AI data analyst for a restaurant chain
Ask in plain words, get the answer from live data: every number shows its source.
- Client
- Restaurant chain
- Industry
- Quick-service restaurants
- What we did
Results
100%
of answers show their source and open the matching dashboard view.
12
characters the mascot plays while the analyst counts.
22
achievements that unlock new outfits for the mascot.
Challenge
A restaurant chain had plenty of data and too little time to read it. Sales, payments, delivery and staff each lived in its own system, and every question from management turned into a request to the analysts and a report a few days later. We built an AI data analyst: managers ask in plain words, the answer is counted from live data, and every number comes with its source and a link to the dashboard view it came from.
Try it below. The demo runs on made-up data for a chain of 24 restaurants; the real platform works the same way on the client’s own data.
Ask the analyst
Management wanted answers, not reports. A question like «why are card payments failing this week?» meant pulling data from three systems, a day of work for an analyst and an answer that was already old. General-purpose AI chatbots were fast but made numbers up, which is worse than no answer. The chain needed something as quick as a chat and as reliable as its own accounting.
Approach
Numbers only from data
The model never invents a figure. It turns the question into a query, the query runs against the chain’s live data, and the model only explains the result. If the data cannot answer the question, the analyst says so.
Every answer shows its source
Under each answer: which system, which period, which restaurants. One click opens the dashboard section with exactly the same filters, so anyone can check the number in seconds.
Live, not monthly
Data updates through the day, so a question at 19:00 gets an answer about today’s dinner rush, not last month.
A mascot people like to open
A friendly 3D chick lives on the login page and in the chat. While the analyst counts it plays one of 12 characters chosen by the type of question, greets people by name and has small day and night scenes. It turns a data tool into something the team opens every day by choice.
Outcome
Managers get answers in seconds instead of days, and they trust them because they can check every number. The analysts spend their time on real analysis instead of routine requests.
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