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.
Book a Demo →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.
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.
Zenlytic requires no ontologies, dashboards, or agent configuration. Teams connect their data and start asking questions immediately, accelerating decision velocity across the org.
Zenlytic is designed for operators, finance, GTM, and executives who don’t speak SQL, making insights accessible without sacrificing accuracy or control.
See how Zenlytic stacks up across the dimensions that matter most.
| Trust, Accuracy & Governance | ||
|---|---|---|
| Feature | Zenlytic | TextQL |
| 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 |
| Ease of Use & Adoption | ||
| Feature | Zenlytic | TextQL |
| Target users | ✓ Built for operators, finance, GTM, and executives | More oriented toward data teams and advanced users |
| Learning curve | ✓ Extremely low | Moderate depending on agent behavior |
| Setup and Time to Value | ||
| Feature | Zenlytic | TextQL |
| 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 | Time to value varies based on data complexity |
| Analytical Depth & Proactive Intelligence | ||
| Feature | Zenlytic | TextQL |
| Iterative analysis | ✓ Strong conversational follow-ups with retained context | Iteration depends on re-prompting agents |
| Root cause analysis | ✓ Optimized to explain why something happened | Optimized to surface what might be happening |
See how leading brands use Zenlytic to move faster and make better decisions.
“One of the best tech decisions Verizon has ever made.”
Chris Colangelo, VP, Verizon Wireless
Read the Verizon story →“I have talked about data democratization for a decade, and Zoë is truly democratizing data. When I’m with our CEO or COO, I just ask Zoë in the meeting. I trust it.”
Tyler Knapp, SVP Data Analytics & Technology Strategy, J.Crew Group
Read the J.Crew story →“Zenlytic and the team have been amazing in opening our eyes into the possibilities in this space. Seeing is believing. And we saw. You made us see.”
Paul G., Senior AI Architect, Stanley Black & Decker
Read the Stanley Black & Decker story →Choose Zenlytic if your team needs clear, verified answers they can act on immediately, where accuracy, explainability, and confidence matter more than raw exploration. 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.
Experience the AI data analyst that shows its work, verified answers, full transparency, zero guesswork.
Book a Demo →See how Zenlytic stacks up against other tools