Beyond Traditional Business Scaling
Since 2018, we've been quietly developing methodologies that challenge conventional growth strategies. Our approach doesn't promise overnight success – instead, we focus on sustainable frameworks that work in real markets.
Research-First Philosophy
Most business scaling advice comes from anecdotal success stories. We took a different path – spending years analyzing market data, testing hypotheses, and building frameworks based on measurable outcomes rather than inspirational narratives.
Our team includes former McKinsey researchers, data scientists from fintech companies, and operators who've scaled businesses across different economic cycles. This combination gives us perspectives that pure consultants or pure operators miss.
-
Market Reality TestingEvery framework gets tested across multiple industries and economic conditions before we consider it viable.
-
Anti-Hype MethodologyWe deliberately avoid trendy business theories, focusing instead on principles that have worked consistently over decades.
-
Adaptive ImplementationOur systems are designed to evolve with changing market conditions rather than requiring complete overhauls.
How We Develop New Methodologies
Pattern Recognition Analysis
We start by analyzing businesses that have scaled successfully across different time periods and market conditions. Rather than looking at unicorn stories, we focus on companies that grew from Skip to main content
crisalivoneth
Beyond Traditional Business Scaling
Since 2018, we've been quietly developing methodologies that challenge conventional growth strategies. Our approach doesn't promise overnight success – instead, we focus on sustainable frameworks that work in real markets.
Research-First Philosophy
Most business scaling advice comes from anecdotal success stories. We took a different path – spending years analyzing market data, testing hypotheses, and building frameworks based on measurable outcomes rather than inspirational narratives.
Our team includes former McKinsey researchers, data scientists from fintech companies, and operators who've scaled businesses across different economic cycles. This combination gives us perspectives that pure consultants or pure operators miss.
-
Market Reality TestingEvery framework gets tested across multiple industries and economic conditions before we consider it viable.
-
Anti-Hype MethodologyWe deliberately avoid trendy business theories, focusing instead on principles that have worked consistently over decades.
-
Adaptive ImplementationOur systems are designed to evolve with changing market conditions rather than requiring complete overhauls.
How We Develop New Methodologies
Pattern Recognition Analysis
We start by analyzing businesses that have scaled successfully across different time periods and market conditions. Rather than looking at unicorn stories, we focus on companies that grew from $1M to $50M+ ARR in competitive markets. The goal is identifying patterns that work regardless of timing or luck.
Controlled Testing Environment
New frameworks undergo rigorous testing with partner companies across different industries. We track leading and lagging indicators for 12-18 months before drawing conclusions. This process has killed more promising ideas than it has validated – which is exactly the point.
Failure Mode Documentation
Understanding where and why methods break down is as important as knowing when they work. We document specific conditions that cause our frameworks to fail, creating clearer boundaries around when to apply different approaches. This prevents the "one-size-fits-all" trap that plagues most business advice.
Former quantitative researcher at Deutsche Bank, specializes in finding signal within noisy business data. Led our 2024 study on revenue predictability patterns.
Built and scaled three B2B companies through different economic cycles. Focuses on creating frameworks that work in both growth and contraction markets.
Controlled Testing Environment
New frameworks undergo rigorous testing with partner companies across different industries. We track leading and lagging indicators for 12-18 months before drawing conclusions. This process has killed more promising ideas than it has validated – which is exactly the point.
Failure Mode Documentation
Understanding where and why methods break down is as important as knowing when they work. We document specific conditions that cause our frameworks to fail, creating clearer boundaries around when to apply different approaches. This prevents the "one-size-fits-all" trap that plagues most business advice.
Former quantitative researcher at Deutsche Bank, specializes in finding signal within noisy business data. Led our 2024 study on revenue predictability patterns.
Built and scaled three B2B companies through different economic cycles. Focuses on creating frameworks that work in both growth and contraction markets.