Update README.md

README.md

@@ -18,60 +18,64 @@ license: other
 
 **Your AI teams are ready. Their data isn't.**
 
-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}
+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.
+
+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}
+- 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  
 
 ## Best-fit use cases
 
-- **LLM and AI evals**  
-  Generate eval sets with better coverage across normal cases, edge cases, rare events, and boundary conditions.
+**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.
+**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.
+**Testing 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}
+**Model training and fine-tuning**  
+Expand sparse datasets and improve diversity while preserving the structure, constraints, and relationships models depend on.
 
 ## 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}
+- 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  
 
 ## 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}
+- financial services  
+- insurance  
+- healthcare  
+- other enterprise environments with strict privacy and governance requirements  
 
 ## 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}
+- 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**
 
 ## Learn more
 
-Website: `https://www.dataframer.ai`
-Docs: `https://docs.dataframer.ai`
+Website: https://www.dataframer.ai  
+Docs: https://docs.dataframer.ai