Do not search dashboards. Ask your business.
Zeno sits on top of the same intelligence layer. Ask a question in plain language and get an answer with data, context and the next action beneath it.
Zeno combines revenue, contribution, CAC and product mix and explains which components caused the change.
Ask the question the way you would ask an analyst.
The answer should not just sound plausible. It should be traceable back to your data.
Why did profit fall while revenue rose?
Zeno decomposes revenue, margin and spend changes.
Which products can take more budget?
Combine product economics, demand and stock.
What needs attention today?
Use alerts, anomalies and business impact.
Bring together · Explain · Decide
Zeno uses the same verified intelligence layer instead of generating an isolated answer.
Understand the question
Intent and required data are identified.
Retrieve sources
Relevant metrics, orders and trends are selected.
Explain
Differences and possible causes are tested against each other.
Answer and action
The result comes with evidence, confidence and a next step.
A strong answer also shows why it is trustworthy.
Without evidence, AI is just a polished interface over uncertainty.
| Element | What you see | Why it matters |
|---|---|---|
| Answer | Concise explanation | Fast clarity |
| Evidence | Metrics and sources | Verifiability |
| Confidence | Strength of evidence | No false certainty |
| Next action | Concrete next step | From insight to decision |
Zeno makes intelligence accessible without a data team.
For small teams it reduces analysis work. For larger teams it shortens the path to the right question.
Founder
Ask what changed without opening six tools.
Ecommerce
Get priorities across products, feeds, customers and channels.
Performance
Ask where budget, CAC or attribution anomalies are forming.
What you want to know before you start.
Can Zeno hallucinate?
Product logic constrains answers to available data and shows evidence. Insufficient data is explicitly stated.
Is this just ChatGPT on my database?
No. The value is in the intelligence layer, business logic and evidence, not just a language model on raw tables.
Go deeper on the decision logic.
The five financial metrics every DTC brand should review weekly
Most DTC founders review revenue and ROAS weekly. They review margin, CAC, and LTV quarterly at best. That cadence is backwards.
What Shopify analytics cannot tell you and what you need instead
Shopify's built-in analytics are a good starting point. They are not an attribution model, they are not a profitability model, and they are not a customer intelligence platform. Here is what they are and what you need beyond them.
Why your Meta ROAS is lying to you and how to measure what actually happened
Platform attribution models are built to make platforms look good. We analyzed 48 DTC brands over 90 days and found the average over-attribution gap is 34%. Here is what to do about it.
Ask what you need to know today.
After connection, Zeno can reason over your own intelligence layer with evidence underneath.