Use case — Engineering, PM, Domain experts
Expert judgment shouldn't live in a spreadsheet.
Capture it in a form engineering can actually use.
Domain experts know what good looks like. The hard part is capturing that consistently, in a form that travels. We found that most teams do this in spreadsheets, Slack threads, or one-off ticket comments. DataFramer gives the whole review process a structure that works for both sides.
What breaks without structure
Feedback gets captured but never reused.
A reviewer flags a bad response, the engineer fixes it, and both move on. The same judgment call gets made again on the next project because nothing was written down in a form that travels.
Reviewers apply different standards without knowing it.
Without shared rubrics, two reviewers scoring the same trace reach different conclusions. The feedback is real but inconsistent, so it's hard to aggregate and act on.
Experts review without enough context.
A domain expert gets a trace and is expected to know what they're evaluating, why it was flagged, and what good looks like. When that context isn't there, review quality suffers.
Engineering can't use what comes back.
Qualitative comments in a doc are hard to turn into a model improvement. Without scores, rubric dimensions, and structured outcomes, engineering has to translate feedback before they can do anything with it.
How DataFramer structures review
From trace to structured feedback to reusable standard.
Define what good looks like, once
Rubrics tell reviewers what to look for, which fields are required, and how to interpret a trace. You set scoring dimensions (pass/fail, scale 1-5, checklist), add positive and negative examples from real traces, and specify the audience. The same rubric guides both human reviewers and any LLM judges you build later.
Rubric Studio
Route traces through Queues
Queues organize review work by priority (low, medium, high, critical) and routing mode (round robin or manual). Each queue is tied to specific rubrics and a set of reviewers. Traces reach reviewers with the failure context, finding collection, and rubric already attached.
Queues
Reviewers score with full context
Each assigned trace shows the finding it came from and the rubric being applied. Reviewers score dimensions, select an outcome, set severity and confidence, and write an explanation. Structured, not freeform.
Assignments
Completed reviews land where engineering can use them
Submissions are visible across the team and filterable by reviewer, outcome, rubric, and severity. Selected submissions can be added directly to a judge eval dataset. The feedback becomes test data, not just a comment.
Submissions
Standards carry into the next project
The rubrics, scoring examples, and reviewer patterns from one project stay in DataFramer. When a new AI workflow starts, the standards your team worked out are already there.
Memory
Built for both sides of the review
Reviewers focus on judgment. Engineering gets something they can use.
For domain experts & reviewers
- Your queue shows exactly which traces are assigned to you and why
- Each trace arrives with the finding context and rubric already attached
- Score dimensions, pick an outcome, add an explanation — no freeform guesswork
- Confidence and severity fields let you flag ambiguous cases honestly
- Review stages track where each trace is in the process
For engineering & PMs
- See all submissions across the team, filter by reviewer, outcome, and rubric
- Add reviewed traces directly to judge eval datasets in one action
- Routing presets send reviewed traces into eval, regression, or detector workflows
- Rubrics defined here feed directly into LLM judge creation
- Review history stays in DataFramer, not lost when a reviewer leaves or a doc is archived
Stop losing expert judgment to one-off reviews.
Free to start, no card required. Bring your own model key or use DataFramer credits.