Update README.md

README.md

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+
 tags:
+
+- ai-workflows
+- workflow-intelligence
+- ai-evaluation
+- llm-evaluation
+- agent-evaluation
+- ai-observability
+- human-in-the-loop
+- llm-as-a-judge
 - synthetic-data
 - data-generation
 - data-anonymization
-- simulation
-- llm-evaluation
 - fine-tuning
-- testing
-- privacy
+- red-teaming
 - enterprise-ai
-- regulated-industries
 pretty_name: DataFramer
 license: other
+
 ---
 
 # DataFramer
 
-**Generate, anonymize, and simulate reality-grounded, diverse datasets from your own data for testing, evals, and fine-tuning ML/AI models.**
+**AI Workflow Intelligence for accurate, high-value AI workflows.**
 
-DataFramer helps AI teams take their own data further — creating realistic, privacy-safe datasets for **testing, evaluation, and post-training** without exposing sensitive production records.
+DataFramer connects **AI traces, user behavior, workflow events, and expert judgment** in one continuous improvement loop.
 
-**DataFramer works from your data**, adding diversity while preserving the **structure, distributions, and constraints** your models depend on.
+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 get blocked because:
+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.
 
-- **their seed data isn’t enough**  
-  Generate diverse, scaled datasets without starting from scratch.
+* **important failures are difficult to find at scale**
+  Discover recurring behaviors, emerging patterns, and meaningful deviations across production traces.
 
-- **their real data is off-limits**  
-  Anonymize sensitive records while keeping structure intact.
+* **expert feedback is difficult to operationalize**
+  Turn human reviews, corrections, and grading standards into reusable rubrics and ground truth.
 
-- **their data doesn’t cover what models will face in production**  
-  Simulate edge cases, rare scenarios, and real-world variation missing from existing samples.
+* **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 seed-based workflow for enterprise AI data readiness:
+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.
 
-1. **Seed input** from manual samples or production data  
-2. **Anonymize** sensitive records when needed  
-3. **Analyze** schema, structure, distributions, and patterns  
-4. **Configure** variation, volume, edge cases, and format mix  
-5. **Generate** realistic datasets across complex formats  
-6. **Use** the outputs for model evaluation, testing, and fine-tuning
+* **Human reviews and rubrics**
+  Define grading standards, assign traces to reviewers, capture corrections consistently, and create reusable ground truth.
 
-## Built for real enterprise data
+* **Automated judges**
+  Build and calibrate judges using the same standards applied by human reviewers.
 
-DataFramer works with **any textual dataset — any format, any domain, any complexity**, including:
+* **Datasets and evaluations**
+  Create benchmark and regression datasets from reviewed traces, production examples, and generated scenarios.
 
-- long-form documents and PDFs
-- structured and semi-structured records
-- nested and hierarchical data
-- multi-file workflows
-- high-variability business inputs
+* **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
 
-- **LLM and AI evaluations**  
-  Build stronger eval datasets with better coverage across common, rare, and edge-case scenarios.
+* **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.
 
-- **Privacy-safe testing**  
-  Use realistic datasets for testing and iteration without exposing sensitive production data.
+* **Red teaming and adversarial testing**
+  Generate targeted prompts and scenarios for testing models and agents under challenging conditions.
 
-- **Anonymization for AI workflows**  
-  Transform restricted real-world data into safe seed inputs for downstream generation and evaluation.
+## Developer access
 
-- **Fine-tuning and dataset expansion**  
-  Extend sparse datasets with more realistic variation while preserving fidelity to source patterns.
+Programmatic access is available through the DataFramer API, Python SDK, and MCP server for:
 
-## Enterprise-ready
+* datasets
+* generation specifications
+* synthetic data generation
+* anonymization workflows
+* evaluations
 
-Built for teams in regulated and data-sensitive environments.  
-**Your data never has to leave.**
+Learn more at **https://www.dataframer.ai**
 
-Learn more at **https://www.dataframer.ai**
+Read the documentation at **https://docs.dataframer.ai**