Rwazi built Sena, a Decision AI that shows retail and e-commerce teams share of shelf, rival price, and delivery-area coverage, from geo-verified store photos.
Online orders and store sales show what sold. The rival shelf, the price beside it, and the stores around each address shape what sells next.
A rival listing, a price cut, or a new private-label pack can change a category between planning cycles.
Sena. Sena reads the products in each shelf photo, rival products included, and tracks share of shelf by store and territory.
A planned price and a promotion reach each channel and store at different times, and some reach the shelf altered.
Sena. Sena reads the price tag in each photo and flags the stores and channels where price departs from plan.
An address with grocery stores nearby orders small, impulse baskets. An address far from stores orders the full shop. Order data blends the two.
Sena. Sena maps the grocery and convenience stores in each delivery radius and sets that count beside the orders from the area.
Each retail intelligence use case below links to its full method, with the signals Sena reads and the questions retail teams bring.
Sena reads the products in each shelf photo, so share of shelf, new rival listings, and price moves show by store, territory, and channel.
Decides: Which category, store, or rival gets the next response.
See share of shelf monitoring →Shoppers state which retailer they chose, which others they considered, and what would move them, by format and market.
Decides: Which price, range, or service change wins the shopper.
See purchase drivers analysis →Sena maps every grocery and convenience store in each delivery radius, then sets that count against order data.
Decides: Where to open, relocate, or reassort a dark store.
See dark store placement →Sena captures the access conditions at each delivery point, from entry rules to parking, and separates access issues from recipient issues.
Decides: Which stops need new timing, routing, or instructions.
See last-mile delivery optimization →A real Sena reading for one category across several markets, with the shelf photo behind every price and listing.
Get the price reading →Sena ties every price and listing to the photo it came from, so a team checks the index store by store.
Each price in the index comes from a photographed tag, located and dated. The index counts confirmed tags only.
Every capture goes through a multi-level QA flow before it enters the index. Sena declines a capture that misses the check, and the decline stays visible.
A price index compares a product's shelf price with the rival product a shopper sees next to it. Sena captures both from the same photo, so the index holds per store, per channel, and per day of capture.
The price index calculator. Two shelf prices and a planned band show where a store sits.
A walkthrough runs Sena on one category in one market, from shelf photo and shopper input to the decision on the table.
Book a walkthrough →Pick the closest fit, and the walkthrough starts on that team's decision.
Share of shelf, assortment, and rival listings, store by store.
Book a merchandising walkthrough →Price index against rival stores, by format and territory.
Book a pricing walkthrough →Dark store placement and delivery issues at the door, by delivery radius.
Book a delivery walkthrough →Rival presence, pricing, and shopper demand in a market before entry.
Book a strategy walkthrough →A real Sena reading for one category across three to five markets, with the shelf photo behind every price and listing.
Sena captures geo-verified photos and consumer activity in the markets a question names, when the question needs it.
Sena reads those captures beside the files and systems already in use, across 250+ integrations.
Sena compares the market against the plan and shows each gap, with every figure traced to its source.
| Area | Common method | Sena |
|---|---|---|
| Share of shelf | Periodic store checks by the team's own reps | The products in each photo, rival products included |
| Price position | Planned price lists and online price scrapes | The price tag in the store, beside the rival product |
| Dark store siting | Address density and rival locations | Store count per delivery radius, set against orders |
| Delivery issues | Route data from pin to pin | Access conditions captured at each delivery point |
| Auditing | Price lists reconciled by hand | Each shelf price opens the photo behind it |
Talk to the team about one category, one channel, and the decision due this quarter.
Talk to the team →