Update README.md

README.md

@@ -1,81 +1,73 @@
 ---
 tags:
-- synthetic-data
-- anonymization
-- data-augmentation
-- data-simulation
 - evaluation
 - testing
 - privacy
+- llm
 - enterprise
+- anonymization
+- data-augmentation
+- simulation
 - regulated-industries
-- llm
+- insurance
 pretty_name: DataFramer
 license: other
 ---
 
 # DataFramer
 
-**Your AI teams are ready. Their data isn't.**
+**Realistic, privacy-safe data for AI testing, evals, and fine-tuning.**
 
-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.
+DataFramer helps AI teams create realistic, diverse datasets for **testing, evaluations, and fine-tuning** without exposing sensitive production data.
 
-It is built for **complex, enterprise data**: multi-file, multi-format, structured, unstructured, nested, and long-context workflows.
+Built for **complex enterprise workflows**, DataFramer supports document-heavy, multi-file, structured, and unstructured data so teams can validate AI systems against real-world variability, not just clean demo cases.
 
-Learn more: https://www.dataframer.ai
+## Why teams use DataFramer
 
-## What DataFramer is for
+AI projects often stall because the data needed for testing and evaluation is:
 
-DataFramer is designed for teams that need to:
+- too sensitive to use directly
+- too limited to cover edge cases
+- too messy to recreate by hand
+- too unrealistic when manually mocked
 
-- 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  
+DataFramer helps teams generate better test and eval data so models can be assessed against the kinds of variation they will actually face in production.
 
 ## 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.
+- **LLM and AI evaluations**  
+  Build eval datasets with stronger coverage across common cases, rare cases, and edge cases.
 
-**Testing complex workflows**  
-Create realistic data for claims, fraud, underwriting, patient workflows, document pipelines, and other enterprise systems.
+- **Privacy-safe testing**  
+  Work with realistic data for testing and iteration without exposing sensitive production records.
 
-**Model training and fine-tuning**  
-Expand sparse datasets and improve diversity while preserving the structure, constraints, and relationships models depend on.
+- **Complex workflow validation**  
+  Test systems that depend on long documents, multi-file inputs, nested structures, and business-specific constraints.
 
-## Built for complex enterprise data
+- **Fine-tuning and dataset expansion**  
+  Expand sparse datasets with more realistic variation while preserving the patterns your models depend on.
 
-DataFramer supports workflows involving:
+## Built for enterprise data
 
-- 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  
+DataFramer is designed for workflows involving:
 
-## Who uses it
+- long-form documents and PDFs
+- structured and semi-structured data
+- nested and hierarchical records
+- multi-file samples
+- high-variability real-world business inputs
 
-DataFramer is especially relevant for teams in **regulated and data-sensitive industries**, including:
+## Who it is for
 
-- financial services  
-- insurance  
-- healthcare  
-- other enterprise environments with strict privacy and governance requirements  
+DataFramer is especially useful for teams in **regulated and data-sensitive environments**, including:
 
-## 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**
+- insurance
+- financial services
+- healthcare
+- enterprise AI teams working with restricted or hard-to-access data
 
 ## Learn more
 
-Website: https://www.dataframer.ai  
-Docs: https://docs.dataframer.ai
+See product examples, use cases, and request access at:
+
+**https://www.dataframer.ai**