Update README.md

README.md

@@ -2,18 +2,76 @@
 tags:
 - synthetic-data
 - anonymization
-- augmentation
-- transformation
-- simulation
+- data-augmentation
+- data-simulation
+- evaluation
+- testing
 - privacy
 - enterprise
-pretty_name: DataFramer AI
+- regulated-industries
+- llm
+pretty_name: DataFramer
+license: other
 ---
 
-# DataFramer AI
+# DataFramer
 
-DataFramer AI is a data platform for **synthetic data generation**, **anonymization**, **augmentation/transformation**, **expansion** and **simulation**—built to help teams develop and evaluate AI systems without exposing sensitive production data.
+**Your AI teams are ready. Their data isn't.**
 
-**Common users:** financial services & banking, insurance, healthcare, energy, and other regulated industries.
+DataFramer helps teams **generate, anonymize, augment, and simulate** realistic datasets for **testing, evaluations, and fine-tuning** of ML and AI systems—without relying on sensitive production data. It is built for **complex, enterprise data**: multi-file, multi-format, structured, unstructured, nested, and long-context workflows. :contentReference[oaicite:1]{index=1}
 
-Learn more: https://www.dataframer.ai
+## What DataFramer is for
+
+DataFramer is designed for teams that need to:
+
+- create **synthetic datasets** from seed examples or from scratch
+- **anonymize sensitive data** while preserving structure and task-relevant fidelity
+- **augment and transform** existing datasets for broader coverage
+- **simulate edge cases, rare events, and scenarios** absent from historical data
+- build **evaluation datasets** that better reflect real-world model behavior :contentReference[oaicite:2]{index=2}
+
+## Best-fit use cases
+
+- **LLM and AI evals**  
+  Generate eval sets with better coverage across normal cases, edge cases, rare events, and boundary conditions.
+
+- **Privacy-safe experimentation**  
+  Work with compliant alternatives when production data is restricted by privacy, security, or governance requirements.
+
+- **Testing for complex workflows**  
+  Create realistic data for claims, fraud, underwriting, patient workflows, document pipelines, and other enterprise systems.
+
+- **Model training and fine-tuning**  
+  Expand sparse datasets and improve diversity while preserving the structure, constraints, and relationships models depend on. :contentReference[oaicite:3]{index=3}
+
+## Built for complex enterprise data
+
+DataFramer supports workflows involving:
+
+- long-form documents and PDFs
+- JSON, XML, CSV, Parquet, and other structured formats
+- nested and hierarchical records
+- multi-file and high-token samples
+- conversational, document, and domain-specific datasets :contentReference[oaicite:4]{index=4}
+
+## Who uses it
+
+DataFramer is especially relevant for teams in **regulated and data-sensitive industries**, including:
+
+- financial services
+- insurance
+- healthcare
+- other enterprise environments with strict privacy, governance, and evaluation requirements :contentReference[oaicite:5]{index=5}
+
+## Why teams use DataFramer
+
+- preserve **structure and constraints**, not just surface realism
+- generate data for **testing, evals, and fine-tuning**
+- improve **edge-case coverage**
+- reduce dependence on restricted production data
+- work with **complex, real-world data formats** that generic tools struggle to handle :contentReference[oaicite:6]{index=6}
+
+## Learn more
+
+Website: `https://www.dataframer.ai`
+Docs: `https://docs.dataframer.ai`