Zenlytic vs TextQL: Any AI Can Explore Data. Zenlytic Helps You Understand It.

TextQL uses AI agents to search across data and surface patterns. Zenlytic goes further... verifying logic, explaining results, and delivering answers teams can trust and act on immediately

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Why Modern Teams Choose Zenlytic

Understanding over generation

TextQL focuses on AI-driven exploration. Zenlytic focuses on making results clear, explainable, and easy to understand so teams don’t have to second-guess what AI produced.

Verified answers, not agent guesswork

TextQL relies on autonomous agents to infer insights across systems. Zenlytic validates the logic behind every answer, reducing risk and increasing confidence for real decisions.

Faster path from question to action

Zenlytic requires no ontologies, dashboards, or agent configuration. Teams connect their data and start asking questions immediately, accelerating decision velocity across the org.

Built for the entire organization, not just data teams

Zenlytic is designed for operators, finance, GTM, and executives who don’t speak SQL, making insights accessible without sacrificing accuracy or control.

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Quick Summary:
Zenlytic vs TextQL

Best-in-Class
Acceptable / Mixed
Weak / Missing
Feature category
Feature
Zenlytic
textQL
Trust, Accuracy
& Governance
Verification of logic
Verifies analytical logic before returning answers
Relies on agent-driven exploration without built-in verification
Explanation of results
Explains results in plain language
Provides summaries that may abstract underlying logic
Transparency of assumptions
Makes filters, assumptions, and calculations explicit
Assumptions are often implicit in agent workflows
Decision readiness
Designed to support high-stakes decisions with confidence
Better suited for exploratory insights than final decisions
Analytics &
BI Capabilities
Primary analytics output
Delivers decision-ready answers
Surfaces exploratory patterns and trends
Use of visuals
Combines numbers, visuals, and explanations in one response
Visuals may be secondary to summaries
Dependence on dashboards
Reduces reliance on dashboards and analyst workflows
Often complements other analytics tools
Ease of Use
& Adoption
Target users
Built for operators, finance, GTM, and executives
More oriented toward data teams and advanced users
SQL requirement
No SQL required at any stage
No SQL required, but deeper validation may need technical review
Learning curve
Extremely low learning curve
Moderate learning curve depending on agent behavior
Setup and Time
to Value
Initial setup
Connect data and start asking questions within minutes
Requires upfront setup to map data and configure agents
Configuration overhead
No ontologies, semantic layers, or agent tuning
Requires metadata mapping and agent configuration
Time to first value
Immediate value with minimal setup
Time to value varies based on data complexity
Analytical Depth &
Proactive Intelligence
Iterative analysis
Strong conversational follow-ups with retained context
Iteration depends on re-prompting agents
Proactive discovery
Focused on answering questions clearly
Strong at autonomous discovery and anomaly detection
Root cause analysis
Optimized to explain why something happened
Optimized to surface what might be happening
Role in the Modern
Data Stack
Primary role
Decision layer on top of the data warehouse
Exploration layer across multiple systems
Relationship to other tools
Complements existing analytics and warehouse tools
Often used alongside BI and analytics platforms
Output purpose
Turns raw data into trusted answers
Identifies opportunities and signals for further analysis
Scalability, Integration
& Enterprise Fit
Scalability model
Scales naturally with modern cloud warehouses
Scales through agent infrastructure and orchestration
Governance alignment
Respects existing permissions and governance by default
Governance requires configuration and oversight
Enterprise operational overhead
Minimal ongoing maintenance
May require ongoing tuning as usage grows
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

‍

Choose TextQL if your primary goal is broad, agent-driven exploration across many systems and you’re comfortable with AI surfacing possibilities that may require follow-up validation. TextQL is well suited for teams that want autonomous agents scanning data for patterns and trends.

Choose Zenlytic if your team needs clear, verified answers they can act on immediately. Zenlytic is built for organizations that value understanding over experimentation, where accuracy, explainability, and confidence matter more than raw exploration.

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

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