DataLang

Teams use DataLang to turn everyday questions into instant answers pulled from the systems they already rely on. A typical setup starts by linking the places where information lives—such as a Postgres database, a company site, spreadsheets, Notion pages, or uploaded files—then shaping what should be searchable through SQL-based data views. This keeps sensitive fields out of reach and ensures the assistant responds with the right slice of information.

In practice, DataLang is used to handle repeat requests and reduce manual lookup work. Support groups connect docs and past resources so agents can ask for troubleshooting steps, policy details, or product specs without hunting through folders. Operations teams point it at internal pages and shared tables to answer questions like status, ownership, or recent changes. Analysts connect databases and use plain-language prompts to explore metrics, pull summaries, and validate assumptions while keeping the underlying queries consistent.

Once the assistant is configured, it can be delivered where people actually work. Some teams share a link for quick access, others embed it into a website for visitors, and some publish it so it can be used inside GPT-compatible surfaces. The outcome is a repeatable workflow: connect sources, define views, tune the assistant’s scope, then deploy it so users can ask and get grounded responses based on approved data. For account or support requests, use the Contact page: https://datalang.io/contact