Baiqa System

The Challenge

Facilities with dozens of IP cameras often rely on manual monitoring, which is expensive and error-prone. The client needed an AI-driven system that detects critical events and surfaces them immediately.

• Manual monitoring misses incidents during high-volume periods
• Need for real-time detection across multiple camera streams
• Event classification, search, and alerting requirements
• Reliability constraints for 24/7 operations

Note: This was a confidential engagement, so we keep sensitive implementation details and client specifics private.

Our Solution

We built Baiqa System as an end-to-end computer vision pipeline: ingest RTSP streams, run real-time detections, aggregate events, and present actionable insights in a dashboard.

• IP camera ingestion (RTSP) with stream health monitoring
• Real-time detection and event triggers (alerts + event timelines)
• Identity & access foundations for secure multi-user operations
• Searchable event history for audits and investigations
• Deployment patterns for facility environments

Challenges We Overcame

• Stream variability: Handling different camera models, bitrates, and lighting conditions
• Low-latency inference: Optimizing inference to keep alert delays minimal
• False positives: Improving precision via tuning, thresholds, and targeted training
• Operational reliability: Building health checks and restart strategies for 24/7 uptime

Technology Stack

Results & Impact

• Faster incident response through real-time alerts
• Reduced reliance on continuous manual monitoring
• Improved visibility with searchable event timelines
• Foundation for adding new detections as operational needs evolve

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