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Services provided
● Backend architecture design and implementation
● Event-driven system development for AI video streams
● Real-time data ingestion and processing
● Integration with access control and CRM systems
● Observability setup (monitoring, logging, metrics)
● CI/CD and infrastructure automation
● GDPR compliance and data privacy configuration
Client's pain
The client managed a parking and perimeter security platform across several EU and UK locations. Each AI-powered camera detected vehicles and recognised license plates, but the legacy backend couldn’t handle the growing load.
Key issues:
→ Events were occasionally missed or delayed, leading to failed gate operations
→ Latency spikes created unreliable automation during peak hours
→ No proper event audit trail or system observability
→ Limited scalability as the number of connected cameras expanded
→ Compliance gaps with GDPR and data retention policies
They needed a reliable, scalable, and transparent event-processing system capable of managing thousands of AI-generated events per minute.
Solution offered
Patternica provided a Senior TypeScript developer under an outstaffing engagement to take full ownership of the backend domain. He rebuilt the platform around a cloud-native, event-driven microservices architecture designed for speed, resilience, and observability.
Core solution components:
Event processing architecture
✓ Modular microservices built with NestJS + RxJS
✓ AWS SQS/SNS queues to decouple ingestion and processing
✓ Fargate containers for scalable runtime environments
Smart rules engine
✓ Access logic based on vehicle allowlists, time windows, and detection confidence
✓ Automated responses for gate opening, alerting, and data logging
Monitoring & auditability
✓ Integrated Prometheus + Grafana dashboards for real-time performance metrics
✓ Full event lifecycle traceability to guarantee transparency and accountability
Security & compliance
✓ GDPR-aligned data handling
✓ Centralised audit logging and privacy-aware retention logic
DevOps enablement
✓ Automated infrastructure with Terraform
✓ CI/CD pipelines via GitHub Actions for continuous delivery
Results
| KPI | Before | After |
| Event reliability | ~85% | 99.95% confirmed delivery |
| Event latency | 5–6 sec avg | <1.5 sec under full load |
| Manual incident checks | Frequent | Reduced by 60% |
| Scalability | Limited | 10× more cameras supported |
Impact highlights:
Cameras now trigger automated gate operations in under two seconds
All events are fully logged, traceable, and compliant
Maintenance costs reduced through automation and monitoring
Timeline
Month 1: Architecture review, backlog setup, AWS infrastructure configuration
Month 2–3: Event ingestion & rules engine development
Month 4–5: Integration with access control & CRM systems
Month 6: Monitoring setup, load testing, and go-live
Key takeaways
— Event-driven architecture dramatically improves real-time automation
— Strong observability is essential for maintaining trust in AI-powered systems
— Outstaffed senior developers can integrate seamlessly and deliver enterprise-level outcomes
— Proper DevOps and CI/CD pipelines ensure long-term scalability and stability
used technologies
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