Tools
Tools we build, polished enough to survive daylight
The portfolio is intentionally compact on the page: short cards first, full project briefs on click. Less clutter, more signal, fewer scroll-induced philosophical incidents.
Local-first where it mattersReceipts over hand-wavingFast, inspectable workflowsBuilt for real operators
Want one of these built for your environment?
We can adapt these capabilities to your stack, data, constraints, and compliance posture. You’ll get measurable acceptance criteria, a deployable system, and artifacts your team can maintain.
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.