AI struck like a thunderbolt from Zeus.

For the first time, machines spoke our language.
We could talk to a computer the way we’d talk to a colleague.

And for a moment, it felt like magic.

Then reality hit.

Because the promise of AI ran straight into the most valuable thing your company owns:
its data.

You’ve spent years building your data infrastructure.
SQL-based lakes. Complex and far-flung for sure, but
based on the actual tools, work flows, and data feeds that make your business run.

You did the hard work.

And then AI showed up and started guessing.

Hallucinated queries.
Unclear sources.
Confident answers built on logic no one can see.

So teams type prompts again and again,
hoping this time the answer is right.

When the answer is wrong, everything downstream breaks.
Decisions slow. Confidence erodes.
And people quietly retreat to what they know:
stale dashboards, endless scrolling, and manual SQL.

We call this the Trust Gap in AI for company data.
It’s the distance between the promise of natural language access
and the reality that no one actually trusts the output.

And it puts data leaders in an impossible position.

The business wants self-serve answers now.
But you can’t risk a system that invents facts about your data.

So the future everyone was promised stalls out.

But it doesn’t have to.

We believe something simple and radical.
If AI is going to sit between people and complex company data, it must behave
like an analyst, not a storyteller.

We believe natural language should compile to truth, not vibes.
That answers should be deterministic, explainable, and grounded.
Every single time.

And we believe the companies that win in the age of AI will be the ones
who make their data instantly accessible without sacrificing trust.

These beliefs are why we built something new.

We built Catch-Phrase.

Today, we’re introducing a new category:
Trustworthy Text-to-SQL.

It was purpose-built for companies who already did the hard part.
Those who invested in SQL-based data lakes and who want AI to finally
unlock their value, not undermine it.

Here’s how it works.

You ask a question in plain English.
Catch-Phrase matches your words directly to database elements and deterministically
constructs the correct SQL. No training, no probabilistic detours, no mysteries.

You see the query before it runs.
You know exactly which tables are used.
And the answer you get is provably tied to your data and your question.
Every time.

This isn’t just theory.

The impact is immediate.

Teams get answers in under a second, not minutes.
Insights move 3X faster because there’s nothing to "check later."
Typing drops by over 60%, because one question is finally enough.

We’re seeing teams go from search and exploration to solution and insight in a single sitting.
Data leaders regain confidence in AI because it behaves predictably and transparently.
And business users stop treating data like something fragile they’re afraid to touch.

For the first time, natural language becomes a real interface to company truth.

Because when AI doesn’t hallucinate, your data finally speaks for itself.

That’s the future we’re building.

And once you experience it, there’s no going back.