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Advisory Catalog

AI infrastructure services without the bloated catalog

Five service tracks answer the real question: how to make systems faster, safer to scale, and cheaper per useful transaction as AI increases load and spend.

Engagement optionsBook 20 min

AI infrastructure advisory services

A focused catalog for teams that need speed, scalability, reliability, and cloud efficiency under one operating model. AI is used where it helps: anomaly detection, signal summarization, scenario planning, cost intelligence, and incident routines.

Five service tracks

AI infrastructure control, performance revenue audit, launch readiness, SRE/incident intelligence, and fractional systems advisory.

Business value

  • Turns infra telemetry into a weekly decision system for cost, risk, and capacity.
  • Makes AI and non-AI workload spend visible by service, environment, and owner.
  • Replaces one-off cost cuts with guardrails that keep waste from coming back.

What is delivered

  • AI-assisted anomaly, idle-capacity, and rightsizing review.
  • Cost-per-request and cost-per-inference baseline.
  • Budget, alert, and scaling guardrails for critical workloads.
  • Leadership control memo with owners and review cadence.

Business value

  • Connects speed improvements to revenue, retention, and paid traffic efficiency.
  • Shows which bottlenecks deserve engineering time and which are noise.
  • Creates performance budgets that survive new features and design updates.

What is delivered

  • Critical journey map across LCP, TTFB, API latency, errors, and saturation.
  • Revenue-impact ranking for frontend, backend, DB, cache, and third-party issues.
  • Performance budget for CI/CD and release review.
  • Executive summary modeled after report-style evidence and recommendations.

Business value

  • Finds capacity limits before customers or partners discover them.
  • Gives product and engineering a clear Go / No-Go frame.
  • Controls the cost of scale instead of treating bigger bills as inevitable.

What is delivered

  • Traffic, queue, dependency, and inference-load scenario design.
  • Spike, stress, soak, and failover validation plan.
  • Release thresholds for latency, errors, saturation, and unit cost.
  • Launch decision packet with rollback and guardrail recommendations.

Business value

  • Cuts operational noise so humans focus on the failures that matter.
  • Uses AI to summarize signals, suggest next checks, and keep runbooks current.
  • Improves recovery routines without hiding accountability behind automation.

What is delivered

  • Golden Signals, RED/USE, SLO, alert, and escalation-path review.
  • Incident pattern clustering and runbook gap analysis.
  • AI-ready incident brief template for on-call and leadership.
  • Postmortem follow-up model tied to backlog owners.

Business value

  • Adds senior systems leadership without hiring a full-time specialist.
  • Keeps AI, infra, product, and finance decisions in the same operating rhythm.
  • Improves prioritization when every team wants capacity, speed, and lower spend.

What is delivered

  • Weekly risk, roadmap, and unit-economics review.
  • Backlog governance by business impact, risk, and implementation effort.
  • Architecture and vendor trade-off support.
  • Leadership reporting for launches, incidents, and cloud spend.

Method stack

Click any item to open quick context and official source links.

Need the right scope for your AI / infra pressure?

Start with the fast request if the pain is clear. If pressure is spread across speed, incidents, scale, and spend, the short intake call is cleaner.

See optionsOpen fast request