Update README

README.md

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 ---
+language:
+- en
+
 license: cc-by-4.0
+
+pretty_name: BeautyCommerceOS
+
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series-forecasting
+- question-answering
+- text-generation
+
+tags:
+- synthetic-data
+- ai-agents
+- llm-evaluation
+- autonomous-agents
+- tool-use
+- causal-reasoning
+- decision-intelligence
+- analytics-engineering
+- data-engineering
+- business-intelligence
+- data-warehouse
+- enterprise-ai
+- ecommerce
+- attribution-modeling
+- experimentation
+- supply-chain
+- finance
+- parquet
+
+size_categories:
+- 1M<n<10M
 ---
+
+# 💄 BeautyCommerceOS  
+## Autonomous Analytics & Enterprise Reasoning Benchmark
+
+BeautyCommerceOS is a large-scale synthetic enterprise data warehouse designed to benchmark **autonomous AI agents and analytics systems operating in realistic business environments**.
+
+It simulates the full lifecycle of a modern global beauty ecommerce company — from user behavior to financial reconciliation — including the ambiguity, inconsistency, and cross-functional complexity found in real enterprises.
+
+---
+
+# 🧠 Why this dataset exists
+
+Most datasets teach *analysis on clean tables*.
+
+BeautyCommerceOS teaches something harder:
+
+> **Cross-domain reasoning across a messy, real-world enterprise.**
+
+It is designed to evaluate whether AI systems can reason across:
+
+- conflicting KPIs across departments  
+- delayed revenue recognition  
+- attribution uncertainty across marketing channels  
+- inventory and supply chain mismatches  
+- finance vs marketing reporting divergence  
+- experimentation interference effects  
+
+This makes it suitable for evaluating **LLM agents, autonomous analysts, and decision intelligence systems**.
+
+---
+
+# 🏗️ Dataset Structure
+
+The dataset follows a modern **medallion warehouse architecture**:
+
+## 🟤 Bronze Layer (Raw Behavioral Data)
+- sessions
+- clickstream events
+- anonymous users
+
+## ⚪ Silver Layer (Conformed Dimensions)
+- products
+- SKUs
+- brands
+- suppliers
+- warehouses
+- identity mapping
+
+## 🟡 Gold Layer (Business & Financial Truth)
+- orders
+- order_items
+- payments
+- refunds
+- campaigns
+- attribution
+- inventory
+- shipments
+- invoices
+- profit & loss (P&L)
+
+---
+
+# 📊 Key Capabilities
+
+BeautyCommerceOS supports evaluation of:
+
+## 🧠 AI Agent Reasoning
+- multi-step business question answering
+- cross-table joins across domains
+- causal inference under noisy signals
+
+## 📈 Marketing Intelligence
+- attribution modeling
+- ROAS vs profit divergence
+- influencer impact analysis
+- channel cannibalization effects
+
+## 🚚 Supply Chain Analytics
+- stockout impact analysis
+- fulfillment delay tracking
+- warehouse performance comparison
+
+## 💰 Financial Reconciliation
+- revenue recognition delays
+- finance vs marketing mismatches
+- margin decomposition
+
+## 🧪 Experimentation Analysis
+- A/B test evaluation
+- treatment contamination
+- causal uplift estimation
+
+---
+
+# ⚠️ Realism & Complexity
+
+Unlike traditional synthetic datasets, BeautyCommerceOS intentionally includes:
+
+- inconsistent attribution signals
+- delayed financial reconciliation
+- missing or noisy event data
+- KPI definition conflicts across teams
+- operational distortions across systems
+
+These are included to reflect real enterprise environments and enable robust evaluation of reasoning systems.
+
+---
+
+# 🔍 Example Evaluation Tasks
+
+BeautyCommerceOS can be used to evaluate systems on questions such as:
+
+### 📈 Business Performance
+- Why did revenue increase while profit declined?
+- Which channels generate the lowest long-term customer value?
+
+### 🧠 Attribution & Marketing
+- Which attribution model best explains observed revenue?
+- Are influencer campaigns profitable after refunds and returns?
+
+### 🚚 Operations
+- Which warehouses contribute most to fulfillment delays?
+- How do stockouts impact downstream revenue loss?
+
+### 💰 Finance
+- Why do finance and marketing report different revenue figures?
+- What is the true margin after operational adjustments?
+
+### 🤖 AI Agent Benchmarking
+- Can an autonomous agent reconcile conflicting KPIs across systems?
+- Can it identify root causes across marketing, finance, and logistics?
+
+---
+
+# 🚀 Intended Use
+
+This dataset is designed for:
+
+- autonomous agent evaluation
+- LLM reasoning benchmarks
+- analytics engineering practice
+- causal inference research
+- BI system testing
+- data warehouse simulation
+- decision intelligence systems
+
+---
+
+# 🚫 Not Intended For
+
+- real-world financial forecasting
+- production decision-making
+- regulatory reporting
+- personal or sensitive data usage
+
+---
+
+# 🔒 Synthetic Data Statement
+
+All data in this repository is fully synthetic and generated programmatically.
+
+No real users, transactions, or personally identifiable information are included.
+
+---
+
+# 📦 Format
+
+- Columnar storage: Parquet
+- Architecture: Medallion (Bronze / Silver / Gold)
+- Structure: Partitioned data warehouse
+- Size: < 10 GB total
+
+---
+
+# 📜 License
+
+Licensed under CC BY 4.0.
+
+You are free to:
+- use
+- modify
+- redistribute
+- build upon
+
+with attribution.
+
+---
+
+# 🏁 Vision
+
+BeautyCommerceOS bridges the gap between:
+
+- clean academic datasets  
+- and real-world enterprise complexity  
+
+It is designed to test whether modern AI systems can move beyond simple data analysis into **true enterprise-level reasoning under ambiguity**.