Trusted AI Analytics
Trust what your AI tells you about your numbers
We help 8-figure e-commerce brands ask their data anything and trust the answer. Everyone in the company gets the same number whether they ask the AI or open a dashboard, and anyone can ask an ad hoc question without waiting days for an analyst.
Bring two or three questions your team has asked its AI.
See how it starts and what it costs
LazyAnalyst is an e-commerce analytics consultancy led by Corinna Zennig:
ex-Deloitte, 10 years in business and 35+ projects delivered.
When your AI’s numbers and your dashboards disagree
8-figure e-commerce brands call us when they can’t trust their AI’s numbers. Does this sound familiar?
- The AI’s number doesn’t match the dashboard.
- You ask the same question twice and get two answers.
- A simple question from the CEO takes the team days.
- The CMO can’t defend the budget with numbers everyone accepts.
- Finance and operations each work from a different set of numbers.
- Nobody can say where a number came from.
One health & wellness subscription brand connected an AI assistant directly to its data and asked: how many active customers do we have? The answer was nearly double the real number. The assistant was reading an outdated copy of the data, with duplicated subscription rows and cancelled customers counted as active.
Why does AI get e-commerce numbers wrong?
Because it reads data that is incomplete, duplicated or undefined, and fills the gaps itself without telling you.
A new analyst would make the same mistakes in their first week: no access to some of your systems, copies of the data full of duplicates, and nobody has told them what “active customer” means here. An AI assistant starts from that position every time you ask.
The answer is a division of labor. Let the AI interpret your question, and let tested software calculate the answer.
How do we make AI answers trustworthy?
Trusted AI Analytics is our build that lets business users ask their data questions in plain language and get answers that have been tested for accuracy. It takes six steps, and each one exists because of what we’ve seen firsthand goes wrong without it.
1
Collect
Your store, ads, email and subscription data flow into one place every day, with their history.
Without it, your AI only sees what someone exported.
2
Clean
One record per customer and per order, with refunds and test orders handled.
Skip it and the same customer gets counted twice.
3
Define
One agreed definition for every metric, signed off by the people who own it.
Without it, one question could get five numbers.
4
Teach
Your AI reads those definitions first and knows when to ask instead of guess.
Otherwise you get confident wrong answers without even noticing.
5
Ask
Your team asks in plain English, and in the tool we set up with you each answer says how fresh its data is.
Until then, every question waits for an analyst.
6
Prove
Before anyone relies on it, we test the new build on your own benchmark questions.
Skip it and nobody knows whether to trust the answers.
What can your team ask once it’s built?
Anything your data can answer, in plain language, without waiting for an analyst. For example:
- Which products had the largest decline in repeat purchases?
- How did our acquisition costs change after we increased Meta spending?
- Which customer segments are becoming less profitable and why?
- Why did our subscription revenue increase while total revenue stayed flat?
Every answer comes from definitions your team signed off, and says how fresh its data is. Your CEO sees numbers from marketing, sales, finance, and ops that hold up when someone checks them.
Results from our Trusted AI Analytics build
These are example results from our latest AI Analytics build (with Claude):
8
AI implementation risks found. The client’s CTO knew of one at the start.
11 of 11
flagged retention questions were answered wrong before the build.
93%
42 of 45 graded test questions answered correctly after the build, on a separate test set. Anthropic reports about 95% for its own analytics with Claude.
How does it start, and what does it cost?
It starts with a free call, and every step after it has a fixed price and a way out ;).
Build prices are quoted after the check, because they depend on your data and your questions.
1
Free strategy call
60 minutes on your goals, your setup and the questions you can’t answer today. You leave with clear next steps.
2
AI Answer Check, $5k
We grade 15 to 20 of your leadership’s real questions against verified numbers and trace every wrong answer to its cause. Within 4 weeks you get the plan, with a fixed price for each phase. The fee is credited in full to your build if you sign within 30 days of the readout, and there’s no fee if every answer checks out.
3
Build it in phases
We build one business area at a time, on the tools you have or on a data platform such as Databricks if they can’t get you there. Every phase has a fixed price. At the end of each one, we test the result on questions from your own business, and you decide whether to continue. Everything is set up in your own environment, and you keep ownership of the tools.
Not sure what you need yet? See every way to work with us.
Questions we hear on our data strategy calls
Can’t I just connect Claude or ChatGPT to my Shopify data?
You can, and it takes minutes. But the AI then works from whatever the connector returns, which may be part of your data and none of its history, and it decides for itself what each metric means. The same question can get two different answers.
Can’t a data connector tool with an AI chat do this?
Sometimes. A connector tool pulls your ad, store and CRM data together and lets an AI chat answer across it, which is often enough for simple marketing questions. What it can’t know is what your business means by “active customer” or “revenue”, or which orders are tests, duplicates or refunds. The AI Answer Check grades 15 to 20 of your real questions against verified numbers, so you know whether the answers you get today are right. If every one is, there is no fee.
What is a semantic layer?
A semantic layer is a shared dictionary that tells every report, dashboard and AI assistant exactly what each business metric means and how to calculate it. “Active customer” is defined once, in one place, and everything that asks for it gets the same number.
Do we need a data warehouse first?
Maybe. A data warehouse is a central database that regularly collects information from your business systems, checks that everything arrived and keeps historical changes. You may need one if your questions cross several tools, if you need history your tools overwrite, or if the AI can’t read all your records straight from the tools. If your questions stay inside one or two tools, you may not. The AI Answer Check traces each wrong answer to its cause, which shows whether you need one. Our software guide compares the options.
We already have dashboards. Is that enough?
It might be! We believe dashboards still have their place and are the best place to start. AI analytics is best added to improve what we call “data detective work”: the one question your CEO asks on Wednesday that the team still hasn’t put on a dashboard by Friday. Ad hoc analysis and really getting to the bottom of an issue are what AI analytics excels at when it’s set up properly.
What does the AI Answer Check cost, how long does it take, and what if all our answers are accurate already?
The AI Answer Check costs $5,000 and delivers the plan within 4 weeks of kickoff. If all your answers are accurate already, there is no fee. If you sign a build within 30 days of the readout, the fee is credited in full.
Where does our team ask its questions, and which AI do you use?
In a chat your team opens like any other AI assistant, set up with you around your own data. A common choice is Claude, but it depends on your setup. Your team asks in plain English, and every answer says how fresh its data is.
How do you keep customer data private?
Data protection is agreed with each client. We’ve implemented this for a HIPAA brand before. We mask or remove personal data where that is needed. Tell us on the call what your data holds, and we’ll tell you what that means for the build.
How long until the team can ask its first question, and what do you need from us?
The first accurate answers come at the end of the first phase, which the final plan from our AI Answer Check outlines. From you we need the people who own your metric definitions, access to your tools and the real questions your leadership asks.
Which of your AI’s answers can you trust today?
Book a free 60-minute data strategy call. You’ll leave with clear next steps for your data; whether you end up working with us or not.
Bring two or three questions your team has asked its AI.
You’ll talk with Corinna Zennig, Founder & CEO of LazyAnalyst.