Business/Customer Intelligence & Data Science

TextQL raises $4.1M to use AI for automating data science lifecycle

The enterprise AI platform integrates with every layer of the modern data stack to replicate the interactions you would have with a senior data scientist that's always available

TextQL, a startup building an AI data analyst that connects to your business intelligence (BI) tools, semantic layers, and existing documentation, announced today that it raised $4.1 million across pre-seed and seed rounds that was co-led by Neo and DCM. Other participants in the round include VC firms Unshackled Ventures, Worklife Ventures, PageOne Ventures, FirstHand Ventures, & Indicator Fund. They are also joined by angel investors like dbt CEO Tristan Handy; former Notion founder Chris Prucha; Tackle executives Dillon WoodsJohn Jahnke, and Brian Denker; Observe CTO Matt Kraning; and Braze CPO and SVP of Growth, Kevin Wang and Spencer Burke.

“With the rise of data came another issue: non-technical workers were not given the tools to find the answers they needed in the data, until TextQL,” says Hurst Lin, General Partner at DCM, and #40 on the Midas List. “We’re excited about the work that TextQL is doing to help non-technical workers across various industries and organizations access the critical data they need to make informed business decisions, and we see TextQL as the solution to free data analysts from the monotony of pulling data requests with their virtual data analyst.”

TextQL’s mission is to fully automate every single step in the lifecycle of data. To do this, TextQL replicates the experience of working with a human data analyst. Its analyst, called Ana, integrates across the entire data stack. Ana connects to your BI tools and points users to existing dashboards when a question has already been asked. It documents your semantic layer and can alternate to write semantic layer code when needed. It’s able to do this by referencing documentation from enterprise data catalogs, like Alation, as well as notes in your Confluence or Google Drive.

“Every conversation about self-service analytics with data practitioners starts with an eye roll. They’ve been sold disappointing self-service products for the past 15 years that are always ready tomorrow, after another new BI tool or a bit more data modeling,” said Ethan Ding, CEO and co-founder of TextQL. “TextQL is built to mimic the hierarchy of responses a human analyst goes through – operating across your data stack without any migration. It browses your BI tools, queries your semantic layer, reads your dbt documents, and asks for help when it doesn’t know what to do. This is the hardest unsolved problem at the intersection of enterprise data, AI and user experience – but the difficulty of the problem has attracted a ton of really incredible people to our team.”

Despite its challenges, TextQL is already partnering with organizations with tens of thousands of employees in industries like media, bio and life sciences, manufacturing and financial services. Most notably, TextQL has recently announced participation in the NBA Launchpad program as an accelerated way to bring the NBA’s data platform on an AI-native path.

This round of funding will be used to expand the TextQL team, which is currently focused on hiring software engineers and forward deployed engineers to join their team of ex-founders to work across data engineering and language model training. With this expanded team, they expect to have the capacity to onboard ten more companies in the next quarter.

“I’ve been blown away by TextQL’s bold vision and Ethan’s technical leadership,” said Ali Partovi, CEO of Neo. “The world of data is at the brink of a seismic shift as AI relieves us from manually organizing database tables and writing SQL. TextQL will unlock a massive surge in data usage where anybody in an org can access data and get insights just by asking questions instead of waiting for the engineers to construct queries.”

The latest features from TextQL’s Ana platform encompass 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 Python-proficient language model that’s HIPAA and SOC 2 compliant; and a Slack integration for on-the-go team communication.

In the coming months, TextQL anticipates the launch of key technology partnerships with their preferred semantic layer, business intelligence platform, and data catalogs.

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