Zenlytic is an AI-powered business intelligence platform built around Zoë, a conversational AI data analyst. It gives business teams self-serve access to company metrics through natural language questions, instead of requiring SQL knowledge or manual dashboard requests to a data team. It is built by Ex Quanta, Inc. for organizations with structured enterprise data.
The platform targets a specific weakness in generic AI text-to-SQL tools: answers that drift, improvise logic, or ignore organizational context. Zenlytic uses a self-learning, Git-governed semantic layer paired with a deterministic validation system, so generated answers stay consistent and traceable across the company.
Zenlytic reads existing data definitions from data warehouses, LookML models, or Power BI DAX files to build an initial context layer. Users then ask questions in Slack, Microsoft Teams, Claude, ChatGPT, or the Zenlytic web app. Zoë translates each question into SQL, decompiles that SQL to show which fields are human-approved, and returns answers with inline citations and visuals. New query patterns are logged so data teams can review and promote them into the governed model.
A key differentiator is output format. Rather than static tables or CSV exports, Zenlytic produces “Live Artifacts”: auto-updating PowerPoint decks, Word reports, and Excel models with live formulas, alongside its Git-integrated context layer and Clarity Engine for field-level validation.
Pricing
Zenlytic does not publish pricing plans, tiers, or per-seat costs on its website. It offers a free interactive product demo and a “Try for free” signup flow, but no disclosed free tier for ongoing use. Third-party listings and Zenlytic’s own comparison content describe a seat-based model combined with query-based pricing and platform fees, though exact figures require contacting sales. There is no published enterprise or usage-based pricing sheet available for direct comparison.
* Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official website.
Key Features
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Zoë AI analyst answers complex data questions in natural language
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Works inside Slack, Microsoft Teams, Claude, ChatGPT, and its own app
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Self-learning semantic layer built from LookML, DAX, and warehouse schemas
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Git-governed data modeling with pull requests and version history
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Clarity Engine decompiles SQL and flags human-approved fields
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Live Artifacts generate auto-updating decks, reports, and Excel models
Use Cases
Executive and Board Reporting
Executive teams use Zoë to assemble live board packages instead of manually compiling department metrics by hand. It populates PowerPoint decks and Word reports with governed figures that trace back to source citations.
Cross-Channel Marketing Analysis
Growth teams query campaign spend and return on ad spend across ad networks directly in Slack or Teams. Zoë breaks down efficiency by channel and can model budget reallocation scenarios in real time.
Customer Churn Investigation
Analytics and customer success teams ask natural language questions about ARR trends and renewal performance. Zoë isolates specific churn drivers and sends proactive anomaly alerts to stakeholders.
Manufacturing Downtime Tracking
Operations managers ask real-time questions about plant throughput, downtime reasons, and scrap rates. Answers arrive by channel, so field managers can pinpoint machine or changeover issues without filing an IT ticket.
Automated Metric Governance
Data engineering teams use Zenlytic to unify scattered metric definitions across tools and reduce semantic drift. Unapproved metric variations get flagged, and a one-click review pipeline merges updated definitions into Git.
Strengths & Weaknesses
Strengths
Integrates directly into channels teams already use, including Slack, Teams, Claude, and ChatGPT.
The Clarity Engine decompiles generated SQL so users can verify query logic without reading code.
Produces live-updating deliverables, including PowerPoint decks and Excel models with working formulas.
Stores the semantic layer in Git, enabling pull requests, version history, and branch testing for data models.
Automatically extracts context from existing LookML and Power BI DAX files during setup.
Weaknesses
Pricing tiers, seat costs, and usage limits are not published; you need to contact sales for a quote.
Initial setup benefits from existing structured models like LookML or DAX, which limits day-one value for teams without them.
The self-learning semantic layer depends on ongoing review by data teams to keep metric definitions current.
Heavily custom-designed artifacts, such as a bespoke pitch deck layout, may still need manual adjustment after generation.
Who Is This For?
Data and analytics leaders who need a semantic layer that cuts repetitive ad-hoc requests while keeping metric definitions under Git control.
Executive leadership and board members who need reporting artifacts where every figure traces back to its source data.
Marketing and growth teams who need fast spend, conversion, and ROAS answers in Slack or Teams without writing SQL.
Operations and customer success teams who benefit from continuous metric monitoring and proactive anomaly alerts in daily workflows.
Frequently Asked Questions
Is Zenlytic pricing publicly available?
No. Zenlytic does not publish plan tiers or per-seat costs. Reporting from third-party review sites describes a seat-based model with query pricing and platform fees, but exact numbers require contacting sales.
Does Zenlytic offer a free trial?
Zenlytic offers a self-guided interactive demo and a “Try for free” signup flow on its website. Specific trial length and feature limits are not disclosed publicly.
What is the learning curve for non-technical users?
Business users ask questions in plain language through Slack, Teams, or ChatGPT, so no SQL knowledge is required. Getting value still depends on how much of the semantic layer is already governed.
What kind of exports or deliverables does Zenlytic produce?
Instead of plain data tables, Zenlytic generates full deliverables such as PowerPoint decks with presenter notes, Word reports, interactive memos, and Excel models with live formulas.
Does Zenlytic support version control for data models?
Yes. The semantic layer is stored in Git, supporting version history, branch-isolated testing, and pull request reviews for metric definitions.
What security certifications does Zenlytic hold?
Zenlytic states it is SOC 2 Type II certified, GDPR compliant, and HIPAA compliant, and supports SSO, role-based access control, and agent-level permissions.
Which platforms can teams use Zenlytic on?
Zenlytic runs inside Slack, Microsoft Teams, Claude, ChatGPT, and its own web application, with the same governed answers across each surface.
What data sources does Zenlytic connect to?
It connects to warehouses including Snowflake, BigQuery, Redshift, Databricks, Athena, Azure Synapse, Trino, Postgres, MySQL, SQL Server, MotherDuck, and Druid, plus dbt and Looker for existing semantic models.
How does Zenlytic prevent inaccurate AI-generated answers?
Its Clarity Engine decompiles the SQL behind each answer and visually flags which fields are human-approved, with inline citations showing the calculation, filters, and data freshness behind a figure.
Who benefits most from adopting Zenlytic?
Organizations with an existing structured data warehouse and a data team willing to review and promote metrics see the most value, since the platform builds on definitions that already exist.
Zenlytic connects to data warehouses and databases including Snowflake, BigQuery, Redshift, Databricks, Athena, Azure Synapse, Trino, Postgres, MySQL, SQL Server, MotherDuck, and Druid. It reads semantic layer sources from dbt and Looker (LookML), and imports business calculations from Power BI DAX files. It is deployed inside Slack, Microsoft Teams, Claude, and ChatGPT, in addition to its own web app. For identity and security, it supports SSO and SAML.