DataFramer provides an institutional learning layer for AI, connecting business outcomes to the AI behavior behind them. The platform identifies where AI falls short and puts humans in the loop to review, correct, and improve workflows, ensuring every lesson compounds into better business results. It helps teams prove AI accuracy and business value by surfacing hidden signals across AI traces.
Funding
Funding not disclosed
Founders
Product
Problem
Businesses lack an institutional learning layer for AI, causing projects to repeatedly relearn what good looks like and delaying business impact. Teams cannot see whether AI workflows are becoming more accurate, more adopted, faster, and more valuable, as important signals hide across AI traces and their root causes are hard to pin down.
Solution
DataFramer connects business outcomes to the AI behavior behind them, identifying where AI falls short and putting the right humans in the loop to review, correct, and improve it. The platform surfaces critical signals such as escalations to a human, process completion time, human corrections and retries, people who gave up midway, support tickets raised, and repeat usage. By making AI’s accuracy and business value provable, DataFramer ensures every lesson compounds into better business results, enabling teams to make lab-trained AI work the way their business needs.
Target Audience
Primary customers are teams building AI into real products and business workflows, including data science, product, and operations teams that need to prove AI’s business impact and continuously improve its performance.
Features
- Tracks business outcomes alongside AI behavior to quantify accuracy, adoption, speed, and value
- Identifies root causes of AI failures by analyzing hidden signals across AI traces
- Integrates human-in-the-loop workflows for reviewing, correcting, and improving AI performance
- Monitors key metrics including escalations, process completion time, human corrections, abandonment rates, support tickets, and return usage
- Provides visibility into whether AI workflows are becoming more accurate, adopted, faster, and more valuable over time