dbt Agents Helps You Automate Data Workflows. Zenlytic Helps You Understand and Act on Your Data.

Zenlytic goes beyond workflow automation, delivering clear, verified answers with transparent logic directly from your data, empowering cross-functional teams to make confident decisions without SQL, dashboards, or complex modeling.

Read More
data agent background

Why Modern Teams Choose Zenlytic

Verified, explainable answers, not just workflows

Zenlytic ensures results are logically sound, explains why they’re correct, and doesn’t leave analytical trust to interpretation.

Connect and get insights fast — no engineering backlog

Zenlytic connects directly to your warehouse and starts answering business questions in minutes; dbt Agents automate data pipelines but require dbt models, tasks, and environment setup.

Built for everyone, not just analytics engineers

Zenlytic delivers insights in plain language for operators, finance, GTM, and leadership — without needing SQL or dbt expertise.

Insight-centric rather than pipeline-centric

dbt Agents are invaluable for CI/CD and data operations automation; Zenlytic is invaluable for understanding data and making decisions across the business.

See Zoë in Action, Book a Demo Today

Quick Summary:
Zenlytic vs dbt

Best-in-Class
Acceptable / Mixed
Weak / Missing
Feature category
Feature
Zenlytic
dbt
Trust, Accuracy
& Governance
Logic verification
Verifies analytical logic before returning answers
dbt Agents execute workflows, but verification of answers depends on models written by engineers
Explanation of results
Provides clear, plain-language reasoning
Not designed to explain analytical insights; focus is on workflow execution
Transparency of assumptions
Makes assumptions, filters, and calculations explicit
Assumptions are embedded in dbt models and docs, not surfaced automatically
Decision readiness
Designed for confident, ready-to-act answers
Workflow status may inform readiness, but not for analytical decisions
Analytics &
BI Capabilities
Natural language querying
Converts business questions into validated insights
Not applicable — dbt Agents do not interpret natural language; focus is orchestration
Output format
Combines data, visuals, and explanations
Executes tasks, triggers jobs and orchestrations
Root cause analysis
Strong at follow-up conversational analysis
Limited to triggering jobs, not exploring data answers
Ease of Use
& Adoption
Target users
Built for non-technical users across the org
Built for analytics engineers and data platforms
SQL requirement
No SQL required
dbt models require SQL expertise
Learning curve
Very low — connect and ask questions
Requires knowledge of dbt, YAML, and engineering workflows
Setup and Time
to Value
Initial setup
Connects to your data and delivers insights quickly
Requires dbt project setup, model build planning, and orchestration
Configuration overhead
No semantic layers, agents, or orchestration setup required
Requires configuration of agents, schedules, and tasks
Time to first insight
Minutes after setup
Time tied to dbt project readiness and agent configs
Analytical Depth &
Proactive Intelligence
Iterative analysis
Maintains conversation context for deeper insight
Not designed for analytical iteration
Proactive insights
Focused on answering questions clearly and confidently
Proactive alerts come from orchestrated jobs, not analytics insights
Root cause analysis
Optimized to explain why something happened
Not a core use case
Role in the Modern
Data Stack
Primary role
Decision layer delivering trusted answers
Workflow automation and orchestration for data transformations
Relationship to other tools
Complements analytics, BI, and warehouse tools
Integrates into dbt ecosystem as part of CI/CD and operational tooling
Output purpose
Turns raw data into trusted answers
Ensures dbt tasks run as scheduled and orchestrated
Scalability, Integration
& Enterprise Fit
Scalability model
Scales naturally with modern cloud warehouses
Scales as part of dbt project deployment infrastructure
Governance alignment
Respects existing data permissions and governance by default
dbt governance dependent on model and repo configuration
Enterprise operational overhead
Minimal ongoing maintenance
Ongoing dashboard and workbook maintenance required
Use Case
Zenlytic
ThoughtSpot
Conversational analytics
Best-in-class
Limited
Fast onboarding for startups
Plug-and-play
Complex setup
Sales & Marketing enablement
Tailored workflows
More generic
Data analyst support
Works alongside analysts
Strong tooling support
Enterprise deployments
Scales well
Trusted by large enterprises

Customer Love & Case Studies

Whether it’s doubling down on channels that are performing, fixing campaigns that are broken or acting on early warning signals to reduce subscription churn rates – Zenlytic gives us the insights we need to grow.

Melissa DiNapoli, Director of Omnichannel
,
LOLA
Challenge

With LOLA’s previous analytics tools, they lacked the ability to see when and where their customer subscriptions were slipping.

Zenlytic Solution

The Zenlytic BI platform allows LOLA to identify exactly what is causing changes in subscription revenue so they can push innovative new features to combat churn. By using Zenlytic to identify, and act on early warning signs of churn, LOLA has seen higher customer satisfaction and a 10% decrease in their chun rate over the last 12 months.

Before Zenlytic, we were wasting 20+ hours per week suffering through excel files and reporting we didn’t even trust. Now the first thing I do every morning is open slack, check Zenlytic, and get to work.

VP of Marketing
,
KOIO
Challenge

KOIO was manually pulling excel files from a multitude of disparate sources (i.e. Shopify, ad platforms, website, etc) and relying on an expensive third party data consultant to provide analytics and reporting. KOIO’s team couldn’t pull reports or interactively slice their metrics on their own – rendering it impossible to capture important aspects of their business like inventory, specific product sales, acquisition channels and more.

Zenlytic Solution

Zenlytic’s data engineering support team was able to organize KOIO’s data pipelines, upgrade their data stack, and get them running on Zenlytic so that their entire team could understand the full story of their brand’s data.

Without Zenlytic, we never would have spotted these patterns. We have 100 x'ed our ability to understand what’s happening in our business.

CEO
,
Neobank
Challenge

For a DTC digital bank, security is paramount. Neo-bank noticed flagged accounts and higher default rates, signaling a possible early attempt to circumvent their fraud protection protocols. They needed to identify high-risk users, quickly and reliably.

Zenlytic Solution

With the help of Zöe, Neo-bank detected common traits among flagged accounts and found bad applicants in their system. In minutes (not weeks), they removed the users and tightened fraud protection.

Conclusion

‍

dbt Agents help you automate data workflows but Zenlytic helps you understand your data and make decisions with confidence.

Schedule A Demo to See Zoë in Action

Give your team a true AI data analyst with Zoë. No setup headaches. No tickets. Just answers.

Book a Demo