The Intelligence System—Case Study
As Director of Strategy & Growth at Monumental, a Shopify agency
Turning scattered client knowledge into compounding strategic intelligence.
The Challenge
While working at Monumental, I identified a recurring failure point in client services:
Strategic context — everything learned in meetings, audits, and data reviews — lived in scattered notes and individual memory, and evaporated when accounts changed hands or time passed between touch points.
Business reviews were being rebuilt from scratch each quarter, taking 9+ hours of deck work, and the insight that made a client feel understood in March was gone by June.
Objectives
Convert every client interaction into durable, compounding strategic intelligence — so each new conversation builds on everything that came before.
Cut business-review and brief prep time without cutting depth.
Build a verification layer that catches AI overconfidence before it ever reaches a client.
Snapshot
Industry: Ecommerce growth agency • Shopify Plus client portfolio
Role: Director of Strategy & Growth • system design • prompt engineering • quality assurance • portfolio rollout
Tech: Claude • Shopify Plus • ClickUp
Duration: November 2025 – February 2026, then in active use across the portfolio
Results: $14M+ misattributed channel revenue surfaced · reviews 9h → 3–4h (~100–150 hrs/yr saved portfolio-wide) · briefs 2–3h → ~30 min · 8 client accounts
Services: System design • Iterative prompt engineering • Domain-expert QA • Live validation • Templatized for scale
What We Delivered
One Prototype → A Repeatable Framework
It started as a single hand-built prototype for one client in November 2025: one long conversation, three meeting transcripts, and a business-review deck, producing a master strategic document plus four supporting ones. Within a day, I generalized it into a repeatable framework.
A Fixed 11-Section Schema
The second deployment forced unification of two diverging prototypes into one fixed 11-section schema — the point where it became a system rather than a one-off document.
A Business-Model Feframe
One engagement proved the system could turn 22 source documents and a 3-year sales export into a client-ready 20+ page strategic document in a single session — and surfaced a reframe: the client's site was a research channel feeding 85% in-store revenue, not an underperforming sales engine.
Scaling In Both Directions
The system extended to a brand-new relationship, generating institutional memory before the engagement even started — and adapted for a lean, low-budget client, proving the model scaled down as well as up.
The Verification Discipline
The system's most defensible piece. I caught the AI's first-pass analysis fabricating a "$25 first order / $12 profit" crisis narrative built on an unrepresentative 9–20% cohort sample — the real number was closer to $52. That correction became a standing rule: benchmark every claim, check sample representativeness, and log every correction before it reaches a client.
The Growth Playbook
By February 2026, the system matured into nine scored, productized 90-day growth initiatives, ranked by a weighted rubric (revenue impact, effort/ROI, client readiness) and delivered as one-page, yes/no-able recommendations — turning the intelligence directly into a revenue-generating deliverable.
The $14M Finding
I applied the same scrutiny to a finding for an outdoor gear brand that turned out to expose a genuine $14M+ attribution gap in their email channel — five years of email-driven revenue that broken UTM tagging had been crediting to direct and organic traffic. The discipline is what let me tell the difference between a real finding and a plausible-sounding wrong one.