TextQL aims to add AI-powered intelligence on top of business data
Mark Hay and Ethan Ding want to make every corporate decision a data-driven one. Ambitious? For certain. But the two engineers, who met a few years ago during the pandemic, are nothing if not optimistic.
Hay and Ding are the co-founders of TextQL, a platform that connects a company’s existing data stack to large language models along the lines of OpenAI’s ChatGPT and GPT-4. The idea, they say, is to give business teams the ability to ask questions of their data on-demand, leveraging tooling that — in Hay’s words — “understands their teams’ ‘nouns’ and semantics.”
“Data leaders have spent 15 years being sold a false promise … Half the Fortune 500 chief data officers are allergic to the word ‘self service’ at this point,” Hay, TextQL’s CTO, told TechCrunch in an email interview. “Their 400,000 data scientists are spending 40% or more of their time pulling one-off data requests, and their business teams are using words that are represented differently in their databases, resulting in months of lost productivity arguing over numbers.”
Hay, previously an engineer on Facebook’s machine learning team, and Ding, an ex-member of Bessemer Venture Partners’ data team (and a fan of gardening metaphors), thought they could devise a better solution.
In 2022, they launched their attempt in TextQL, which uses a data model to map a company’s database to the “nouns” representing a customer’s business in their language — e.g. words like “order,” “item,” “dealer,” “SKU,” “inventory” and so on.
TextQL connects to business intelligence tools and points users to existing dashboards when a question has already been asked. It’s able to reference documentation from enterprise data catalogs such as Alation, Hay says, as well as notes in platforms like Confluence or Google Drive.
Concretely, this enables TextQL users to ask questions of a chatbot such as “Can you show me a list of orders that were very late?” and “Calculate the distribution centers with the highest concentration?” Beyond answering questions, TextQL — via an automation component — can take certain actions, for example sending an email to managers about specified data.
“In an economic environment where everyone’s trying to do more with less, we’re able to give enterprise operators superpowers in one platform,” Hay said.
Hay — which sees TextQL competing against vendors like Palantir and C3.ai — says that TextQL has half a dozen customers across healthcare, bio and life sciences, financial services, manufacturing and media presently. Annual recurring revenue is in the “six figures,” he claims, giving TextQL “several years” of runway.
“The slowdown hasn’t affected us as much, if anything — companies are excited about our software since it can help them do more with their lower headcounts,” Hay said. “Our entire team consists of previously venture-backed veteran founders — which is talent that’d be pretty hard to pick up outside of this environment.”
On the subject of venture backing, TextQL, which has a ~10-person team, has raised $4.1 million across pre-seed and seed round sco-led by Neo and DCM with participation from Unshackled Ventures, Worklife Ventures, PageOne Ventures, FirstHand Ventures and Indicator Fund.
