Why dhino exists

We have spent years watching AI fail on enterprise data. Not because the AI is bad. Because it is being asked to do something it was never designed for. Large language models are built to understand and generate text. They are not built to query databases accurately.

The industry response has been text-to-SQL. Let AI write database queries. It sounds elegant. It works great in demos. And it fails catastrophically on real enterprise data. Ambiguous column names, complex relationships, business logic that lives in people's heads. The AI guesses. Sometimes it guesses right. Often it does not.

We saw a different path. What if we separated understanding from execution? Let AI do what it does best: interpret natural language, understand intent, grasp context. Then hand off to deterministic systems that do what they do best: execute precisely, every time, without guessing.

That insight became dhino. A data layer that sits between AI and your enterprise systems. Templates that capture business logic. Governance that protects sensitive data. Results you can actually trust for real business decisions.

We are building for a future where AI on structured data just works. Where business users get answers without waiting for IT. Where data teams have control without being bottlenecks. Where reliable AI on enterprise data is the norm, not the exception.