In San Francisco, generative AI-powered data discovery and analytics tools firm TextQL has raised $4.1m in pre-seed and seed funding, with which to expand its team working on data engineering and language model training.
The firm aims ‘to fully automate every single step in the lifecycle of data’, employing a virtual analyst called Ana which integrates with a user firm’s data stack and connects with its Business Intelligence tools, pointing users to existing dashboards when a question has already been asked.
The software taps documentation from enterprise data catalogs and from notes in the company’s Confluence or Google Drive to develop and prep its own semantic layer code to supplement what it finds already running.
The funding rounds were co-led by Neo and DCM, supported by several other VC firms and a large number of angel investors.
Hurst Lin, General Partner at DCM comments: ‘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 it as the solution to free data analysts from the monotony of pulling data requests with their virtual data analyst’.
TextQL CEO Ethan Ding says the software ‘is built to mimic the hierarchy of responses a human analyst goes through… 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’.
He adds: ‘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’.
