DataLang

DataLang is the essential platform for transforming your complex, dispersed business data—from SQL databases and spreadsheets to Notion pages and website content—into powerful, custom AI chatbots. It solves the challenge of data accessibility by enabling natural language queries, allowing any user to gain instant, accurate insights without writing a single line of code. DataLang empowers organizations to quickly deploy highly intelligent assistants trained exclusively on their proprietary knowledge for immediate internal support or customer engagement.

Key Features

DataLang provides a robust framework that connects vast data landscapes and simplifies the deployment of conversational AI, ensuring your assistants are always knowledgeable and up-to-date.

🔌 Unified Data Connectivity

DataLang breaks down data silos by supporting a broad spectrum of data sources, including major relational databases (PostgreSQL, MySQL, SQL Server, Snowflake, Oracle), cloud services (Google Sheets, Notion, HubSpot), and various files (HTML, text). This extensive compatibility ensures your custom chatbot trains on a comprehensive, single source of truth, regardless of where your operational data resides.

💬 Natural Language Querying (Chat with Your Data)

Democratize data access by allowing users to interact with complex databases using simple, conversational language. Once your data sources are configured, users can ask high-level questions like, "Show me the sales data for the last quarter," or "What are the top-performing products this month?" The system instantly translates these requests into database queries, delivering quick, accurate insights without requiring SQL expertise.

⚙️ Automated Database-to-API Conversion

DataLang offers a unique feature to securely convert defined SQL scripts—known as Data Views—into instant, accessible API endpoints. This abstraction layer enhances security by keeping configuration details encrypted and off-limits. Crucially, it enables the connected GPT assistant to call these endpoints in real-time, ensuring data synchronization and currency with every interaction.

🚀 Flexible Deployment and Sharing

Deploy your custom assistant exactly where your users are with maximum flexibility. DataLang supports four primary sharing methods: a direct Public URL, an embeddable Chatbot Widget for your website, integration with the official GPT Store, and access via a dedicated API for custom application integration.

Use Cases

DataLang transforms how different teams interact with organizational knowledge and operational data, moving beyond static reports to dynamic, conversational insights.

1. Internal Knowledge Base Assistant

Connect corporate documentation, HR policies stored in files, and project details managed in Notion. Instead of searching through countless documents, employees can ask the DataLang assistant specific questions like, "What is the policy for remote work reimbursement?" or "Find the latest Q4 marketing strategy document," receiving precise, sourced answers instantly.

2. Real-Time E-commerce Customer Support

By linking your e-commerce database (e.g., MySQL or Redshift) and CRM (HubSpot), you can deploy a customer-facing chatbot widget. This assistant can handle complex, data-driven queries such as, "Check the status of my order 5678," or "What are the dimensions of the Model X laptop?" providing real-time, accurate responses that drastically reduce support ticket volume.

3. Streamlined Data Analysis for Non-Technical Users

Create specific Data Views that expose key performance indicators (KPIs) from your financial or sales databases. Leadership and marketing teams, who may not be proficient in SQL, can query critical business metrics conversationally: "Compare year-over-year revenue growth in the European market," simplifying reporting and accelerating decision-making cycles.

Why Choose DataLang?

Choosing DataLang means prioritizing efficiency, security, and integration, allowing you to focus on leveraging data insights rather than managing infrastructure.

• Rapid, Four-Step Workflow: DataLang minimizes setup time. The process is streamlined into four simple steps: Set up your Data Source, Add a Data View (scope the data), Chat with your data (train the GPT), and Share it (deploy). This speed enables immediate proof-of-concept and rapid iteration.

• Security Through Abstraction: Unlike solutions that expose raw database access, DataLang utilizes the Database-to-API feature. This ensures that sensitive connection strings remain encrypted and are only decrypted briefly before use, while the Data View allows you to expose only the necessary data scope, maintaining strict security control.

• Seamless GPT Ecosystem Integration: DataLang is specifically designed for the modern AI landscape, offering direct publishing capabilities to the GPT Store. This allows organizations to monetize their proprietary data models or distribute highly specialized assistants directly to users within the ChatGPT ecosystem.

Conclusion

DataLang is the definitive tool for unlocking organizational intelligence, making the creation and deployment of sophisticated, data-aware chatbots straightforward and secure. By connecting your data and utilizing conversational interfaces, you empower every user to find the answers they need instantly.