TextQL to make Big Data queryable without the complexity

TextQL joined the recent edition of The IT Press Tour in New-York city. The firm positions itself as a pragmatic response to one of enterprise data’s most persistent frustrations: extracting value from large, complex datasets without forcing every user to become a data engineer. The company has built its proposition around a simple idea—querying data should be as intuitive as writing a question - while still meeting the performance, scale, and governance demands of modern organizations.

At its core, TextQL provides a natural-language interface that allows users to ask questions about their data in plain English and receive structured, SQL-ready outputs. Instead of replacing SQL or existing analytics platforms, TextQL acts as a translation layer between human intent and technical execution. Business analysts, product managers, and operational teams can query data conversationally, while the system generates optimized queries that data teams can inspect, refine, or deploy directly.

What differentiates TextQL from earlier “chat with your data” attempts is its emphasis on reliability and enterprise-readiness. The platform is designed to understand database schemas, relationships, and constraints, reducing the risk of ambiguous or misleading queries. Rather than producing opaque answers, TextQL outputs transparent, auditable SQL, helping organizations maintain trust in the results and align with governance and compliance requirements.

The company is clearly targeting environments where data complexity has outpaced usability. As organizations accumulate data across warehouses, lakes, and operational systems, access often becomes bottlenecked by a small group of specialists. TextQL aims to relieve that pressure by democratizing access while keeping technical control in place. In practice, this means faster insights for non-technical teams and fewer ad hoc requests landing on data engineers’ desks.

TextQL also frames its technology as an accelerator rather than a disruption. It integrates with existing databases, BI tools, and workflows, allowing organizations to adopt it incrementally. This “fit-into-what-you-already-have” approach reflects an understanding of enterprise realities, where wholesale platform replacement is rarely an option. By working alongside established data stacks, TextQL positions itself as a productivity layer rather than yet another system to manage.

From a market perspective, TextQL sits at the intersection of analytics, AI-assisted development, and data accessibility. Its pitch resonates particularly in sectors where data-driven decision-making is widespread but unevenly distributed, such as finance, SaaS, retail, and operations-heavy enterprises. The value proposition is less about flashy AI outputs and more about shaving time, reducing friction, and lowering the barrier between questions and answers.

In an era where organizations are awash in data but still struggle to use it effectively, TextQL’s approach reflects a broader shift: success is no longer defined by how much data you store, but by how easily people can work with it. By translating human language into structured queries with precision and accountability, TextQL is betting that the future of analytics lies not in replacing experts, but in empowering everyone else to think - and ask questions - like one.