Resources and blog
Starter explainers, incident playbooks, and architecture notes for teams evaluating observability platforms, AI observability, retrieval drift, system drift detection, OpenTelemetry, and SRE tooling.
A practical guide to monitoring production AI through prompt hashes, retrieval metadata, latency, fallback, escalation, and drift signals without exporting raw sensitive content.
A practical playbook for detecting retrieval drift through source coverage, no-source rate, fallback behavior, latency, and answer quality after knowledge-base or ranking changes.
A practical playbook for turning jailbreaks, grounding failures, and policy misses into runtime controls, eval reruns, alerts, and audit evidence.
A practical release checklist for production AI teams covering prompt, model, retrieval, guardrail, eval, cost, latency, and human-review evidence.
A practical guide to mapping NIST AI RMF work into production AI telemetry, evals, guardrails, drift evidence, and operator review.
A practical guide to AI observability covering prompt drift, model drift, retrieval quality, guardrail evidence, eval results, cost, latency, and incident-ready operations.
A practical definition of observability for engineering teams that need to understand production systems through logs, metrics, traces, alerts, incidents, and change context.
How logs, metrics, and traces differ, when to use each signal, and why production teams need all three for reliable incident detection and response.
A plain-language guide to system drift, how it appears in production telemetry, and how deterministic drift detection can help teams find issues before they become incidents.
A practical incident management guide for reducing mean time to recovery by connecting telemetry, alerts, ownership, timelines, and change context.
How engineering and SRE teams can detect production incidents earlier by combining observability, system drift detection, OpenTelemetry context, and incident management workflows.
Executive evaluation
Walk through deployment posture, baseline evaluation logic, audit evidence, drift detection, hallucination-risk controls, and the operating record required for regulated AI systems.