Part of the DEPLOY Method — Launch phase
The AI pilot you shipped last quarter is doing what pilots are supposed to do — real users, real outcomes, real feedback — and the next commitment it has to carry is bigger than what the current system was built for. An enterprise launch, a public sector go-live, an SME multi-site rollout, a Series A raise — each of these is a commercial stage gate that exposes gaps the pilot could tolerate and the production system cannot. This is the LAUNCH phase of the DEPLOY Method: a 12-week embedded engagement that takes a working pilot through readiness assessment, evaluation and observability, security and compliance, and scaling readiness. The work is not glamorous and it is rarely what your engineering team wants to spend a quarter on — but it is what separates organizations that convert pilots into production systems from organizations that keep piloting forever. I architected Auralink — 1.7 million lines of production code, roughly 20 autonomous agents resolving 78% of incidents without human intervention, peer-reviewed on arXiv — and I've shipped eight AI ventures to production. I've also advised more than 30 AI startups as a Berkeley SkyDeck mentor through exactly this transition. The failure patterns repeat, the fixes are known, and the sequence matters.
The evaluation practice that got you to pilot cannot answer 'did this model update improve or regress.' You evaluated during development with spot checks and a small validation set. Production evaluation is a different discipline — structured eval suites, regression tests, statistical methodology, objective quality baselines. Without it, every model change becomes a gamble. The first time a key stakeholder — an enterprise customer, a regulator, a ministerial sponsor, a board member — asks you to prove the system got better, you will not have an answer, and the stage gate stalls.
You find out your system is broken when a stakeholder tells you, not when a dashboard does. You have no AI-specific observability: no latency distributions under real load, no model drift detection, no cost-per-request tracking, no alerting on the failure modes that matter. Every incident becomes a forensics exercise measured in days. In a pilot this is tolerable because everyone is in the same room. In production it is unacceptable because the commitment is bigger and the room is larger.
SOC 2, GDPR, PII defense, and audit trails are aspirational and your first production commitment will not be forgiving. The enterprise procurement team wants a SOC 2 report. The public sector sponsor wants a data residency statement that holds up. The EU AI Act risk classification is waiting. The PII redaction strategy that worked during pilot is not documented in the form a compliance reviewer will accept. These are invisible until you are three weeks into a procurement conversation or an audit that is about to fall through — and at that point the work is measured in quarters, not sprints.
Your inference path has never been load-tested at realistic concurrent traffic. You do not know whether your bottleneck is model serving, vector database throughput, LLM provider rate limits, or your own backend. The pilot ran at pilot scale. The commercial rollout will multiply that load by five to fifty times depending on the commitment. The first time you hit real concurrent traffic you will find out which layer breaks — and you will find out in front of the people whose approval the stage gate depends on.
The engagement runs in four three-week phases. I work embedded with your team — your engineers build, I bring the readiness ranking, the eval methodology, the compliance sequence, and the scaling tests I've run on my own production systems and on 30+ AI startups through exactly this transition. The goal is not to rebuild what works; the goal is to harden it into a system that clears the next commercial stage gate with evidence, not with hope.
I go deep on your current system — code, infrastructure, data pipelines, evaluation practice, security posture, observability, operations readiness. I produce a written readiness assessment ranked on four tiers: stage-gate blockers (fix now, the next commitment fails without it), operational blockers (fix this quarter, you cannot operate at commercial scale without it), scaling risks (fix before you multiply load), and polish (fix when capacity allows). Each item has an effort estimate and an owner suggestion. This is the same work a sophisticated technical due diligence or an enterprise procurement review would surface — but produced from an ally, not an adversary.
The two systems your pilot is missing and your production version cannot live without. I build a structured evaluation pipeline with baselines, regression tests, and objective quality metrics — so your team can ship model updates with measurable confidence instead of crossed fingers. We stand up AI-specific observability: latency distributions, token accounting, cost-per-request, model drift detection, and the dashboards your operations team will read during incidents. These become your team's daily tools, not a deliverable I hand off and leave.
Audit logs, access controls, prompt injection defenses, PII handling, data retention policies, and the documentation trail that SOC 2, GDPR, or EU AI Act reviewers will actually accept. For public sector engagements, the residency and sovereignty story is built into the architecture, not bolted on. For regulated industries, the evidence chain matches the regime your compliance lead is working to. Done right, this phase takes three weeks and gives you a durable foundation. Done wrong — which is what happens when it starts two weeks before a procurement deadline — it becomes a six-month compliance death march during your most important commitment. We do it right the first time.
Realistic load patterns based on the commitment you are about to make — the enterprise rollout size, the public sector user base, the SME multi-site footprint, the growth curve in the Series A deck. We find the bottlenecks — model serving saturation, vector database throughput, LLM provider rate limits, backend coupling — and we fix the ones that would bite you at the load you will actually see. We document the ones you are choosing to accept and the signals your team should watch for when those trade-offs start to matter. Your scaling inflection does not have to be a surprise.
Enterprises preparing an AI product launch or a first major customer deployment. Public sector bodies approaching a go-live with ministerial or regulatory visibility. SMEs rolling a successful pilot from one site to a multi-site footprint. AI-native startups heading into a Series A with a diligence process that will examine production readiness. Any organization where a pilot has real users, a commercial stage gate is on the calendar, and the team knows the current system was not built for what is coming. This is not for teams whose 'pilot' is a notebook — those organizations need the Strategy Sprint or the Readiness Audit first. It is also not for organizations without engineering capacity to embed with the engagement; the handoff model assumes a team that will own the system after week twelve.
Because the pilot was built for pilot load, pilot users, and pilot tolerance. The commercial commitment coming — whether that is an enterprise launch, a go-live, a multi-site rollout, or a fundraise — multiplies the load, raises the tolerance bar, and adds reviewers who will not accept 'it works in testing.' About a third of the pilots I assess in week one turn out to be closer to production-ready than the team thought, and in those cases the engagement focuses on the specific gaps rather than the full program. I will tell you honestly in week three whether the full program is warranted.
The startup service is calibrated to Series A diligence, enterprise customer procurement, and the specific failure modes of AI-native startups. This service is the same methodology generalized across enterprise launches, public sector go-lives, SME multi-site rollouts, and startup fundraises. If you are a pre-Series A AI-native startup, the startup-specific version is the better fit because the language and the deliverables are calibrated to your stage gate. Every other audience should use this one.
Yes, and I often do. Your SI owns the integration layer, the enterprise data plumbing, the change management, or whatever scope they were engaged for. I own the AI-specific production readiness — eval, observability, AI security, inference scaling. We meet weekly so the work products reconcile and the handoff to your operations team is clean. I have done this alongside large SIs and specialist firms; the boundary works when both sides respect scope.
Then this is probably not the right engagement. Pilot-to-Production Hardening is a production-readiness program, not a model improvement program. If your pilot's model quality is the problem, you need the Domain-Expert LLM Lab. If your pilot's architecture is the problem — especially for multi-agent systems — you need Agentic System Engineering. I make that call honestly in week one. Running a readiness program on a system whose underlying model or architecture is the bottleneck is a waste of your budget.
For the pilot-to-production scope, yes, to the level required by your risk classification. For limited-risk or minimal-risk systems, the compliance work fits inside the security and compliance phase. For high-risk systems, the EU AI Act work is substantial enough that it runs alongside this engagement with its own workstream. I scope that in week one based on your classification and will be explicit if the compliance burden warrants a separate engagement rather than stretching this one.
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