Capability 01
Fast retail checkout and sale details
Case Study
Royal Retail Collective
Powering Multi Branch Retail Checkout Inventory and AI Monitoring
Royal Retail Collective needed faster checkouts and tighter inventory, plus loss prevention visibility that did not interrupt cashiers. We delivered an AI ready POS for multi branch reality.
- 14 week engagement
- Multi branch ops
- Checkout speed
- AI monitoring
System overview
An AI powered retail POS suite covering checkout, inventory, products, customers, analytics, and live camera monitoring.
Engagement
Engagement snapshot
- Client
- Royal Retail Collective
- Industry
- Retail, Point of Sale
- Duration
- 14 weeks
- Technologies Used
- Next.js, React, Node.js, PostgreSQL, AI camera monitoring
Branches ran slightly different till habits, so inventory never matched HQ expectations.
We standardized the sale path first, then inventory and customer modules, then AI camera monitoring for exception review.
Branch managers got reports that compare cleanly because the underlying sale events match.
Royal now scales playbooks across stores without forcing cashiers through clumsy extra steps.
Challenge
The business challenge
Royal Retail Collective needed faster checkouts and tighter inventory, plus loss prevention visibility that did not interrupt cashiers. Branches ran slightly different till habits, so inventory never matched HQ expectations.
Shrink and stockouts were debated with anecdotes instead of shared sale events. Checkout customizations per branch made training expensive, and HQ reporting was unreliable because each store defined a sale differently.
Prior retail tools failed when AI camera features added friction at the till, or when HQ forced uniformity that cashiers worked around. Inventory movements lagged checkout, so low stock and product issues surfaced late across locations.
What was at stake was cashier speed during peak hours, trustworthy multi branch comparisons, and loss prevention review that managers could use without slowing the critical path. New branches would inherit chaos without a known POS playbook.
Royal needed one sale event model across branches, local configuration within guardrails, and AI monitoring that reviews exceptions after the sale, not during it.
Approach
How we approached it
One sale event model across branches, local configuration within guardrails, and AI monitoring that reviews exceptions after the sale, not during it.
- Unified SKU and sale events so HQ reporting stops fighting branch variants.
- Protected cashier speed as a non negotiable acceptance criterion.
- Rolled AI monitoring as a manager review layer, not a till blocker.
- Trained branch leads who could coach peers without waiting on HQ.
Capabilities
What the solution included
Capability 02
Inventory and product management
Capability 03
Customer and multi branch operations
Capability 04
Sales and business reporting
Capability 05
Live AI camera monitoring
Capability 06
Store settings and secure sign in
Stack
Technology stack
Frontend
- Next.js
- React
- TypeScript
- Tailwind CSS
Backend
- Node.js
- REST APIs
- POS & inventory services
Database
- PostgreSQL
Cloud / Infrastructure
- Cloud hosting
- AI camera integrations
Results
Business outcomes
Outcome 01
Cashiers keep a fast path while inventory movements stay consistent
Outcome 02
HQ compares branches on the same sale definitions
Outcome 03
Low stock and product issues surface earlier across locations
Outcome 04
Camera exceptions are reviewed beside operational context
Outcome 05
New branches launch with a known POS playbook
Outcome 06
Customer history follows the shopper across participating stores
Insights
What we learned delivering it
- Retail AI fails when it adds friction at the till, we kept monitoring off the critical path.
- Multi branch success is mostly data discipline, not more buttons.
- Branch champions beat central training alone for lasting adoption.
Screenshots
Selected product screens
Interface snapshots from the live build. For full module documentation, see the Royal POS project page.
System overview
An AI powered retail POS suite covering checkout, inventory, products, customers, analytics, and live camera monitoring.
POS checkout
Fast product search, category filters, cart management, taxes, discounts, and multi method payment checkout.
Sale details
Completed sale invoices with itemized products, payment summary, change calculation, and print or share actions.
Inventory
Track stock levels, warehouses, low stock alerts, stock value, and product movement across branches.
AI monitoring
Live store camera feeds with people counting, dwell time, conversion insights, alerts, and heat maps.
Reports
Sales, orders, profit, payment methods, top products, and branch performance with export ready analytics.
Settings
Configure business details, branches, users, payments, receipts, taxes, backups, integrations, and security.
Secure sign in
Branded retail login with secure authentication so store teams can access POS and operations modules.
Testimonial
Client feedback
We got consistency without punishing cashiers. Inventory finally matches what left the till, and monitoring helps managers without slowing checkout.
Case study FAQs
Engagement questions, answered
Questions about this engagement: how we scoped, sequenced, and measured outcomes for Royal Retail Collective.
RolloutDo all branches go live together?
For Royal Retail we prefer a pilot branch, then a coordinated wave once the sale path is proven.
AIWill AI flag every camera event?
No. Rules focus on exceptions for manager review so branch teams are not flooded.
PricingCan pricing differ by branch?
Yes, within policies Royal defines, while sale events remain comparable for HQ.
ScopeWhere is the product walkthrough?
Screenshots and module detail are on the Royal POS project page. This case study covers the multi branch engagement story.
RolesWho owned acceptance criteria?
Branch managers and HQ ops jointly protected cashier speed as non negotiable. Loss prevention leads scoped AI as a post sale review layer.
TimelineWhat was the engagement timeline?
Fourteen weeks: unify sale events and inventory first, then customer modules, then AI camera monitoring for exception review.
IntegrationsWhat integrations were included?
AI camera monitoring was layered for manager review beside operational context. Deeper ERP links can follow once sale definitions are stable.
TrainingHow were cashiers and leads trained?
We trained branch leads who could coach peers without waiting on HQ, after the pilot sale path was proven.
FitDoes this fit a smaller retail group?
Yes. Even a few stores benefit from one sale event model; AI monitoring can wait until you need exception review at scale.
OutcomesHow were outcomes measured?
Royal compared checkout path timing, HQ branch report consistency, low stock signal timing, and manager exception review load.
MigrationWas SKU and inventory data migrated?
SKU catalogs and on hand baselines were unified into the shared sale event model before pilot cutover, with HQ reconciliation checks.
SupportWhat support continued after launch?
After the branch wave we refined pricing guardrails, inventory alerts, and AI exception rules from store lead feedback.
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