AI-startup-TextQL-raises-$4.1-million-to-transform-data-analysis-with-Virtual-Analyst

AI startup TextQL raises $4.1 million to transform data analysis with Virtual Analyst

AI startup TextQL raises $4.1 million to transform data analysis with Virtual Analyst

The startup TextQL has raised $4.1 million in pre-seed and seed rounds. It makes an AI data analyst that works with business intelligence (BI) tools, semantic layers, and existing documentation. Other investors in the round were Unshackled Ventures, Worklife Ventures, PageOne Ventures, FirstHand Ventures, and Indicator Fund. Neo and DCM led the round together. TextQL’s goal is to fully automate every step of the data lifecycle by making working with a human data analyst feel like working with a computer.

Ana is the company analyst who connects to BI tools and shows users existing dashboards when a question has already been asked. There is integration across the whole data stack. It keeps track of the semantic layer and can write semantic layer code when needed. In order to do this, Ana can look at documentation from enterprise data catalogs such as Alation and notes in Confluence or Google Drive.

TextQL wants to make it possible for a huge increase in the use of data so that anyone in an organization can access data and learn from it by asking questions instead of having to wait for engineers to make queries. The latest additions to TextQL’s Ana platform include

  • A dynamic metadata engine for indexing from Notion, Confluence, Google Drive, and Microsoft Office
  • Business intelligence compatibility with Tableau, Looker, and PowerBI
  • An AI-boosted semantic layer for dbt, Cube, and LookML
  • A language model that works with Python and is HIPAA and SOC 2 compliant
  • Slack integration for team communication while on the go. 

TextQL’s goal is to make data analysis easier for everyone and more automated by creating generative AI-powered data discovery and analytics for the modern data stack. TextQL is taking over the daily tasks of human data analysts by automating them. These tasks include pulling dashboards and answering data questions directly from a company’s data warehouse. This means that business teams can get answers about their company’s data in seconds instead of days.

TextQL is also interested in beginning collaborations with significant technology companies within the next few months. The company already collaborates with other organizations in a variety of fields, including the media, healthcare, and financial services. Through the use of its funding, the TextQL team will be expanded, with an emphasis placed on engineering talent, and ten additional companies will be brought on board during the second quarter of the year.

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