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| title: README | |
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| # 🧠 DataArcTech | |
| **Grounded in context graphs. Empowered by synthetic data.** | |
| [🌐 dataarctech.com](https://www.dataarctech.com) | |
| --- | |
| ## 🚀 About Us | |
| **DataArcTech** bridges enterprise knowledge and synthetic data to build **GenAI-ready infrastructures**. | |
| Our core framework — **Context Graph + Synthetic Data** — enables organizations to represent, augment, and operationalize knowledge for intelligent systems. | |
| We focus on **AI compliance, contextual reasoning**, and **data synthesis technologies** that empower enterprises to transition from static data management to adaptive, knowledge-driven AI. | |
| --- | |
| ## 🧩 What We Do | |
| | Area | Description | | |
| |------|--------------| | |
| | **Context Graph (SoG / Graph Synthesis)** | A structured framework that connects data, context, and reasoning for LLM readiness. | | |
| | **Synthetic Data Generation & Augmentation** | Produces high-quality, domain-specific datasets when real data is limited, sensitive, or unavailable. | | |
| | **End-to-End AI Lifecycle Support** | From data synthesis and curation to model training and fine-tuning. | | |
| | **AI Governance & Compliance** | Aligning intelligent systems with enterprise-level data governance and regulatory standards. | | |
| --- | |
| ## 🧪 Research & Open Source | |
| We contribute to the GenAI research ecosystem through open projects and publications: | |
| - **[ToG-2 (Think-on-Graph 2.0)](https://github.com/IDEA-FinAI/ToG-2)** – Knowledge-guided reasoning and retrieval for LLMs | |
| - **[JudgeAgent](https://arxiv.org/html/2509.02097v3)** – An agent framework for automated evaluation of conversational and generative models | |
| - **[SQL-R1](https://www.github-zh.com/projects/981865038-sql-r1)** – Reinforcement learning for natural language to SQL translation | |
| - **[Awesome-FinLLMs](https://github.com/DataArcTech/Awesome-FinLLMs)** – A curated list of LLMs and datasets for financial AI research | |
| --- | |
| ## 💼 Industry Applications | |
| Our technology powers domain adaptation and synthetic data generation in sectors such as: | |
| - **Financial Services** | |
| - **Manufacturing** | |
| - **Healthcare** | |
| - **Cloud Computing** | |
| - **Education & Research** | |
| We help enterprises build **domain-specialized LLMs** by combining our hybrid synthetic datasets with proprietary client data — achieving safe, contextual, and compliant AI transformation. | |
| --- | |
| ## 🌍 Our Vision | |
| To make enterprise AI **contextually intelligent**, **data-secure**, and **governance-ready** — | |
| where every knowledge graph and dataset contributes to a more explainable, adaptive, and trustworthy AI ecosystem. | |
| --- | |
| ## 🤝 Collaboration | |
| We’re open to collaboration on: | |
| - Dataset and model sharing | |
| - LLM fine-tuning and evaluation | |
| - Context graph / knowledge integration research | |
| 💬 Reach out via [dataarctech.com](https://www.dataarctech.com) or connect through our [GitHub organization](https://github.com/DataArcTech). | |