Update README.md

README.md

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 ---
-title: README
-emoji: 🏆
-colorFrom: blue
-colorTo: blue
-sdk: static
-pinned: false
----
-
----
-license: proprietary
 tags:
 - synthetic-data
-- long-form-document-generation
-- data-anonymization
-- data-augmentation
-- data-transformation
-- data-simulation
-- tabular-data
-- text-generation
-- sql-generation
+- anonymization
+- augmentation
+- transformation
+- simulation
 - privacy
-- evaluation
-- enterprise-ai
+- enterprise
 pretty_name: DataFramer AI
 ---
 
 # DataFramer AI
 
-**DataFramer AI** is an enterprise-grade data infrastructure platform for generating, anonymizing, augmenting, transforming, and simulating structured and unstructured datasets.
-
-It enables teams to create statistically realistic, privacy-safe, and regulation-ready datasets for machine learning, AI system evaluation, analytics validation, and QA testing — without exposing sensitive production data.
-
----
-
-## 🚀 Overview
-
-DataFramer supports four core capabilities:
-
-### 1️⃣ Synthetic Data Generation  
-Create entirely new datasets derived from seed samples while preserving:
-- Schema & structure  
-- Statistical distributions  
-- Cross-field dependencies  
-- Logical constraints  
-
-### 2️⃣ Data Anonymization  
-De-identify sensitive datasets while maintaining analytical utility.  
-Designed to reduce re-identification risk beyond simple masking or token replacement.
-
-### 3️⃣ Data Augmentation & Transformation  
-- Expand small datasets for ML training  
-- Rebalance skewed distributions  
-- Standardize, normalize, or reshape datasets  
-- Convert between formats (e.g., structured ↔ text-based representations)
-
-### 4️⃣ Simulation  
-Model rare events, edge cases, stress scenarios, and synthetic system behaviors for:
-- Risk modeling  
-- QA testing  
-- Failure analysis  
-- Scenario planning  
-
----
-
-## 🧠 Specification-Driven Architecture
-
-DataFramer uses a structured workflow:
-
-### Step 1: Seed Input  
-Upload representative samples (CSV, JSON, SQL pairs, text corpora, multi-file datasets).
-
-### Step 2: Specification Inference  
-The system infers:
-- Schema definitions  
-- Field distributions  
-- Conditional logic  
-- Constraints & dependencies  
-- Domain-specific patterns  
-
-This produces a **generation specification** — a transparent, editable blueprint.
-
-### Step 3: Controlled Output  
-Users generate large-scale datasets with:
-- Distribution controls  
-- Constraint validation  
-- Rare-event injection  
-- Bias mitigation adjustments  
-
-Specifications can be reviewed and modified before generation.
-
----
-
-## ✨ Key Features
-
-- Distribution-aware modeling  
-- Constraint & syntax validation (including SQL validation)  
-- Cross-field dependency preservation  
-- Rare-event and stress-case generation  
-- Bias and fairness tuning  
-- Multi-format support (tabular, JSON, text, SQL, multi-file corpora)  
-- Enterprise governance workflows  
-
----
-
-## 🏦 Industry Applications
-
-DataFramer is used across regulated and data-sensitive industries, including:
-
-- **Financial Services & Banking**  
-  - Risk model training  
-  - Fraud detection datasets  
-  - Synthetic transaction simulation  
-  - Regulatory testing  
-
-- **Insurance**  
-  - Claims simulation  
-  - Underwriting dataset generation  
-  - Rare-loss scenario modeling  
+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.
 
-- **Healthcare**  
-  - Privacy-safe patient data modeling  
-  - Clinical workflow simulation  
-  - Synthetic EHR datasets  
-
-- **Energy & Utilities**  
-  - Demand simulation  
-  - Infrastructure stress testing  
-  - Sensor data augmentation  
-
-- **Enterprise AI Teams (Cross-Industry)**  
-  - LLM evaluation datasets  
-  - Text-to-SQL benchmarks  
-  - QA & staging data  
-  - Model robustness testing  
-
----
-
-## 🔍 How It Differentiates
-
-| Capability | DataFramer | Prompt-Only LLMs | Basic Synthetic Tools |
-|------------|------------|------------------|-----------------------|
-| Full dataset generation | ✅ | ❌ | ✅ |
-| Statistical distribution modeling | ✅ | ❌ | Limited |
-| Editable specifications | ✅ | ❌ | Rare |
-| Anonymization workflows | ✅ | ❌ | Varies |
-| Data augmentation | ✅ | Manual | Limited |
-| Scenario simulation | ✅ | ❌ | Rare |
-| Governance & compliance focus | ✅ | ❌ | Limited |
-
-DataFramer is designed as **data infrastructure for AI systems**, not just a text generator.
-
----
-
-## 📦 Supported Data Types
-
-- CSV / tabular datasets  
-- Structured JSON  
-- Text corpora  
-- Text-to-SQL pairs  
-- Multi-file structured datasets  
-- Domain-custom schemas  
-
----
-
-## ⚖️ Privacy & Compliance
-
-DataFramer supports both:
-- Fully synthetic dataset generation  
-- Privacy-preserving anonymization workflows  
-
-This enables data sharing, testing, and AI development in regulated environments without exposing sensitive production records.
-
----
-
-## 👥 Intended Users
-
-- ML Engineers  
-- Data Engineers  
-- AI Evaluation Teams  
-- Risk & Compliance Teams  
-- QA & Testing Engineers  
-- Enterprise Innovation Teams  
-
----
-
-## ⚠️ Limitations
-
-- Synthetic data quality depends on representativeness of seed input.  
-- Highly domain-specific constraints may require manual specification tuning.  
-- Synthetic data should complement — not replace — real-world validation in high-risk deployments.
-
----
-
-## 📚 Citation
-
-If you use DataFramer AI in research or enterprise workflows, please cite appropriately according to your organization’s standards.
-
----
+**Common users:** financial services & banking, insurance, healthcare, energy, and other regulated industries.
 
-For more information: https://www.dataframer.ai
+Learn more: https://www.dataframer.ai