What's blocking your AI team?
You don't know what's actually failing in production.
Surface hidden failures across production traces, including outputs that are wrong, incomplete, or subtly off in ways standard metrics never catch.
When you find a failure, figuring out why takes hours of manual trace inspection.
Narrow the root cause to the prompt, retrieval step, tool call, reasoning step, or workflow logic without reading every trace by hand.
Expert feedback from one sprint doesn't make it into the next eval suite.
Structure reviews with shared rubrics, capture reviewer judgment in a reusable form, and turn reviewed examples into regression tests that travel with every new version.
What DataFramer does
Failure Discovery
Search for known failure patterns, browse a problem library, or scan broadly to find failures you were not looking for. Surface outputs that are wrong, incomplete, subtly off, or worth understanding before users find them.
Root Cause Analysis
When a trace fails, narrow down where in the workflow it broke: the prompt, retrieval, context, tool call, reasoning step, or model behavior. In agentic workflows, trace failures back through multi-step chains to where the decision actually went wrong.
Expert Review Workflow
Route the right traces to the right reviewers with shared context and rubrics. Feedback comes back structured and consistent rather than scattered across Slack threads. Reviewer judgment gets recorded in a reusable form that flows into evals and regression suites.
Judge Calibration
Use reviewed examples to align LLM judges with human labels. Catch judges that reward-hack, drift after model changes, or score differently based on wording. Build confidence in automated scoring before it runs at scale.
Regression Testing
Turn reviewed examples into regression suites that travel with every new version. When a prompt or model changes, test it against failures that were already validated. Catch regressions before they reach users.
Use cases
Silent Failure Discovery
Find the failures that look like successes: wrong outputs, incomplete answers, and domain-specific errors that standard metrics never flag
Agentic Workflow Debugging
Trace failures in multi-step agent chains back to the decision that started it, not just the output where it surfaced
RAG Quality Review
Find retrievals that return relevant documents but miss the business context, and route them to reviewers who can judge correctness
Expert Review Workflows
Route traces to domain experts with shared context and rubrics. Capture feedback in a form engineering can act on directly
LLM Judge Calibration
Use reviewed examples to align judges with human labels. Catch reward-hacking and drift before it degrades your eval scores
Regression Suite Building
Turn reviewed production failures into regression tests that travel with every new prompt or model version
Cross-Team Knowledge
Share failure patterns, rubrics, and reviewed examples across teams so each new project starts with more than the last one had
Production Quality Monitoring
Track recurring failure patterns and whether fixes are actually holding across projects and teams
Find your first hidden production failure.
Connect your traces and see what your metrics are missing.
Common questions from AI and ML teams
How does DataFramer connect to our existing observability stack?
DataFramer integrates with LangFuse, LangSmith, and other observability tools. You bring your production traces and DataFramer helps you find failures, diagnose root causes, structure expert review, and build regression tests from what you find.
We already have an eval framework. How does DataFramer fit alongside it?
DataFramer sits upstream of your eval framework. It helps you find the failures that should become eval cases, get human review on them, and build regression suites from reviewed examples. Your eval framework runs the tests. DataFramer helps you build the right test set from real production failures.
How does DataFramer help with LLM judge calibration?
DataFramer lets you use real reviewed examples to align judges with human labels. You can see where your judge disagrees with reviewers, catch reward-hacking, and update calibration as models and prompts change. The goal is automated scoring that stays aligned with what actual domain reviewers consider correct.
How does expert review work in DataFramer?
You route specific traces to specific reviewers with context and rubrics already attached. Reviewers do not need to be AI engineers. They answer the questions relevant to their domain. Their feedback gets recorded in a structured way and flows directly into eval datasets and regression suites.
What AI systems does DataFramer work with?
DataFramer works with RAG pipelines, agentic workflows, LLM applications, and multi-step AI systems. If it produces traces, DataFramer can help you find failures in them.
Can DataFramer be deployed in our own environment?
Yes. DataFramer deploys on-prem or in your own cloud. For teams with strict data governance requirements or proprietary production data, your traces stay inside your infrastructure.