200 locations.
One real story.
Your chain can't read 10,000 reviews a week. We do. Across Zomato, Swiggy, DoorDash, and Uber Eats — classified by sentiment, clustered by complaint, and broken down by location, by dish, and by time. You get answers, not dashboards full of word clouds.
From a pile of reviews
to operator-ready answers
Full review corpus
Text, rating, reviewer metadata, timestamp, dish tags where available — clean, per location.
Sentiment at aspect level
Not one score per review — separate signals for food, delivery, packaging, portion, taste, and value.
Dish-level analysis
Which dishes customers love, which quietly drag your rating, and which inconsistently vary by kitchen.
Location benchmarking
Rating, complaint rate, and themes per outlet, ranked — so regional managers see exactly which kitchens are dragging chain NPS.
Competitor comparison
Your signature dishes vs named competitors' signature dishes, side by side, in the same zone.
Trend over time
Complaints that are growing vs shrinking. Post-launch sentiment shifts after menu or packaging changes.
Useful where reviews matter
QSR chains with 50–500 outlets
Consistency is everything. This is how you find the outlets that are silently hurting the brand.
Cloud kitchen operators
Tune menu and ops per virtual brand with real customer feedback, not founder intuition.
Category leads & R&D
Decide dish changes and new launches from what customers actually wrote, at scale.
Research & consulting firms
Ship city, category, and player-level sentiment reports grounded in primary data.