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At Zenlytic, we believe that LLMs are now powerful enough for everyone to have a personal AI data analyst. This is a key unlock for real self-serve analytics and rapid improvements in decision velocity.
But there’s a catch: without the correct context management, the LLMs won’t do a good job answering questions. And without the interface, non-technical users will struggle to understand (and trust) what the LLM is doing.
That’s why we’re thrilled to introduce the Clarity Engine.
It delivers the two necessary components for self-serve data:
- Trust (understandable answers)
- Depth (answers all questions)
We’re doing things differently at Zenlytic. Zoë, our AI analyst for people who don’t speak SQL. She works using our Clarity Engine, which addresses the past failings in analytics.
The World Is Speeding Up
Across industries, the pace of work has accelerated. Coding, design, and writing now move at lightning speed, powered by AI assistants. Data is following; it's happening now.
Already:
- 55% of teams say AI has sped up decision-making.
- 70% of people ask AI before their boss.
The businesses that can harness this speed, what we call decision velocity, will thrive. Those who can’t will be left behind.
The Problem With Early AI Data Tools
The first two waves of AI-powered analytics didn’t quite get there. They promised self-serve insights, but neither text-to-SQL nor the semantic layer method had both critical dimensions:
- Text-to-SQL lacked Trust: Users couldn’t understand the outputs. LLMs generated SQL, but non-technical teammates had no way to verify its accuracy.
- Semantic layers lacked Depth: Most systems only answered pre-built questions, leaving real business problems unsolved.
The result? Endless “Can you check if this is right?” messages to data teams. Days, weeks, months of waiting - while the world kept moving forward.
The Clarity Engine: Trust + Depth
The Clarity Engine solves these problems by combining:
- The Depth of SQL, the power to answer any question, not just what’s on a dashboard. From CAC tradeoffs in campaign strategy to Python-powered analytics, Clarity Engine delivers full-stack analysis.
- The Trust of a Semantic Layer ensures transparent, explainable outputs that are governed, making every answer both understandable and secure.
As Amanda Yan, Head of Data at J.Crew and Madewell, put it:
“We’ve tried every AI-powered platform out there. But our self-serve users still asked us to verify everything. Zenlytic solves this. Once our end users understand the results, they trust the results.”
More Than AI-Powered BI
This isn’t just BI with AI layered on top. AI-powered BI simply utilizes the LLM with the same controls within the BI platform. The Clarity Engine analyzes data from first principles, learns on the fly, and moves teams from facts to decisions.
As Matt Griffiths, CTO of Stanley Black & Decker, said:
“We already had a dozen tools that could tell us our sales last week. But only Zenlytic can answer the questions dashboards can’t. Zoë handles those high-impact questions that would be impossible to ask in traditional data platforms.”
How The Clarity Engine Works
The Clarity Engine learns as you ask questions:
- A business user asks Zoe in their own language, the question they are looking to solve.
- Zoe’s LLM generates SQL,
- The Clarity Engine applies data governance to the SQL query
- The Clarity Engine structures the query into understandable metrics, with plain language explanations
- Answers and visualizations are returned instantly.
- Memory updates automatically, learning over time. And the Clarity Engine recommends updates to the semantic layer as necessary.
No heavy setup. No endless manual modeling. Just ask, and get the deeper answers the business needs.
It’s Not About the Data, It’s What You Do With It
The Clarity Engine transforms data into action. By collapsing the decision window, it gives your team the power to learn faster, act faster, and outpace the competition.
The world just sped up. It’s time your data did too.
👉 Meet the Clarity Engine — and step into the future of decision-making.
