Kitchener-Waterloo-based runQL has revealed $1.6 million CAD in pre-seed funding as it looks to improve how data and development teams document and manage queries.
The round, which consisted of simple agreements for future equity (SAFE) and closed in late May, was led by Mistral Venture Partners with participation from MaRS Investment Accelerator Fund, Inovia Capital, University of Waterloo Velocity Fund, Philip Rathle, CTO of Neo4j, and other Waterloo-based founders that the company declined to disclose to BetaKit.
“We believe that we’ll be the central repository for all queries used in an organization.”
runQL has developed a query platform aimed to help data professionals and developers write queries faster, and eliminate the chaos around saving, documenting, and dealing with multiple versions of queries. According to CEO and founder Rob Darling, the startup is looking to address a long-standing pain point for analysts and developers.
“Data teams are growing in organizations, and that’s put more pressure on them to have newer tools that are updated [and] that allow them to collaborate easier and make discoverability of existing queries better,” Darling told BetaKit in an interview.
Darling has a good deal of experience in this space. Prior to launching runQL, he was the CTO of Kitchener-Waterloo, Ont. innovation hub Communitech, and ran ecosystem data startup Briefed.in (Disclosure: BetaKit was previously a Briefed.in media partner), which was acquired by Communitech in 2022.
He also previously founded now-exited customer relationship management startup LaunchSpot.io, worked as a consultant to startups, and brought business intelligence solutions to large enterprises. It was, in part, Darling’s resumé that drew Mistral partner Raif Barbaros to the startup.
“Rob is a highly respected and successful founder,” Barbaros told BetaKit. “Beyond his impressive track record, he has built a reputation as an exceptional leader and has garnered deep insights into RunQL’s target market through his years as a technical founder.”
RELATED: Communitech acquires Briefed.in to add data-backed insights to its repertoire
In around 2010, when working on enterprise software architecture with insurance giant Manulife, Darling noticed problems with how queries that are handled by data teams were stored and managed. These problems re-emerged in his work at Communitech, and through his consulting work with startups and other businesses.
For data analysts, a query is a specific request for information or data from a database. By writing a query, typically using the programming language Structured Query Language (SQL), analysts can extract, filter, and manipulate large datasets to obtain relevant information to answer a question or generate insights.
The problem, Darling said, is that these queries need to be documented and managed. He conducted numerous customer problem interviews with data analysts and business leaders, finding that analysts spend up to two hours a day just on documentation. Many of them used tools like Google Drive, but those documents were often not updated to account for changes in the data.
“You can have perfect data, but if your queries are wrong, the business is actually going to get the wrong answers,” Darling added.
Git, an open-source version control system that helps teams manage and track changes to code or text files collaboratively, is also built more for developer workflows, Darling added, and isn’t tailored to the unique needs of data analysts. So, he set out to build a platform that could help data analysts better document and manage queries.
runQL’s platform streamlines query writing for data analysts by providing automatic suggestions based on previously saved queries, allowing users to start from existing templates instead of typing from scratch.
“The best way to think about this is like Google search,” Darling explained. “When you’re typing in Google Search, it’s giving you suggestions on the next word that makes sense based on your search. We do the exact same thing, but for queries.”
Analysts can save and make versions of queries, instantly share them with teammates, and these queries are automatically documented. Certified queries can be marked as the “gold standard,” with team discussions and reviews to ensure accuracy, giving users confidence in using trusted queries. The setup aims to reduce what Darling described as “painful admin work.”
RELATED: DevOps platform Codezero raises $3.5-million seed round
runQL launched its minimum viable product at the end of last year, and ran a beta in January 2024. Now fully launched, Darling said runQL has four business customers that are very different from one another from both a size and industry perspective, as well as a number of users who are signing on to use the platform. So far, the industries include banking, life sciences, geospatial data, and e-commerce, and they range in size from just 100 employees to over 10,000.
Darling claimed runQL can save teams two hours a day in manual work, which he said equates to roughly $3,000 a month in productivity savings per data analyst, or $36,000 a year.
“Data teams constitute approximately two to three percent of the workforce in most companies across various industries,” Barbaros added. “This represents a significant target market and presents a substantial opportunity for growth.”
The CEO declined to disclose how much of the startup’s funding has been deployed, but noted the funds are being put towards sales and marketing as well as product development. The startup’s team sits at six people, including two co-op students from the University of Waterloo, and Darling plans to keep the team lean for the time being.
Darling envisions runQL’s platform as a way to give users accurate, data analyst-approved answers to their questions without requiring a data analyst’s direct involvement each time.
He said by capturing metadata around queries and organizing data analyst workflows, the platform could allow business users to access reliable answers to frequently asked questions. He noted that unlike text-to-SQL solutions, which rely on large-language models to generate queries but have low accuracy rates, runQL offers high-confidence answers by mapping user questions to pre-approved, analyst-created queries.
“We believe that we’ll be the central repository for all queries used in an organization, and our system will be the lynchpin to all other systems that query data,” Darling added.
Feature image courtesy runQL.