Planera

Analytics teams live between two bad options: either someone writes every query by hand and the workflow stays slow, or an AI tool hides the work behind a polished answer and forces users to trust a black box.

What I wanted to solve was not just query generation. I wanted a system that could shorten time-to-insight without removing the operator's ability to inspect what happened, understand where the answer came from, and step in when the model was wrong.

A basic NL-to-SQL demo looks convincing right up until the schema gets messy, the business logic has exceptions, or the user asks a question that requires changing grain, joining the right tables, and preserving the correct filters. At that point, plausible SQL is not the same thing as correct SQL.

In production, users need more than an answer box. They need validation signals, recovery paths, and enough visibility to tell whether a failure came from the prompt, the schema, or the model's assumptions. Without that, a single hallucination is enough to make the whole surface feel unsafe.

I did not frame Planera as a chatbot. I framed it as an inspectable analytics workspace where every query is a controlled system interaction with visible state transitions.

That changed the design philosophy. Instead of trying to expose private model reasoning, I focused on exposing the operational trail: what the system believed the question was, how it planned the work, what SQL it produced, what validations passed or failed, and how the final answer was synthesized.

The biggest shift was not that the system answered faster. It was that the interaction became safer to trust. Users could see the generated SQL, inspect validation signals, and understand the boundary between model assistance and verified execution.

That changes adoption dynamics. Instead of treating analytics AI like a novelty, Planera makes it easier to use the system as a controlled workspace where visibility, recovery, and confidence are built into the product.