Non-technical users often struggle to access and analyze data, requiring them to rely on data analysts or navigate complex business intelligence tools. This dependence creates bottlenecks and delays in obtaining data-driven insights, hindering agility and decision-making speed. Furthermore, data documentation is often scattered and inconsistent, making it difficult for users to understand and trust the data.

TextQL provides a natural language interface to data, enabling users to ask questions and receive answers within familiar communication platforms like Slack and Teams. By indexing existing business intelligence tools and automating data cataloging, TextQL eliminates the need for redundant dashboard creation and centralizes data definitions. The platform leverages large language models (LLMs) to translate natural language queries into SQL or Python code, execute them against the data, and present the results in an easily understandable format. TextQL also incorporates guardrails and anonymization techniques to ensure data security and compliance.

Natural language query interface for data analysis within Slack, Teams, and other communication platforms

Integration with existing business intelligence systems to prevent dashboard sprawl

Automated data cataloging and documentation with verified links to data definitions

LLM-powered translation of natural language queries into SQL and Python

Secure and compliant deployments with configurable compliance standards

Fine-tuning of LLMs to meet specific team needs and improve accuracy

Data anonymization through the Incognito engine to protect sensitive information

TextQL targets business users, data analysts, and other professionals who need to access and analyze data quickly and easily, particularly those working in data-driven organizations.