In the AI arms race, most companies are building agents that hallucinate answers to complex financial questions. Preql is taking the counterintuitive approach: they're building the data quality layer that makes those agents actually trustworthy. It's a bold bet on the unsexy foundation of the AI revolution.

"AI fails without context. Preql delivers the data quality and semantic foundation that makes AI agents accurate and trustworthy."

Preql's approach is fundamentally different from traditional data cleaning tools. Instead of just cleaning data, they're building a semantic layer that understands the meaning and context behind financial data. This allows AI agents to not just process numbers but understand what those numbers actually mean in business context. It's the difference between an AI that can calculate a ratio and one that can tell you what that ratio means for your company's financial health.

• Builds the semantic foundation for AI agents in finance
• 16-person team with $2.2M in revenue
• 42% direct traffic indicates strong brand recognition
• Focuses on data quality, not just data processing
• Early-stage but showing clear market validation