Choice engagements across multiple industries delivering production AI marketing infrastructure. Anonymized per engagement terms.
Marketing teams at multi-location, consumer, and high-growth companies have adopted AI tools individually—but lack the engineering layer that turns scattered experiments into compounding infrastructure.
Nearly 90% of CMOs are experimenting with AI. Fewer than 10% have captured end-to-end value at scale.
“Their power is limited when used to improve isolated steps. Realizing this potential is only possible through the reimagining and rebuilding of workflows around agentic AI.” McKinsey & Company, Reinventing Marketing Workflows with Agentic AI, 2026
Without a dedicated engineering layer, each operator’s gains stay local. Data stays siloed. Reporting is rebuilt per channel, per location, per team. The infrastructure gap widens as the business scales.
MH-1 embeds a pod that builds the unified data layer, agentic workflows, and intelligence systems the team operates against. Across engagements in 2026—including multi-location services, consumer marketplaces, DTC brands, membership communities, and B2B platforms—the pattern is consistent: audit, then production infrastructure, then compounding returns.
Each engagement followed the same pattern: audit, infrastructure build, then managed iteration. Outputs listed are what was produced and delivered.
Established home services company (40+ years operating) serving residential and commercial customers across Southeast Michigan with 24/7 emergency support. Five service categories operating like a franchise across disciplines—each with its own seasonal demand curve, competitive set, and customer acquisition pattern. Per-visit pricing plus recurring service plans. Community-focused brand but zero marketing infrastructure at engagement start: no unified CRM, no attribution, no cross-category campaign coordination. Google had indexed only 9 pages of their site.
AI-native home services company expanding across 20+ US markets with independent operators—similar franchise-like scaling challenges. Organic search drove 57% of bookings, but paid channels had no unified measurement across locations. 80% quote-to-close rate but CAC 3x target.
Peer-to-peer marketplace with 500K+ unique customers, 1.7M lifetime bookings, and extreme seasonality (10x demand swing). Data scattered across Snowflake, Braze (860 custom events), Google Ads ($4.9M historical), and Meta. Internal team lacked engineering capacity to unify.
Energy drink brand co-founded by a global sports celebrity (50M+ social followers) launching across three channels simultaneously. Internal team had brand and retail covered; lacked the AI infrastructure layer for DTC growth and cross-channel intelligence.
Curated e-commerce platform managing Meta ($60K/mo), Google, TikTok, and Klaviyo lifecycle (35% of revenue from email). Nine years of customer data but no always-on campaign infrastructure, no cross-channel attribution, and unrealistic ROAS targets.
Multi-product membership community (42,000+ contacts, $599/yr membership) with events, grants, advisory services, and content. Three revenue streams but no unified member lifecycle view, no automated retention triggers, and reporting rebuilt manually each month.
Every engagement produces a subset of these systems. The specific outputs are scoped during the two-week audit based on the team’s existing infrastructure and needs.
| Output Category | What It Contains |
|---|---|
| Brand Intelligence System | 9–11 structured files: brand master, voice & tone, positioning, messaging, guardrails, glossary, personas, products, offers/pricing, objections, visual identity. Every AI workflow reads from these. |
| Revenue Model & Driver Tree | Quantified model connecting marketing inputs to revenue outcomes. Lever priorities rank highest-ROI interventions. Compounding loops identify reinforcing activities. |
| Competitive Intelligence | 6–38 competitor profiles. Landscape matrix. Counter-moves playbook. AI disruption analysis. Updated quarterly. |
| Signal System Design | Automated trigger architecture: data event X → marketing action Y. Connects booking velocity, seasonal demand, review sentiment, and campaign performance. |
| Unified Data Layer | Warehouse integration. Cross-channel attribution. Semantic layer. Eliminates per-channel, per-team, per-location reporting rebuilds. |
| AI Visibility Audit | How the brand appears in AI-generated answers (ChatGPT, Perplexity, Gemini, Google AI Overviews). Identifies gaps and informs SEO/AEO strategy. |
| Campaign Architecture | Prioritized campaign backlog mapped to revenue levers. Always-on vs. promotional. Seasonal demand curves. Cross-channel learning loops. |
| Agentic Workflows | 115 AI-powered marketing skills: ad copy, email sequences, landing pages, creative briefs, CRO audits, SEO articles, social content, reporting. |
| Founder / Brand Voice | Structured voice rules from real content: ALWAYS/NEVER rules, signature phrases, 30+ annotated examples. Ensures brand-aligned AI output. |
| Market & Investor Landscape | TAM/SAM/SOM sizing. Funding landscape. Strategic positioning vs. well-funded competitors. Board-ready. |