollout benefits from every one that came before it.
The compounding moat
Here's what actually carries forward.
The system accumulates context about your AI workflows: which failure types appear in your domain, what rubric standards your reviewers apply, which fixes have worked before. When you start a new AI project, that context is already there.
Expert feedback doesn't disappear. It becomes part of how the next review is run.
Rubrics defined for one workflow apply to the next one too.
When a known failure type shows up in a new project, DataFramer already knows to look for it.
Fixes that worked get remembered and applied when similar issues show up later.