Update README.md

README.md

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-Edit this `README.md` markdown file to author your organization card.
+---
+license: proprietary
+tags:
+- synthetic-data
+- long-form-document-generation
+- data-anonymization
+- data-augmentation
+- data-transformation
+- data-simulation
+- tabular-data
+- text-generation
+- sql-generation
+- privacy
+- evaluation
+- enterprise-ai
+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  
+
+- **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.
+
+---
+
+For more information: https://www.dataframer.ai