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Zenlytic

AI-native business intelligence with a conversational analytics agent

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About Zenlytic

Zenlytic offers a business intelligence platform built around a conversational AI analyst that answers questions from governed data using a semantic layer. It is aimed at data teams and business users who want self-serve analytics, with a focus on ecommerce and consumer brands. The agent can also run deeper multi-step analysis.

Common use cases

  • Self-service analytics
  • Ecommerce performance analysis
  • Executive Q&A

Social listening

What people are saying about Zenlytic

The few reviews found value Zenlytic's natural-language querying and quick semantic-layer setup, and buyers typically plan to validate answer accuracy.

What stands out

  • Non-technical users can get answers through natural-language-to-SQL.
  • Semantic-layer setup is described as relatively easy with drag-and-drop tools.
  • Snowflake connection and vendor support are reported positively.

Things to consider

  • Worth validating answer accuracy against known results, including simple questions.
  • Teams typically plan to specify output formatting explicitly in prompts.
Sources (1)

Themes summarized from public reviews, community discussion and press coverage as of November 2026. They reflect public discussion, not a rating or an endorsement by AI Connect.

Products and services

Zoe

Conversational AI analyst for business data.

Semantic layer

Governed metric definitions used by the agent.

Dashboards and exploration

Standard BI visualizations.

Pricing and editions. Capterra lists a free self-service tier for up to 5 users, a 14-day trial, and custom-quoted Business and Enterprise plans.

Key features

  • Natural language querying
  • Semantic layer
  • Multi-step analysis
  • Governed metrics
  • Embedded analytics

Integrations

  • Snowflake
  • Google BigQuery
  • Databricks
  • Shopify
  • Klaviyo

Zenlytic alternatives

FAQs

Does it use a semantic layer?

Yes, answers are grounded in governed metric definitions.

Who is it for?

Data teams and business users, notably in ecommerce.

Where does the data live?

In the customer's data warehouse.

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