DataFramer is an AI Workflow Intelligence Platform for accurate, high-value AI workflows. It helps answer the following questions:

1. Is the AI behaving accurately within the context of your business?
2. Is the complete AI-powered workflow delivering value to your users and the organization?

It does that by bringing together AI traces, user behavior, worflow events, and expert judgment into one repeatable operating layer. It connects individual model and agent interactions to the user journeys and workflows in which they occur. From there, DataFramer turns this data into a continuous cycle of analysis, human review, evaluation, and measurable workflow improvement.

​

How it works

DataFramer follows one loop, end to end: Capture & Correlate Capture user interactions, feedback, and workflow events, from your applications with the Signals SDK, connect your AI observability tool (ex. Langfuse, LangSmith) to pull existing traces with metadata, and auto-tag traces with dataframer-journey. DataFramer stitches signals, events, and traces together with a shared journey id, so a user’s actions and the AI traces around them can be unified into User Journeys. Measure User Journeys See the complete timeline of each user journey or their workflow, including user actions, AI traces, agents, and workflow events, in one place. Then zoom out to analyze all journeys and understand completion, adoption, drop-offs, throughput, and cycle time to understand where AI is helping or hurting the workflow and prioritize improvements that increase accuracy, efficiency, and business value. Discover & diagnose patterns Findings lets you search your traces for any AI behavior you care about, not just failures or accuracy problems. It surfaces the known patterns you already track and helps you find new ones. From a pattern you can look into likely causes, focus on the deviations that matter most, watch how often each one comes back, and check that a fix really worked without breaking something else. Human review operations Send selected traces to human experts in Human Reviews, the structured layer that captures expert judgment as reusable data, not scattered feedback. Grading standards are set in Rubric Studio and humans grade traces through the operational loop, with the Review Copilot speeding them up. Every correction becomes ground truth for your judges and datasets, reused on the next similar case instead of redone from scratch. Evaluate & generate Build a Judge from the same grading standards and calibrate it against your reviewers’ verdicts until its scores align with theirs, then run it across datasets of real reviewed traces to score quality at scale and catch regressions before they reach users. For gaps your traces don’t cover, generate synthetic data grounded in your real data.

​

Everything compounds

Each step feeds the next, and the loop closes. Journeys show where AI helps or hurts. Findings turns that into patterns, Reviews turns patterns into expert judgment, and Judges and datasets turn that judgment into quality you can trust at scale, which sharpens the next round. Along the way, context builds up. Your corrections, the rubrics and examples that define what good looks like, and the failure patterns you name all add up into a shared memory of how your business judges quality. New reviews start from judgments already made, judges calibrate against more and more ground truth, and a new workflow starts from the standards you’ve already set instead of from zero. Measured in business terms All of it rolls up across your journeys dashboards. You see the outcomes that matter, like completion, drop-off, throughput, cost, and cycle time, so you can tell whether AI is delivering value, not just running.

​

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.

​

Programmatic access