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README.md

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-Edit this `README.md` markdown file to author your organization card.
+---
+
+# 🌍 Join Neuralk-AI's tabular universe!
+
+[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Neuralk-AI) 
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/company/neuralk-ai) 
+[![Twitter](https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white)](https://twitter.com/neuralk_ai) 
+[![Blog](https://img.shields.io/badge/Blog-FF6F61?style=for-the-badge&logo=internet&logoColor=white)](https://neuralk-ai.com/blog)
+[![Careers](https://img.shields.io/badge/Careers-0077B5?style=for-the-badge&logo=internet&logoColor=white)](https://neuralk-ai.com/careers)
+
+---
+
+# 💡 Bringing tabular models closer to industrial tasks
+
+Neuralk-AI builds the first Tabular Foundational Model for industrial tasks found in Commerce and beyond. 
+
+We created **TabBench**, the first benchmark focused on evaluating and advancing tabular models on real-world use cases and ML workflows that can be found in industries like Commerce, such as product categorization, deduplication, and more.
+
+**What TabBench currently supports (frequently updated!):**  
+  - Real-world use cases: product categorization, deduplication
+  - Easily evaluate your model or dataset for each use case thanks to a steamlined Workflow logic (load → vectorize → predict → evaluate)  
+  - Evaluation on both industrial datasets (private) & academic ones (OpenML)
+  - Classic ML & Tabular Foundation models: NICL, TabICL, TabPFNv2, XGBoost, CatBoost, LightGBM, MLP  
+  - Built on **Neuralk Foundry**, an open-source, modular framework to customize your own industrial workflows 
+
+---
+ 
+# 🚀 How to get started?
+
+Install TabBench with pip:
+
+```bash
+pip install tabbench
+```
+
+or directly clone the repository:
+
+```bash
+git clone https://github.com/Neuralk-AI/TabBench
+cd TabBench
+```
+
+Jump straight into our example notebooks to start exploring tabular models on industrial tasks:
+
+| File | Description                                             |
+----------|---------------------------------------------------------|
+| [1 - Getting Started with TabBench](https://github.com/Neuralk-AI/TabBench/blob/main/tutorials/1%20-%20Getting%20Started%20with%20TabBench.ipynb)    | Discover how TabBench works and train your first tabular model on a Product Categorization task.
+| [2 - Adding a local or internet dataset](https://github.com/Neuralk-AI/TabBench/blob/main/tutorials/2%20-%20Adding%20a%20local%20or%20internet%20dataset.ipynb) | How to add your own datasets for evaluation (local, downloadable, or OpenML).
+| [3 - Use a custom model](https://github.com/Neuralk-AI/TabBench/blob/main/tutorials/3%20-%20Use%20a%20custom%20model.ipynb) |  How to integrate a new model in TabBench and use it on different use cases.
+
+---
+
+For more information about TabBench, open-source code and tutorials, you can check our [Github Page](https://github.com/Neuralk-AI/TabBench/)
+
+---