Use case — Engineering, PM
AI quality shouldn't reset with every project.
Track it across all of them.
When teams run multiple AI workflows, quality work happens in silos. Each project starts over: new rubrics, new judges, no memory of what failed before. DataFramer tracks accuracy across all your projects in one place and carries what you learned forward. Customers told us this was one of the main reasons their rollouts got faster over time.
What happens without a shared quality layer
Every new project starts from zero.
Rubrics get redefined, judges get rebuilt, failure patterns get rediscovered. Work from the last project doesn't transfer because there's nowhere for it to live.
Nobody knows how quality is trending across the org.
Individual teams track their own metrics, but there's no shared view across workflows. Leadership can't see which projects are improving, which are regressing, or where quality problems are building up.
The same failures get found over and over.
A hallucination pattern found in a customer support workflow shows up again in a document summarization workflow six months later. Without a record of what was found and fixed, teams repeat the same investigative work.
Fixes don't compound.
A prompt fix that resolved a retrieval failure in one workflow has no path into the next one. Each team makes its own discoveries and keeps them local.
What DataFramer carries forward
Each project teaches the system something the next one can use.
Tracked findings
Failure patterns saved in one project stay visible as new matching traces arrive. When a similar pattern shows up in another workflow, DataFramer already knows to look for it.
Rubrics
Quality criteria defined for one workflow are available to the next team that needs them. No one rewrites the same rubric from scratch.
Calibrated judges
Judge prompts built and calibrated against human reviewers carry across projects that share the same rubric. Agreement scores travel with them.
Regression datasets
Eval datasets built from real failures stay in DataFramer. Future projects in the same domain can test against failures that were already found and fixed, not just new ones.
Expert feedback
Reviewer input doesn't disappear. Patterns in how experts scored outputs, what they flagged, what they passed, inform how new projects get reviewed.
Fix history
Fixes linked to root causes and tracked findings give future teams a starting point. When a known failure type reappears, there's a record of what resolved it.
How tracking works in practice
Findings that keep updating as new data arrives.
Save the failures worth watching
When Discovery surfaces a failure pattern worth monitoring, save it to Tracking. It auto-updates as new matching traces arrive, so you always have a current count without re-running discovery.
Tracking
See quality trends at a glance
The dashboard shows trace volume, latency, cost, model distribution, and failure counts for the active time window. Widgets are draggable and resizable. The Findings Map gives a full-screen view of everything being tracked across the project.
Dashboard
Get alerted when something spikes
Enable Slack alerts on any tracked finding. When a known failure pattern spikes, your team finds out before users do.
Alerts
Compare any time range
Scope all tracking data to last hour, last 7 days, last 90 days, or all time. Trace counts and metrics update to match. Compare ranges to see whether quality is actually moving.
Time window
Make each AI rollout smarter than the last.
Free to start, no card required. Cross-project tracking scales on paid plans.