Case Study
Case Study
Case Study
AI GTM & Brand Platform
The platform for strategic AI brand positioning & GTM
Year:
2025
Year:
2025
Year:
2025
Industry:
MarTech
Industry:
MarTech
Industry:
MarTech
Team:
22
Team:
22
Team:
22
Approach
Approach
Approach
We began with a clean-slate, agent-led architecture and a clearly defined GTM flow—from research and hypothesis through creation, activation, and learning. From the start, we mapped out agent roles and agreements, and outlined the marketing stack to coordinate (audience data, experimentation, content workflow, activation, and attribution). Scalability, reliability, and governance principles were embedded from day one. Focused research refined the core personas (strategists, media planners, performance marketers, content managers) and pinpointed barriers across insight generation, creative development, approval paths, and campaign rollout. Journey insights shaped guided experimentation, narrative frameworks, explainable recommendations, and a unified workspace where teams manage audiences, messages, budgets, and metrics.
We began with a clean-slate, agent-led architecture and a clearly defined GTM flow—from research and hypothesis through creation, activation, and learning. From the start, we mapped out agent roles and agreements, and outlined the marketing stack to coordinate (audience data, experimentation, content workflow, activation, and attribution). Scalability, reliability, and governance principles were embedded from day one. Focused research refined the core personas (strategists, media planners, performance marketers, content managers) and pinpointed barriers across insight generation, creative development, approval paths, and campaign rollout. Journey insights shaped guided experimentation, narrative frameworks, explainable recommendations, and a unified workspace where teams manage audiences, messages, budgets, and metrics.
We began with a clean-slate, agent-led architecture and a clearly defined GTM flow—from research and hypothesis through creation, activation, and learning. From the start, we mapped out agent roles and agreements, and outlined the marketing stack to coordinate (audience data, experimentation, content workflow, activation, and attribution). Scalability, reliability, and governance principles were embedded from day one. Focused research refined the core personas (strategists, media planners, performance marketers, content managers) and pinpointed barriers across insight generation, creative development, approval paths, and campaign rollout. Journey insights shaped guided experimentation, narrative frameworks, explainable recommendations, and a unified workspace where teams manage audiences, messages, budgets, and metrics.
Architecture
Architecture
Architecture
The orchestrator governs intent flow, session logic, and policy rules across strategic, creative, and analytical environments. Core agent domains included audience intelligence and modeling; narrative and creative generation with validation stages; testing and hold-out management; dynamic budget allocation; and attribution grounded in governed taxonomies and evidence kits. Guardrails ensured privacy, brand consistency, and cost discipline.
The orchestrator governs intent flow, session logic, and policy rules across strategic, creative, and analytical environments. Core agent domains included audience intelligence and modeling; narrative and creative generation with validation stages; testing and hold-out management; dynamic budget allocation; and attribution grounded in governed taxonomies and evidence kits. Guardrails ensured privacy, brand consistency, and cost discipline.
The orchestrator governs intent flow, session logic, and policy rules across strategic, creative, and analytical environments. Core agent domains included audience intelligence and modeling; narrative and creative generation with validation stages; testing and hold-out management; dynamic budget allocation; and attribution grounded in governed taxonomies and evidence kits. Guardrails ensured privacy, brand consistency, and cost discipline.
Outcome
Outcome
Outcome
By merging agentic orchestration with robust engineering practices (typed contracts, compensation logic, full traceability), the platform accelerated GTM experimentation and cut operational overhead. Teams executed more validated launches with stronger ROI transparency and fewer manual dependencies.
By merging agentic orchestration with robust engineering practices (typed contracts, compensation logic, full traceability), the platform accelerated GTM experimentation and cut operational overhead. Teams executed more validated launches with stronger ROI transparency and fewer manual dependencies.
By merging agentic orchestration with robust engineering practices (typed contracts, compensation logic, full traceability), the platform accelerated GTM experimentation and cut operational overhead. Teams executed more validated launches with stronger ROI transparency and fewer manual dependencies.
GTM EXPERIMENT SPEED
+38%
GTM EXPERIMENT SPEED
+38%
GTM EXPERIMENT SPEED
+38%
CUSTOMER ACQUISITION COST
–25%
CUSTOMER ACQUISITION COST
–25%
CUSTOMER ACQUISITION COST
–25%
AUTO-OPTIMIZED CAMPAIGNS (NO HUMAN HANDOFF)
80%
AUTO-OPTIMIZED CAMPAIGNS (NO HUMAN HANDOFF)
80%
AUTO-OPTIMIZED CAMPAIGNS (NO HUMAN HANDOFF)
80%
AIDED BRAND LIFT
+12 pts
AIDED BRAND LIFT
+12 pts
AIDED BRAND LIFT
+12 pts
Audience Signal Accuracy
+29%
Audience Signal Accuracy
+29%
Audience Signal Accuracy
+29%
hours/month saved
+27 PTC
hours/month saved
+27 PTC
hours/month saved
+27 PTC
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Whether you have questions or just want to explore options, I'm here.
Ready to start?
Get in touch
Whether you have questions or just want to explore options, I'm here.