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Telemetry & Observability

Bike4Mind is instrumented with robust, real-time telemetry that provides insight into system behavior, user interaction, feature usage, and platform health. Observability is treated as a first-class concern and is embedded into the architecture from the infrastructure layer through to the user interface.

Built-In Platform Telemetry​

Every instance of Bike4Mind ships with integrated metrics collection and analysis tools:

  • Over 40 tracked telemetry points across:

    • Authentication and session behavior
    • File uploads and ingestion events
    • Prompt and agent usage
    • Project creation and sharing
    • Dashboard interactions
    • System errors and exception rates

  • Real-time event tracking is performed natively within the platform and is environment-aware (dev/staging/production).

Daily and Weekly Reporting​

  • Summaries are generated and sent to internal Slack channels and email:

    • Daily Reports: Highlight authentication patterns, usage counts, ingestion trends, and top-used features
    • Weekly Reports: Include 7-day trend visualizations, growth rates, and engagement comparisons
    • GenAI Summaries: Each report is accompanied by a generative AI-produced TL;DR to surface actionable insights quickly

These summaries provide engineering and product teams with high signal, low noise updates on platform behavior.

Internal Dashboards​

Bike4Mind includes a self-contained analytics dashboard that eliminates the need for third-party BI tools:

  • User insights:

    • Browser, OS, screen resolution
    • Session durations, login frequency
    • Feature-level interaction metrics

  • System insights:

    • API response time distributions
    • Error rate trends
    • External service latency (e.g., LLM API calls)

  • Dashboards are role-scoped and accessible by admins and product leads directly within the platform.

Logging and Metrics Aggregation​

  • Application logs include:

    • User and session context
    • Route-level tracing
    • Permission check failures
    • Unexpected input payloads (without sensitive data)

  • Aggregated metrics are emitted using:

    • AWS CloudWatch Logs
    • SST Observability (for environment-aware logging)
    • Optional integration with external APM tools like Datadog or Sentry (enterprise-only)

Tracing and Dependency Monitoring​

  • AWS X-Ray is used to trace service calls:

    • Lambda cold start performance
    • MongoDB query times
    • External API interactions (e.g., LLM calls, Google APIs, Auth providers)

  • Trace analysis is regularly reviewed during incident post-mortems and performance audits

Alerting and Anomaly Detection​

  • Alerting thresholds are defined for:

    • Unusual spikes in error rates
    • High latency in ingestion or generation tasks
    • Failing third-party integrations

  • Alerts are sent to Slack channels and email lists based on environment and severity

  • Alert payloads include direct links to affected logs, traces, and service dashboards

Goals and Outcomes​

Telemetry is not only used for troubleshooting but directly feeds into:

  • Feature prioritization (adoption data)
  • Customer success health scoring (based on engagement metrics)
  • Operational excellence KPIs (e.g. MTTR, deployment success rates)

This telemetry system ensures that development, operations, and leadership teams have constant visibility into the platform's behavior and performance.