DataFramer

For teams building AI into real products and business workflows

Connect business outcomes to AI quality, and improve both.

DataFramer tracks how your users' journeys lead to business outcomes, pinpoints where AI falls short, and gives your team fixes based on real feedback.

Start with free credits. No credit card required. Or see DataFramer in action with our team.
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DataFramer AI Quality Loop AI BEHAVIOR FIXES & ROOT CAUSES REUSABLE KNOWLEDGE HUMAN JUDGMENT AI TRACES WORKFLOW EVENTS USER ACTIONS EXPERT JUDGMENT BUSINESS OUTCOMES Detected failures Monitored traces Problems library Alerts Prioritized findings Root cause analysis Clustered failures Prioritized fixes Trace diagnostics Reusable memory Human-aligned judges Expanded datasets Optimized prompts Cost-efficient models Evaluation suites Copilot Assigned traces Accelerated reviews Organized rubrics Structured feedback Accuracy Adoption Cycle time Business value

Your Institutional AI Quality Layer

A private layer that encodes how your business wants AI to work.

Why DataFramer exists

Can you answer how AI is affecting your business and users?

DataFramer connects business outcomes to the AI behavior behind them, identifies where AI falls short, and puts the right humans in the loop to review, correct, and improve it.

Tasks completed successfully

Escalations to a human

Process completion time

Human corrections and retries

Cost per completed task

People who gave up midway

Support tickets raised

People coming back to use it

AI’s accuracy and business value are hard to prove.

Business outcomes

Teams cannot see whether AI workflows are becoming more accurate, more adopted, faster, and more valuable.

Important signals hide across AI traces and their root causes are hard to pin down.

Discovery & diagnosis

Bad answers and AI behavior look successful on the surface. Even after you find one, pinning down the root cause can be hard.

Human review is slow and unstructured.

Expert review

Domain experts know what good looks like, but their feedback gets trapped in spreadsheets, tickets, and one-off reviews.

Optimizations feel like risks.

Fixes & evals

LLM judges need calibration. QA datasets miss messy edge cases. Fixes can introduce regressions.

Continuous improvement is not continuous.

The loop

Reviews, evals, fixes, and rollout are stitched across tools. Lessons do not compound into reusable business context.

The AI Quality & Business Impact Platform

Better AI comes from connecting business outcomes, accuracy, and human judgment.

DataFramer turns scattered quality work into a connected operating loop.

Unify

Bring every part of the AI workflow together

Connect AI outputs, user behavior, feedback, workflow events, and expert judgment in one place to see how each step affects the user and business outcome.

Business impact

Tie the outcomes that matter to AI accuracy and quality

Measure accuracy, adoption, completion, speed, human effort, cost, and value across the full workflow.

Discover + Diagnose

Find accuracy deviations and important patterns in AI behavior

Surface known and unknown signals across thousands of traces, group related cases into clear findings, and investigate each one with full context.

Human Review

Turn expert review into reusable ground truth

Send traces to domain experts with full context. Their scores, rationales, and corrections become reusable ground truth for judge calibration and future reviews.

Track + Validate

Track accuracy, regressions, and impact across user journeys

Watch Human-graded, AI Judge scores, and Judge-Human Alignment alongside adoption, completion, cycle time, cost, and business value.

Enterprise clarity with startup voltage.

Ready to make AI quality repeatable?

Start with free credits. No credit card required. Or see DataFramer in action with our team.