URTech provides an on-premises AI agent platform that learns directly from a company's own data to generate SQL queries, perform analyses, and suggest new research questions. The system operates locally within a user's data silo, using specialized agents for query generation, analysis, and hypothesis development while keeping a human in the loop. It comes pre-trained in SQL best practices and builds a repository of successful queries over time to improve accuracy and efficiency.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations often struggle to extract meaningful insights from their data because it requires deep SQL expertise and an understanding of complex, proprietary data schemas. Traditional analytics tools are either too rigid to handle ad-hoc questions or require significant manual effort to write and debug queries, limiting data accessibility for non-technical stakeholders.
Solution
URTech provides an agentic-based AI system that runs locally within a company's data silo, learning the specific structure, relationships, and meaning behind the user's data over time. The platform consists of three collaborating agents: a Query Generator that writes and refines SQL queries based on natural language questions, an Analysis Engine that transforms query results into summaries and visualizations, and an Interrogation Driver that proposes new hypotheses and explores relationships between variables. The system comes pre-trained in SQL best practices and basic data analytics, then fine-tunes itself by storing successful queries and their outcomes for future reference. An overseer agent coordinates the workflow and keeps a human in the loop to ensure the system stays aligned with user priorities and research goals.
Target Audience
Primary users are data analysts, business intelligence teams, and research professionals who work with complex, proprietary datasets and need to generate queries and explore data without writing extensive SQL code manually.
Features
- Pre-trained local language model with SQL best practices and standard data analytic techniques
- Query Generator that learns schema names, table relationships, and variable meanings through interaction
- Self-maintaining repository of successful queries that improves accuracy and efficiency over time
- Analysis Engine that converts query outputs into data summaries and visualizations
- Interrogation Driver that suggests unexplored variable relationships and generates follow-up hypotheses
- Overseer agent that coordinates agent collaboration and maintains human oversight
- Runs entirely on-premises within the user's network, ensuring data never leaves the local environment