Reliability engineering, not “support”
We operate software and AI systems with measurable baselines, controlled change, and a prevention-first mindset. The goal is fewer incidents, clearer behavior, and safer iteration.
Operations scope
Platform & product ops
- Stability work: bug fixes, regression control, reliability improvements
- Performance: latency tracking, cost profiling, targeted optimization
- Security hygiene: dependency updates, secrets practices, safe defaults
- Resilience: backups, recovery checks, auditable change history
- Observability: logs, metrics, tracing, alerting tuned to what matters
AI ops (where it gets real)
- Drift monitoring + periodic quality checks against evaluation sets
- RAG health: retrieval accuracy, indexing freshness, source coverage
- Guardrails & policy tuning based on real usage + failure modes
- Model/version rollouts with rollback paths and tracked changes
- Eval regressions: prevent “it got worse” quietly shipping to users
This is maintenance as a discipline: observe → measure → improve → prevent recurrence.
Operational baselines
SLOs
Targets for uptime, latency, and error rates
Response
Same-day critical triage + tracked incident timeline
Change
Release notes, approvals, rollback-ready deployments
Visibility
Weekly report: shipped work, risk, next priorities
Engagement models
- Retainer: stable capacity for ongoing improvements
- SLA: production-critical systems with response guarantees
- On-call: incident readiness + postmortems + prevention
Optional: a short stabilization phase to establish baselines before steady-state ops.
Need this in production?
Share your system context, constraints, and risk profile. We’ll propose a stabilization plan and an operations model that matches reality.