DataFramer
DataFramer is an AI observability and improvement platform that connects business outcomes to the AI behavior behind them, helping teams identify where AI falls short and put the right humans in the loop to review and correct it. The platform tracks signals like escalations, process completion time, human corrections, and abandonment rates to make AI accuracy and business value provable. DataFramer runs on models served from a company's own Databricks workspace, ensuring data never reaches another provider's models.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Developer Tools
- Software Only
- AI Agents
Funding
Founders
Founder details are not available yet.
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. The platform runs on models a company's own Databricks workspace serves, so data never reaches another provider's models, and it can generate larger datasets from Unity Catalog samples that preserve structure and variation.
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
- Runs on a company's own Databricks-served models, with credentials stored under Settings > Keys and billing through the customer's Databricks account
- Generates synthetic data from Unity Catalog tables or volumes using notebooks that authenticate as the runner and read the DataFramer API key from a Databricks secret scope
- Includes calibrated judges, dataset evaluations, API guardrails, and Review Copilot features that all call the customer's own models