Part of the DEPLOY Method — Optimize phase
Shipping an AI system to production is the moment the real work starts. Every week in production surfaces something your pre-launch work did not anticipate: a latency distribution that degrades under real user patterns, an eval regression from a base-model update you did not know shipped, a cost line that doubles on a Tuesday when somebody rolls out a feature that triples tokens per request. Most teams either hire a full-time AI SRE they cannot afford at their stage, or they do the work reactively, which means they do it during incidents instead of before them. This is the OPERATE phase of the DEPLOY Method packaged as a recurring engagement. I run this exact discipline on my own production systems, including Auralink — 1.7 million lines of code, ~20 autonomous agents, the observability stack that lets ~78% of incidents resolve without human intervention. The patterns I have built to keep my own systems healthy are the patterns I apply to yours — the retainer is how a team gets an experienced AI SRE without the full-time headcount, and without the inevitable incidents that come from deferring the work.
You find out the model regressed when a customer complains. There is no structured eval running on a recurring cadence against the production distribution, so when the upstream base model updates or a fine-tune gets promoted or retrieval content shifts, quality moves silently. By the time someone notices, you have already lost trust with the user who filed the ticket and the three users who churned without filing one. Eval regression detection is straightforward engineering; what is missing is the discipline to run it every day and the person whose job it is to respond.
Your inference costs are surprising you monthly, not weekly. Nobody is tracking cost-per-request at the feature level, which means the expensive features subsidize the cheap ones invisibly. A feature launch triples average tokens per session and nobody notices for six weeks, at which point the CFO asks why the bill moved 40% and your team needs a week to reconstruct the answer. FinOps for AI is its own discipline — usage attribution, cost allocation by feature, unit economics per cohort — and it is not the same as your cloud FinOps program.
On-call for AI systems is a pattern your team has not built. Your incident response was written for web services; it does not cover the AI-specific runbook for model drift, guardrail failures, retrieval degradation, or a frontier provider pushing a breaking change. When an AI incident fires at 2am, your on-call engineer is doing first-principles reasoning under pressure instead of executing a runbook. That is how 30-minute incidents become 4-hour incidents, and how 4-hour incidents end up in the postmortem everybody reads.
The quarterly review that should catch upstream change does not happen. A frontier provider deprecates a model endpoint with 90 days' notice, an open-weight release renders your current fine-tune a generation behind, or an upstream API pushes a silent performance regression — and the recurring review pass that would catch these within days is always lower priority than the next feature. Model choice, fine-tune recency, quantization settings, hardware class, and retrieval strategy each benefit from a scheduled quarterly look from someone with pattern recognition across other systems. Teams that run this discipline well catch upstream change early and plan the response; teams that do not catch it during incidents, after the accumulation of deferred decisions has already become the main thing slowing them down.
The retainer is structured around a recurring cadence — weekly, monthly, and quarterly rituals — plus on-call response for incidents that exceed your team's in-house capability. The first month builds the instrumentation; every subsequent month runs the discipline. You own the dashboards, the runbooks, and the eval pipeline; I am the experienced set of hands that keeps them sharp.
I audit your existing production AI stack — observability, eval, cost tracking, incident response — and build out what is missing. Dashboards for latency distributions, error rates, cost-per-request at the feature level, token usage by cohort, retrieval hit rates, and model-specific signals. Alerts calibrated to real incident thresholds, not generic defaults. The baseline eval harness running against production distribution on a daily cadence. By end of month one, the instrumentation is in place and owned by your team — the retainer runs it, your team can also run it.
Each week: review the eval dashboard, investigate any regression signal, review cost-per-feature deltas, flag anomalies that need engineering response. Each month: a written report covering production health, cost trend, eval trajectory, model-class changes from providers that affect you, and the one or two recommendations that matter most for the next month. On-call is shared — your team handles routine operations, I respond to incidents that exceed the runbook or involve novel AI-specific patterns.
Once a quarter: the structured review your team would otherwise skip. Is the base model still the right choice given what has shipped in the open-weight ecosystem? Is the fine-tune recent enough to matter? Is the quantization tier still optimal for your cost profile? Is the hardware class still the right call? Are the retrieval strategies and prompt templates still earning their keep? The output is a written memo with recommendations and the reasoning, which feeds your engineering backlog.
The retainer has a defined exit from day one — either your team builds the in-house AI SRE capability and graduates to owning the discipline, or the system matures to the point that a lower-touch retainer is sufficient. I document the playbooks, the decision log, and the institutional memory so handoff is clean. Six-month minimum; no long lock-in; the goal is that your team eventually does not need me on retainer, which is the opposite of how most consulting engagements are structured.
Enterprises and growth-stage startups with AI systems live in production carrying real user traffic and real cost, but without a full-time AI SRE function to keep them healthy. Teams where the head of engineering knows the observability and eval gaps exist and does not have the hiring velocity or the specialist pattern recognition to close them internally. Organizations that have shipped a pilot or an initial production system and now need the operating discipline that carries it through the first year of production traffic. This is not for teams in early development — pre-production AI needs the Pilot-to-Production Hardening engagement first, and the retainer picks up from there. It is also not a replacement for the on-call engineering rotation your team already runs; it is a specialist layer that sits alongside your existing SRE practice and covers the AI-specific surface your generalist rotation does not have the depth to cover.
Price and pattern recognition. A competent full-time AI SRE is expensive, slow to hire, and rare — the market for specialists who have actually run production AI at scale is small and contested. The retainer gives you an experienced set of hands on your system at a fraction of the cost, and more importantly, pattern recognition from systems other than yours. When a problem appears that your team has not seen before, I have usually seen it before on a system that looked different. When your team is ready to hire a full-time AI SRE — and they eventually will be — the retainer is a better bridge than running the discipline reactively in the meantime.
Shared on-call with your team. Your team handles the routine production operations, the ones your runbooks cover, and the incidents where a general engineering response is sufficient. I respond to incidents that exceed the runbook — novel AI-specific patterns, eval regressions from upstream changes, cost anomalies that require attribution work, frontier provider incidents where the response requires pattern recognition from other systems. Response time is hours, not minutes, which is appropriate for the kind of incident that lands in my queue rather than your on-call rotation. The retainer is not a 15-minute pager; it is the specialist you escalate to when the generalist runbook runs out.
Possibly yes, in which case I will tell you in the initial conversation. The retainer makes economic sense when you have AI systems in production carrying enough traffic and enough cost that the observability and eval discipline pays for itself. For very small footprints the right service is usually a Pilot-to-Production Hardening engagement, which leaves your team with the dashboards, runbooks, and eval pipeline; you run them yourself and bring me back as a retainer later if the system grows into that need. I would rather route you to the right engagement than sell you a retainer that does not earn its keep.
Yes. Technical interviewing, scorecard design, reference checks with candidates I know from the network, and sometimes an introduction to someone actively looking. The retainer naturally winds down as your in-house capability matures, and the hiring support is part of that handoff. I have a direct interest in you eventually not needing me on retainer, because the consulting relationships that compound are the ones where the client's capability grows faster than their dependency.
Yes, and I prefer to. Your SRE and platform team owns the general production discipline — deploys, rollbacks, infrastructure, incident response patterns. I am a specialist layer focused on AI-specific surfaces: eval, drift, inference cost, retrieval quality, upstream provider changes, model refresh cadence. We share the same dashboards, the same alerting system, and the same on-call tooling where that makes sense. I explicitly do not try to replace or relitigate the decisions your existing SRE team has made. The retainer works because of that discipline, not around it.
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