Define camera sources, detection targets and alert KPIs in a one-hour session.
Train or fine-tune custom Rekognition models; validate accuracy on a sample set.
Ingest live or batch video, run detections, push flagged frames to S3 and alerts to Slack, SMS or your SIEM.
Add new classes, tune confidence thresholds and surface analytics in QuickSight dashboards.
Plant Manager, SafeSteel
97 percent average detection accuracy
4× faster incident response compared with manual monitoring
Slow, inconsistent media generation and no way to scale high-quality images or short-form video threatened Photozig’s competitive edge.
Avahi built an AWS-native workflow: Bedrock-powered prompt enhancement plus GPU-accelerated diffusion and video models deployed through Lambda, API Gateway and EC2 L40S instances.
100 % AWS-native stack, zero third-party SaaS dependencies
6+ AI models (Nova Canvas, SD 3.5, Flux, AnimatedDiff, etc.) integrated for image & video generation
Supports 3+ resolutions (1024×1024, 864×1536, 1536×864) for multi-format delivery
GPU-optimized L40S instances deliver high-throughput inference and future-proof capacity
Entire platform designed, built and production-ready in 8 weeks
Yes. Amazon Rekognition Custom Labels lets us upload labeled images to detect domain-specific items.
Most common codecs (MP4, MOV, MKV, HLS) and real-time RTSP or WebRTC streams.
Frames stay in your AWS account with encryption at rest and in transit, plus IAM-based access control and audit logging.
