A lease runs for years and the data behind it usually describes a whole district. Sena reads the block: who else sells food within walking distance, at what price, and which cuisine the people living there say the area still misses.
The decision is an address. The data describes a district.
Which candidate site scores highest on competition, local demand, and price viability, and what the menu should cost there.
QSRFast-CasualCoffee ChainsConvenienceFitness
Sena, the Decision AI from Rwazi.
Decision AI with access to real-world dataA site decision runs for the length of a lease. The data behind it is usually shaped like a census tract.
Population, income and age for an area drawn by a statistical office. The right shape for a report, and a wider shape than the corner you are signing for.
A count or a model for the street, which tells you how many people pass. It stays quiet on whether any of them want what you plan to sell.
Rent, frontage, co-tenancy, term. All accurate, and all about the box, where the decision turns on the trade it will do.
What the decision needs is the block. Who already sells food within walking distance, at what price, and which cuisine the people who live there say the area still misses. That is three questions about one address, and the three sources above answer none of them.
Set the radius that matters for your format, which is a walk for a coffee bar and a drive for a highway unit. Everything after this is measured inside that boundary.
Sena Computer Vision reads the foodservice operators inside the boundary: who they are, what they charge, what the menu shows, what condition the site is in, and how busy it looks. The result is a density figure built from real storefronts.
Sena Consumer Activity asks people who live and work there what they eat, what they pay, how far they travel for food, and which cuisine they still miss nearby.
Sena returns a composite for each candidate: competitive intensity inside the radius, how well local demand matches your concept, whether your price point holds against what is already charged, and the demand it estimates. Candidates come back ranked.
The same reads repeat on a cycle, so a new competitor inside the radius or a shift in local demand shows up while the response still matters.
Rank your candidate sites before the lease is signed
See how Sena scores a trade area →the radius that matters changes with the format. A coffee bar competes with everything a customer can reach on foot, and a drive-through competes along a route. The same read applies to both, and the boundary is what differs.
| Capability | Role in this use case |
|---|---|
| Sena Computer Vision | Core. It reads every foodservice operator inside the trade area: brand, location, menu prices where they are posted, format, and site condition. This is the competitive density figure, built by reading the streets. |
| Sena Consumer Activity | Core. It collects local preference from people who live and work in the trade area: what they eat, what they pay, how far they travel, and what the area still misses. |
| Sena (Decision AI) | Core. It scores each candidate site across competition, demand fit, and price viability, ranks the shortlist, and reads it against how your existing locations perform. |
| Sena Connect and Integrations | Core. Brings in your existing location performance, your unit economics, your site criteria, and the candidate pipeline, by CSV or Excel upload. |
Both reads happen inside the boundary you draw, which is what makes the score about the site, where a city figure averages it away.
Demographics for the area, a traffic estimate for the street, a broker’s read on the property, and the pattern-matching of a team who know the format. Real inputs, all at a wider resolution than the decision.
The competitor count inside your radius with their prices, and what the people living there say the area still misses. Both at the resolution of the block.
Which site you sign, what the menu costs there, and which candidates come off the list before anybody visits them.
Sena
Illustrative interface. Numbers are an example, not a Rwazi result.
The core user. Sign sites on a scored shortlist, with the competitive count and the local demand behind each one.
Strategy solutions →Choose which cities and neighbourhoods to enter, and in what order.
Watch the trade area around existing locations, and see a new competitor while there is still time to respond.
Build the local message from what people in that trade area said about the category.
Adapt the menu and the price to the market, using local preference, where the national template flattens it.
Innovation solutions →A demo takes about 20 minutes. For most use cases, the first decisions land days after kickoff.
Competitive density comes from reading the streets inside your boundary, so a closed unit or a new opening shows up as it is, where a directory records it late.
The same measurement runs on a cycle, so the trade area you signed for stays measured through the term.
Build an entry plan from real store, consumer, and competitive data.
See the use case → UC · Shelf & AvailabilityKnow what sits beside your products in every store.
See the use case → UC · Consumer UnderstandingRead real consumer demand and preference in the markets legacy sources cover thinly.
See the use case →Tell us the decision you are weighing, and we will bring the market read to the conversation.
Bring the decision in front of you and we will run the first read against it on the call.