Blog
Introducing Artifacts: living documents that refresh, evolve, and never go stale
BI tools solved data access. Nobody solved what happens after. Zenlytic's Artifacts are living documents: AI-generated presentations, financial models, and data apps that stay connected to your data warehouse, refresh automatically, and export as real .pptx, .docx, and .xlsx files. Built on Zoë, Zenlytic's analytics agent, Artifacts are governed by your semantic layer and branded to your company. Not a one-shot AI output, but a trusted, versioned, schedulable data product. This is the last mile of analytics, finally automated.
It's 2026. Why aren't we all using analytics agents?
It is 2026. AI analytics agents can deliver incredible answers if you are willing to spend weeks configuring them. The real barrier is not intelligence. It is setup. We have been forcing AI to look like legacy BI, complete with manual modeling and OLAP style configuration. Introducing Patterns by Zenlytic. With one sync, Zoë learns from your Snowflake query history, including thousands of past questions, dashboards, and SQL queries, and gains a semantic understanding of your business. Zero setup. Answers in minutes.
Hype Alert: Not All AI-Enabled Business Intelligence Makes the Grade
John Santaferraro, CEO and Head Research Analyst at Ferraro Consulting, published five best practices to maximize impact of AI for BI. He also provides the foundational AI enablers for Business Intelligence.
Has the modern data stack failed the business?
The modern data stack (MDS) transformed how data teams work, providing more power and efficiency than ever before. Tools like dbt, cloud warehouses, and automated testing have streamlined many processes. But is the business actually realizing the benefits?
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Ad Fontes Media uses Zenlytic to scale analytics with self-serve BI
By partnering with Zenlytic, a software company building the first self-serve business intelligence platform, Ad Fontes Media was able to increase productivity of the data team so they could deliver more precise insights to its customers, faster, without having to hire more team members.
Zenlytic Helps KOIO Achieve Data-Driven Success
"It's just so nice to log in any time of day and have up to the day accurate data. It's been a fun surprise for me just to have a better view into how our product has been selling. And like, men, women, by style, by size, by specific product, that's been a nice unlock for us through Zenlytic."
LLM's & Semantic Layer: Self Serve has Entered the Chat | Zenlytic
Paul, the CTO and co-founder of Zenlytics, discusses how LLMs and semantic layers enable self-serve analytics. He explains that self-serve is a spectrum that increases capabilities continuously based on the power of underlying technology. While large language models (LLMs) are powerful tools for understanding intent and distilling it into useful information, they require business context to be able to make correct decisions. This is where semantic layers come in - they encode important information like definitions, dimensions, joins, etc., ensuring correctness every time you calculate something. Companies without proper semantic layers often struggle with ad hoc SQL queries or outdated dashboards which can lead to errors in reporting. Warby Parker is a good example of a company that spends most of its data team's time refining their semantic layer to ensure consistency in metrics across stakeholders.
Building Self Serve Business Intelligence With AI And Semantic Modeling At Zenlytic
Tobias Macy interview Paul Blankly and Ryan Janssen about their no-code business intelligence tool, Zenlytic, which aims to enable self-serve BI through conversational technology. They explain that the intersection between large language models and semantic layers is necessary for effective self-serve BI. To achieve this goal, they focus on asking clarifying questions to help end-users articulate what they actually want from the data.
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