LUMENALTA GTM DESIGN
AI-Native Parallel Delivery
Reframing digital transformation as a high-throughput engineering system.
The GTM Framework
M
Metrics
Targeting 40-60% cycle time compression and 3–5× throughput.
E
Economic Buyer
CTOs and VPs of Engineering focused on non-headcount-driven growth.
D1
Decision Criteria
Preference for 'AI-Native' systems over generic body-shopping.
D2
Decision Process
Accelerated via short assessment-to-pilot cycles (2-6 weeks).
P
Paper Process
Leveraging HIPAA/SOC2 ready governance for faster security clearance.
I
Identify Pain
Eliminating 'sequential SDLC' bottlenecks and senior dev alert fatigue.
C1
Champion
Head of AI or Digital Innovation needing proven GenAI implementation.
C2
Competition
Direct dev shops and indirect dev productivity tool auctions.
Operational Advantage
Senior-Led Pods: Average 12 years exp. orchestrating parallel AI threads.
Cycle Compression: Reducing concepts to iterative value by 40-60%.
Non-Headcount Growth: Scaling throughput 3–5× without adding salary overhead.
GTM Design Artifacts
Parallel Coding AssessmentM, D2, I
Audit and ROI model for parallelization opportunities.Contextual Intelligence MapD1, P, C1
System design for unifying AI agents with senior oversight.Pilot Execution RoadmapALL
4-6 week sprint plan for production-ready AI delivery.