Max Kmet

Maps the full competitive landscape — direct, indirect, substitute, and emerging competitors — with positioning gap analysis, review mining, and market_insights-calibrated saturation scoring. Feeds into idea-scoring, cac-modeler, pricing-and-wtp, and tam-sam-som-builder.

Scores the strength of core human desire motivations (survival, status, belonging, control, curiosity) for a given app idea to predict user pull and retention potential.

Generates structured pivot options for a scored idea based on weak dimensions, market_insights signals, and founder constraints. Includes scoring simulation, minimum viable pivot criteria, effort estimation, and indie buildability filtering.

Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to market_insights trend data, competitor revenue proxies, and community size signals. Includes indie capture rate benchmarks and growth-rate adjustments by trend velocity.

Analyzes idea-scoring output to identify the weakest dimensions, root causes of low scores, and probable failure modes if the idea were pursued as-is.

Writes a concise, human-readable decision brief summarizing the full validation analysis — including score, verdict, RAT experiment, pre-mortem, and tier-appropriate next actions. The document a founder actually acts on.

Aggregates all dimension scores into a final idea score (0–100) and issues a verdict. Implements a multiplicative-floor algorithm with Riskiest Assumption Test (RAT). The final output of every validation workflow.

Classifies the user into an ICP tier (beginner/builder/growth) based on experience, constraints, and goals. Run once per user session before any workflow.

Predicts retention potential by evaluating usage frequency, habit formation mechanics, and churn risk factors for a B2C app idea.

Conducts a deep background interview to understand what types of app ideas this specific user is most likely to succeed with based on their domain knowledge, networks, and skills.

Maps viral social content and trending topics to concrete app opportunities by extracting the underlying problem and validating monetization fit.

Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and market_insights monetization signals. Recommends pricing model, freemium conversion estimate, and annual/monthly strategy.

Analyzes market trends across platforms (TikTok, Reddit, App Store, Google Trends) for a given topic or category. Writes a new file to memory/market_insights/.

Models LTV, CAC by channel, LTV:CAC ratios, and payback period for an indie developer. Uses market_insights to calibrate channel CPMs and competitive intensity. Includes indie budget tier definitions and viability thresholds.

Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea. Includes viral coefficient estimation, ASO scoring rubric, and tier-adjusted verdicts.