An engineering lab forrestaurant chains.
We build the systems that connect sales, orders and feedback across every store.
The data already exists. It just doesn't talk to itself.
POS, delivery apps, loyalty, reviews and spreadsheets. Each answers a narrow question. None explain why a store is slipping.
Customers look anonymous
No single view of who your regulars are, or when they leave.
Feedback is read, not used
Patterns across stores are spotted late, if at all.
Store problems show up as lost sales
By the time it's in the weekly numbers, customers are gone.
Follow-up depends on someone remembering
Win-backs and escalations are manual and rarely measured.
What we engineer
Each starts with a question operators already ask, and ends with a system that answers it daily.
- 01
Customer intelligence
“Which customers matter, and which are at risk?”
One profile per customer across every channel, with regulars flagged when their pattern changes.
- 02
Feedback intelligence
“What are customers actually telling us?”
Reviews and complaints grouped into recurring issues, linked to the stores and revenue they affect.
- 03
Store intelligence
“Which stores are hurting the network, and why?”
Sales, retention and issues per location, so decline is visible weeks earlier.
- 04
Workflow automations
“What should happen next, without anyone chasing it?”
Win-back campaigns, recovery messages and escalations triggered automatically, and measured.
AI and engineering, inside the operation.
Systems that run in the store, on the phone line and in the data, every day.
- Voice AI
Phone and drive-thru ordering
Voice agents that take orders, handle menu questions and send tickets straight to the POS, so no call goes unanswered at peak.
- Data engineering
One customer across every channel
Pipelines that merge POS, delivery app, loyalty and web orders, and resolve duplicate identities into one customer record.
- Automations
Complaint to resolution, hands-off
Agents that read a complaint, check the order, issue a credit within policy and log it against the store.
- Forecasting
Demand, prep and staffing
Hourly demand per store from sales history, weather and local events, feeding prep lists and shift plans.
- Computer vision
Speed of service and order accuracy
Existing store cameras that measure drive-thru and pickup times, and check orders before they leave the counter.
- Menu intelligence
Menus and promotions that bring people back
Which items and offers drive repeat visits, and which only move discounted volume.
StoreShield: operational intelligence for multi-location restaurant chains.
A product built from our restaurant chain research. It shows where revenue is at risk, and how to recover it.
Visit StoreShield
Customer Intelligence
Know which customers matter to your revenue.
Store Intelligence
Know which stores are affecting network revenue.
Feedback Intelligence
Understand what customers are telling you.
Recovery Workflows
Turn revenue risk into recovery action.
Buster's Pizza & Donairs: one view of every customer and store.
- Client
- Buster's Pizza & Donairs
- Location
- Canada
- Timeline
- Dec 2025 – Mar 2026
A growing Canadian pizza chain on disconnected tools. No view of customers across stores, and every campaign was a broad, expensive blast.
$3.5M
Revenue attributed to the platform
5x
Return on SMS marketing spend
+90%
Growth in loyal customers
70%
Reduction in marketing expense
2.5x
Recovery rate on dissatisfied customers
500K+
SMS messages delivered
How the lab works.
- 01
Study the operation
Time with operators, managers and the raw data.
- 02
Prototype on real data
Working tools on your own data, not mock-ups.
- 03
Engineer for scale
Harden what works so it holds across every store.
- 04
Measure in the store
Tied to revenue, retention or cost. Nothing vaguer.
