Three years ago, I was working on a project where every sprint retrospective felt like déjà vu. Dependencies were missed, scope crept despite careful planning, and the same collaboration patterns kept derailing delivery. My co-founder Robert Amanfu and I started asking: what if we applied the same data-driven rigor we use for user behavior to our team dynamics?
That question led to US Patent 12,106,240 B2 (“Systems and methods for analyzing user projects”) and taught me something fundamental: product thinking and engineering rigor need to work together differently than we usually assume.
What Traditional Frameworks Miss
Product managers are trained to identify user pain points, prioritize trade-offs, and iterate based on feedback. But when we manage our own teams, we often fall back on tools that treat collaboration as linear.
Those tools track task completion and velocity, but miss the factors that actually determine collaboration success:
- Shared vocabulary — do teams mean the same thing by the same terms?
- Systems thinking alignment — do we agree on which levers create change?
- Technical debt awareness — do product choices reflect real constraints?
- Implicit knowledge gaps — what assumptions are teams making about each other?
Traditional tools don’t surface that the front-end team interprets “responsive” as mobile-first while the back-end team hears adaptive layouts. They don’t show how a database architecture makes a top-priority feature expensive.
The Engineering Perspective
Engineers model relationships, identify patterns, and build scalable systems. But without product context, they can optimize for the wrong metrics. Over time, I developed a framework—Cupcake, Cake, Wedding Cake—to help teams model maturity and complexity without boiling the ocean.
Where Both Disciplines Converge (and Diverge)
Product managers and engineers share a bias toward decomposition and iteration, but they optimize for different things:
- Product: user outcomes and business value
- Engineering: system behavior and technical constraints
- Product: learning and adaptation
- Engineering: reliability and scalability
The patent bridges these perspectives by treating collaboration data like user behavior data—measurable, analyzable, and improvable.
Rethinking Uncertainty Through a Product Lens
The cone of uncertainty assumes predictability improves over time. In practice, uncertainty can increase when multiple teams aren’t aligned. Product thinking clarifies what uncertainty means for outcomes; engineering rigor models how coordination patterns break down.
The Technical Implementation
The system builds ontologies from project metadata, uses neural networks to identify relevant historical patterns, and surfaces collaboration bottlenecks before they impact delivery.
Why This Matters
Modern product development is increasingly cross-functional. We need better tools to understand team interactions and better product context to interpret collaboration metrics. That intersection is where the most important innovation happens.
If you’re interested in seeing the working prototype and demo, reach out.