Evidence
Fictional records, research notes, metrics, and constraints.
A portfolio architecture that turns fictional go-to-market evidence into traceable decisions—using focused agents, reusable skills, deterministic calculations, and explicit human approval points.
The new ideas are integrated into the existing system instead of creating duplicate agents. Three specialists gain deeper operating modes; AI Visibility joins as the eleventh specialist.
Growth Command reconciles analytics, ad, CRM, email, and social data; explains changes; forecasts pacing; and recommends the next test.
$gtm-growth-commandExperience AI turns approved signals into a reusable page schema, evidence-backed variants, fallbacks, duplication checks, and controlled tests.
$gtm-experience-aiCreative Studio reads comparable recent results, detects fatigue, creates controlled variants, and preserves a useful experiment log.
$gtm-creative-studioThe new strategist records answer-engine observations and prioritizes question gaps, credible citations, disclosed community work, and honest reviews.
$gtm-ai-visibilitygrowth_command_analyst
Reconciles cross-channel data, diagnoses funnel and attribution changes, forecasts pacing, and frames the next executive growth decision.
revenue_prioritization_agent
Scores B2B accounts using explainable fit and intent signals, then recommends routing and next-best actions.
market_radar_researcher
Detects meaningful competitor changes across pricing, product, positioning, advertising, reviews, and releases.
customer_signal_strategist
Synthesizes interviews, reviews, tickets, and win/loss notes into segment insights and grounded positioning.
experiment_design_agent
Turns ambiguous growth problems into prioritized hypotheses, measurement plans, guardrails, and decision rules.
capital_allocation_agent
Recommends constraint-aware investment mixes using marginal return, saturation, capacity, and forecast ranges.
experience_personalization_agent
Selects a web experience—or a governed matrix of landing pages—from allowed signals, then defines proof, fallback, quality checks, and a controlled test.
market_launch_strategist
Compares adjacent markets and builds an evidence-gated entry thesis, ICP, positioning, channels, and roadmap.
journey_orchestration_agent
Designs behavior-triggered onboarding, adoption, expansion, renewal, and win-back journeys across channels.
creative_strategy_agent
Turns a campaign goal and comparable recent performance into grounded territories or controlled variants with fatigue checks and a reusable test log.
ai_visibility_strategist
Audits answer-engine visibility and prioritizes evidence-led GEO content, credible citations, authentic community participation, and honest review programs.
gtm_lab_director routes cross-tool briefs to the right specialists, reconciles overlaps, and returns one coherent recommendation using $gtm-lab-demo.
Run the GTM AI Lab portfolio demo for NexaServe. Start with the growth decision, show which specialist agents you delegate to, preserve evidence IDs, and end with one approval-ready decision brief.
The demo makes the system’s boundaries inspectable. That matters as much as the quality of any single recommendation.
Source IDs travel with claims so a reviewer can find the record behind the recommendation.
A shared machine-readable contract keeps facts, assumptions, options, confidence, and approvals distinct.
Regression criteria test honesty, math discipline, missing-evidence handling, and human-control boundaries.
Messages, budget changes, routing, and other external actions remain drafts or simulations until approved.