Case StudyJammetry
Orchestrating a highly observable sales data extraction and AI pipeline.
Jammetry requires processing heavy, complex sales datasets with AI automation. The architectural challenge was ensuring this high-throughput pipeline remained transparent, resilient, and observable at every step.
Overview
Shining a light on complex backend automation.
When data extraction pipelines fail, they often fail silently. For Jammetry, I designed the infrastructure to ensure that every AI operation, data transformation, and API call generated precise telemetry.
By embedding observability directly into the architecture, teams can proactively identify bottlenecks and track the exact health of the sales extraction logic.
Observability First
I instrumented the entire backend using OpenTelemetry, effectively neutralizing "black box" AI operations. Every document ingestion and data extraction traces through the system, logging memory usage and execution time.
This telemetry is piped directly into Prometheus and visualized through custom Grafana dashboards. If a specific AI model starts timing out, or if the queue backs up, the platform alerts the team immediately—long before the end user feels the impact.
Outcome
- Implemented comprehensive telemetry using OpenTelemetry, giving unprecedented visibility into the AI extraction pipeline.
- Designed Prometheus and Grafana dashboards to monitor latency, extraction success rates, and API limits in real-time.
- Scaled the system to process massive datasets without dropping concurrent requests.