Maintenance
Reliability engineering, not “support”
“Support” is reactive. Reliability engineering is prevention-first: clear baselines, controlled change, measurable quality, and incident response that actually improves the system instead of producing more meetings.
SLOs + Dashboards
uptime, latency, errors, cost
Controlled Change
release notes, rollbacks, approvals
Prevention Cycle
observe → measure → fix → prevent
What we operate
Platform & product ops
- Stability: 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 user impact
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 from real usage + failure modes
- Model/version rollouts with rollback paths and tracked changes
- Eval regressions so “it got worse” doesn’t ship silently
What you get every month
Delivery artifacts
- Weekly change log: shipped work, risk notes, next priorities
- Incident reports with timelines, impact, and corrective actions
- Postmortems that produce prevention items (not blame theater)
- Runbooks + on-call notes that reduce MTTR over time
Quality guardrails
- Regression suite updates when new failure modes appear
- Alert tuning to reduce noise and increase signal
- Release gating for high-risk changes (AI + infra)
- Cost ceilings and performance budgets, tracked and enforced
Maintenance as a discipline: observe → measure → improve → prevent recurrence. That’s the job.
Baselines & guarantees
SLOs
Targets for uptime, latency, error rate, and cost
Response
Same-day critical triage + tracked incident timeline
Change
Release notes, approvals, rollback-ready deployments
Visibility
Weekly report: what shipped, what changed, what’s next
Recovery
Backups + restore verification (RTO/RPO aligned)
Engagement models
- Retainer: steady capacity for continuous improvement
- SLA: production-critical systems with response guarantees
- On-call: incident readiness + postmortems + prevention work
Optional: Stabilization Sprint
A short baseline phase to set SLOs, dashboards, alerts, evals, and the initial hardening plan.
Production systems deserve grown-up ownership
If your stack includes AI, retrieval, or integrations that can fail in creative ways, we’ll help you measure quality, reduce incidents, and ship changes safely without slowing delivery to a crawl.
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.