Industries / 03
Restaurant Intelligence
Ask the business. Get the answer.
Conversational BI, guest experience analytics, and anomaly detection for single brands and multi-brand portfolios.
Overview
How we approach it.
A restaurant leader who wants one answer has to toggle between the POS, labor, and finance systems. Guest feedback is split across surveys, apps, and review sites, and invoice errors and duplicate charges slip past manual review. Operational teams and executives also need different views of the same numbers, and most have neither the time nor the training to dig through traditional BI tools.
We connect the business data to semantic models and put a conversational interface on top, so people ask questions in plain English and get answers. One sentiment engine reads every feedback channel, line-item anomaly detection watches invoices and purchase orders, and automated briefings and prioritized alerts help teams decide what to follow up on. For one restaurant operator, time to an answer went from days to seconds.
Systems we connect
Where your data lives today.
The problem
What gets in the way.
Leaders toggling between POS, labor, and finance systems
Guest feedback scattered across surveys, apps, and review sites
Invoice errors and duplicate charges slipping past manual review
What we build
What changes.
- 01
Conversational BI in plain English
- 02
One sentiment engine for every feedback channel
- 03
Competitor pricing, menu, and promotion tracking
- 04
Line-item anomaly detection on invoices and purchase orders
- 05
Multi-agent systems for briefings and alerts
In practice
One we built.
Related services
How we deliver it.
FAQ
Questions buyers ask.
01What is conversational BI for restaurants?
Conversational BI lets restaurant leaders ask questions about their business in plain English and get answers from their POS, labor, and finance data without waiting on a report. At one restaurant operator, it cut the time to an answer from days to seconds. It runs on semantic models connected to the operator's own data.
02How can restaurants catch invoice errors and duplicate charges?
Line-item anomaly detection on invoices and purchase orders flags the errors and duplicate charges that manual review misses. Alerts are prioritized, so teams see the most important issues first. At one restaurant operator, financial anomalies are now caught in near real time instead of weeks later.
03How do you combine guest feedback from surveys, apps, and review sites?
You run every feedback channel through one sentiment engine that classifies comments with AI, so the results can be compared and acted on in one place. We built this for a restaurant operator, where guest insights once buried across platforms now reach the teams who can act on them. Feedback analytics, anomaly detection, and automated briefings together help teams identify issues and prioritize follow-up.
Bring us the hard problem.
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