DataFramer

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AI Workflow Intelligence for accurate, high-value AI workflows.

DataFramer connects AI traces, user behavior, workflow events, and expert judgment in one continuous improvement loop.

Teams use DataFramer to understand where AI is helping or hurting, discover recurring behavior patterns, turn expert feedback into reusable ground truth, evaluate quality at scale, and measure whether AI-powered workflows are delivering real value.

Why teams use DataFramer

AI teams often struggle because:

• model performance is disconnected from user outcomes Connect AI traces to workflow completion, adoption, drop-offs, throughput, cost, and cycle time.

• important failures are difficult to find at scale Discover recurring behaviors, emerging patterns, and meaningful deviations across production traces.

• expert feedback is difficult to operationalize Turn human reviews, corrections, and grading standards into reusable rubrics and ground truth.

• evaluation datasets do not cover enough scenarios Build datasets from real traces and generate synthetic edge cases, rare scenarios, and controlled variations.

How it works

DataFramer supports a continuous workflow for measuring and improving AI systems:

1. Capture user interactions, workflow events, feedback, and AI traces
2. Correlate model behavior with user journeys and business outcomes
3. Discover recurring behaviors, failures, and emerging patterns
4. Review traces using expert-defined rubrics and structured workflows
5. Calibrate automated judges against human verdicts
6. Evaluate models and agents using reusable benchmark datasets
7. Generate synthetic data to expand coverage and test missing scenarios
8. Measure whether changes improve AI quality and workflow performance

Each correction, rubric, example, and identified failure pattern becomes reusable context for future reviews, evaluations, and datasets.

Core capabilities

• Signals and user journeys Connect user behavior, application feedback, workflow events, agents, and model traces into complete user journeys.

• Findings Search for known behaviors, discover unexpected patterns, investigate likely causes, and verify that fixes work.

• Human reviews and rubrics Define grading standards, assign traces to reviewers, capture corrections consistently, and create reusable ground truth.

• Automated judges Build and calibrate judges using the same standards applied by human reviewers.

• Datasets and evaluations Create benchmark and regression datasets from reviewed traces, production examples, and generated scenarios.

• Synthetic data generation Generate realistic, diverse datasets from seed examples or natural-language descriptions.

• Data anonymization Detect and mask sensitive information before using production-derived data in downstream workflows.

Synthetic data generation

DataFramer uses distribution-based generation to infer and control:

• data structure and format
• domain, tone, sentiment, complexity, and length
• probability distributions
• dependencies and co-occurrence patterns
• new values and dimensions beyond the original examples
• rare scenarios and edge cases

Generate structured records, documents, independent files, or multi-file samples for evaluation, testing, simulation, training, and fine-tuning.

Best-fit use cases

• AI and agent evaluations Build benchmark and regression datasets grounded in real traces, expert judgment, and targeted synthetic scenarios.

• Human-in-the-loop quality operations Standardize expert review and convert feedback into reusable evaluation data.

• Failure and behavior discovery Identify recurring issues, emerging patterns, and meaningful deviations across AI traces.

• Workflow-level measurement Connect model and agent behavior to completion, adoption, drop-offs, throughput, cost, and cycle time.

• Evaluation coverage expansion Generate edge cases, rare scenarios, and controlled variations missing from production data.

• Synthetic training and fine-tuning data Expand high-quality examples while preserving the required structure, style, and behavior.

• Privacy-aware testing Detect and mask sensitive entities before preparing data for development, testing, and evaluation.

• Red teaming and adversarial testing Generate targeted prompts and scenarios for testing models and agents under challenging conditions.

Developer access

Programmatic access is available through the DataFramer API, Python SDK, and MCP server for:

• datasets
• generation specifications
• synthetic data generation
• anonymization workflows
• evaluations

Learn more at https://www.dataframer.ai

Read the documentation at https://docs.dataframer.ai