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DataFramer sits above your existing AI observability stack, combining traces, user behavior, and expert judgment to identify where AI behavior deviates from expectations. It helps teams diagnose root causes, structure expert review, and turn validated feedback into judges, regression datasets, and reusable business context. The result is a repeatable accuracy-management loop for improving individual AI features, complete applications, and the larger workflows they support.

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The pipeline

DataFramer follows one loop, end to end: 1. Capture & Correlate: Send user events from your app with the Signals SDK, and bring in your AI traces, either connect your observability tool (Langfuse and LangSmith are supported today) to pull existing traces, or auto-tag new ones as they’re created with dataframer-journey. DataFramer stitches signals and traces together with a shared journey id, so a user’s actions and the AI traces around them line up automatically. 2. View & Filter Journeys: Browse journeys: a user’s full timeline, with the AI traces that happened inside it, in one view. Or zoom out with dashboards: counts, trends, funnels, and cycle times across all your journeys and users. 3. Discover patterns: Findings surfaces known failure patterns you’re tracking and helps you discover new, unknown ones through guided exploration of your traces. 4. Assign to reviewers: Route flagged traces to human experts in Reviews. Their corrections become ground truth, and get reused automatically the next time a similar case shows up. 5. Evaluate & generate: Turn reviewed traces into rubrics, calibrate Judges against human judgment, and use DataFramer’s dataset generation to build regression sets and synthetic training data from the failure patterns you found.

Signals & Journeys

Capture user events and correlate them with AI traces

Findings

Discover known and unknown failure patterns

Reviews

Structure expert review and reuse feedback

Judges & Datasets

Calibrate judges and generate synthetic data

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Also: synthetic data generation

Separately from the accuracy loop above, DataFramer can generate realistic, diverse synthetic datasets at scale, from example data or a text description alone. See Core Concepts and the Quickstart.

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Programmatic access

API & MCP

Python SDK and MCP server for datasets, specs, generation runs, and evaluations. Findings, Reviews, and Judges are UI-only today: no public API yet.