Cloud architecture for strategic partner deals: $60M+ closed, $200M+ influenced

The Setup

Where the work started

An AWS partner ecosystem needed scalable architecture review delivery across mid-market and enterprise accounts. Generic reviews — checklists, screenshots, thirty-page PDFs — were not moving the needle. Findings were landing as shelfware.

Partners needed technical architecture support credible enough to close strategic deals and retain accounts, with cost and risk findings that executive buyers could actually act on. The portfolio spanned enterprise buyers (D.R. Horton, Warner Bros.) and AI-native teams shipping production GenAI.

What I Did

The architecture

Applied the AWS Well-Architected Framework's six pillars per engagement. Treated every finding as a delivery contract with the customer's engineering team, not a slide in a deck. On AI-native deals, pushed further into GenAI architecture — retrieval, inference, evaluation, and cost attribution — as a first-class pillar of the review.

1. 01

   Pillar-based review with direct evidence

   Each pillar review produced concrete evidence: IAM policy extracts, VPC topology diagrams, cost attribution reports, incident history. No finding left the engagement without an artifact behind it.

2. 02

   Prioritization by blast-radius × effort

   Findings were ranked by likely blast radius (security, reliability, cost) multiplied by remediation effort. High-impact, low-effort items ran first. Shelfware died early.

3. 03

   Business-language executive summaries

   Every review produced a separate executive summary written for the business sponsor — no jargon, no screenshots. Dollar impact, risk exposure, and a three-decision recommendation. Technical detail lived in a paired engineering document.

4. 04

   GenAI architecture as a first-class pillar

   For AI-native customers, extended the review into retrieval topology, inference cost per request, evaluation posture, and safety boundaries. Gave AI buyers the same caliber of review their traditional workloads were already getting.

5. 05

   Reusable remediation playbooks

   Built a library of playbooks across common remediation patterns: multi-account landing zones, IAM segmentation, VPC rearchitecture, cost-attribution tags, data-lake governance. Partner teams used them to finish what the review started.

6. 06

   Named remediation owners per finding

   Each prioritized finding left the engagement with a named owner on the customer side. No anonymous handoffs. Remediation completion rates moved accordingly.

Outcome

What actually happened

$60M+ in partner contracts closed, $200M+ in customer cloud spend influenced, and cost reductions ranging from 20% to 70% delivered across engagements over 38 months in seat.

$60M+

Partner contracts closed

$200M+

Customer revenue influenced

20–70%

Cost reduction range

38 months

Time in seat

• Typical engagement produced 30–45% cloud cost reduction within six months of remediation.
• Average review-to-remediation cycle shortened to roughly six weeks.
• Remediation completion rate held above 75% inside six months post-engagement — well above the industry norm.
• Review templates and GenAI playbooks became standard delivery artifacts across the partner organization.

Why it matters

The parts another team can take

• Tie every technical finding to a dollar or risk number. Otherwise it will not get prioritized, no matter how correct it is.
• Write two documents — executive and engineering — not one hybrid. Different readers, different decisions, different failure modes.
• GenAI deserves the same architectural discipline as any other workload. Retrieval, inference cost, and evaluation posture are first-class, not afterthoughts.