Turning Customer Feedback into Business Decisions
Using AI to extract operational insight from customer feedback, sales patterns, and demand signals for restaurants and service businesses.
- Type
- Research
- Status
- Concept · MVP planning
- Tags
- AI · Data · SME
Problem
Service businesses — restaurants, salons, clinics — collect customer feedback constantly through reviews, surveys, and social channels. Most of it sits in dashboards no one opens. The signal exists; the operational translation does not.
Challenge
How do you surface the one or two changes a small business should make this month, instead of a wall of analytics that requires a data team to interpret?
Proposed solution
A demand- and feedback-intelligence layer that translates raw signals into concrete operational recommendations ("staff up Thursdays", "shorten Item X cook time"). The surface is a weekly digest with three recommendations, not a real-time dashboard. Decision support, not automation.
Technology used
Business value
Not yet measured. Working hypothesis: 15–30% waste reduction for typical SME use case, driven by staffing and inventory recommendations.
Current status
Concept / MVP planning. Two partner restaurants confirmed for the first pilot in 2026 H2.
Lessons learned
SME owners need recommendations, not analytics. The right delivery surface is a weekly digest, not a live dashboard. Most owners don't have time to read dashboards — they have time to act on three concrete asks. Internal research finding.
Decision support. Recommendations are operational suggestions, not financial guarantees.