50% More Seating Efficiency
Eliminated double-bookings, improving seating efficiency by 50%
Key metrics
- 50%50% reduction in double-bookings through real-time availability
- 700%700% ROI within 12 months of deployment
- 2× FasterIntegrated POS sync halved payment reconciliation time
- 124K124,000 core
active users
About the Client
Client
LetsBAR online/offline table reservation CRM
Industry
Restaurant & Hospitality Technology
Client overview
LetsBAR is a web-based reservation and CRM system with 2D/3D floor planning and direct POS-terminal integration.
The Challenge
Problem statement
Restaurants faced inventory conflicts and manual floor planning, causing booking errors and inefficient seating.
Pain points
No visual mapping of tables or zones
Disconnected POS requiring manual reconciliation
Lack of analytics visibility into booking patterns, peak hours, and table popularity
No personalized recommendations for diners to discover other restaurants
Restaurants lacked visibility into customer overlap with competitors
Difficulty running targeted marketing campaigns or selling event tickets
No mechanism to collect post-visit feedback for continuous improvement
The Solution
Approach
Built a responsive web app and CRM with 2D/3D rendering for precise floor planning, integrated POS-terminal sync, and added advanced analytics, recommendation, marketing, feedback, and ticketing modules.
Technologies used
React, Python, AWS (ECS, RDS, S3, CloudFront), POS APIs
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Implementation detailsPhase 1. Assessment
We started by mapping real floor layouts and POS workflows in working restaurants, looking for where seating, payment reconciliation, and staff habits actually broke down. That map became the spec.
Implementation detailsPhase 2. Development- The core build: a visual floor editor for seating plans, a booking API, and full POS integration so payments and reservations stopped living in separate systems.
Implementation detailsPhase 3. Testing & Deployment- Deployed on AWS with a CI/CD pipeline, load-tested against dinner-rush traffic, and rolled out with CRM tutorials so staff knew the system before it went live.
Implementation detailsPhase 4. Ongoing support after initial release- We still run support: monitoring, bug fixes, and new features driven by what restaurant staff actually ask for.
Results and Impact
Key outcomes
50% Less Reconciliation Time
POS sync reduced reconciliation tasks by 50%.
12 Actionable Insights
CRM analytics surfaced 12 patterns the owners acted on, from the 5–7 PM dinner-rush peak to table-popularity heatmaps that reshaped seating layouts
+20% Satisfaction QoQ
Post-visit feedback surveys increased satisfaction scores by 20% quarter-over-quarter by highlighting areas for targeted service improvements
Client feedback
Helen Smith
Guests love the QR menu and tipping finally just works. Our revenue per guest has never been higher, and tables turn around three times faster than before
Tech Stack
- Backend
- Python (REST API for bookings, POS sync, analytics)
- PostgreSQL on AWS RDS for transactional data
- Redis (AWS ElastiCache) for caching, sessions, queues
- Worker layer for async jobs (POS reconciliation, notifications, ML tasks)
- WebSockets for real-time availability updates
- Integration layer for major POS and payment gateway APIs
- Frontend
- React web app with 2D canvas and 3D WebGL rendering for floor plans
- PWA capabilities: service worker, offline mode, IndexedDB caching
- Social login and customer portal components
- Web Push notifications
- Infrastructure
- AWS ECS (containers) behind an Application Load Balancer
- Amazon RDS (PostgreSQL), S3 for media and static assets
- Amazon CloudFront for global edge caching and asset delivery
- Amazon SQS/SNS for messaging and eventing
- Route 53, ECR, IAM
- CI/CD pipeline (e.g., GitHub Actions → ECS deploy)
- Observability with CloudWatch logs/metrics and alarms
- Automated backups and multi-AZ configuration
- Security
- Authentication via OAuth 2.0/OIDC, role-based access control for staff and venues
- Encryption in transit (TLS 1.2+) and at rest (KMS for RDS/S3)
- Secrets stored in AWS Secrets Manager / SSM Parameter Store
- Network isolation with VPC private subnets and least-privilege IAM policies
- Optional AWS WAF for L7 protection and rate limiting
- GDPR-ready features: user data export/delete, consent logging, audit trails
Key Takeaways
Key takeaways
Analytics That Pay Rent
Booking data turned out to be as valuable as the bookings. Heatmaps of table popularity and peak-hour patterns now drive seating layouts and staffing decisions, and post-visit surveys feed a steady loop of service fixes that lifted satisfaction scores 20% quarter over quarter.
Key takeaways
Scalability
New venues onboard with minimal setup, from a single bar to a chain. Load balancing keeps the Friday dinner rush from being a stress test.
Key takeaways
Operational Efficiency
Reconciling payments, adjusting seating plans, and updating availability used to be manual chores. They now happen in real time, which saves staff hours and removes the retyping errors that used to slow service down.
Ready to optimize your table bookings
and floor management?
Contact LI Solutions to implement LetsBAR today!
Contact us