The thinking behind the system
The thinking behind the system
The thinking behind the system
Feb 23, 2025
Feb 23, 2025
Feb 23, 2025

Before we designed a layout or wrote a line of code, we spent two years trying to answer a harder question:

What actually makes someone valuable to a team?

Not in abstract terms. Not in frameworks. But in the day-to-day reality of how people show up, adapt, and influence the work around them.

We didn’t start with templates or personality models. We started with theory and contradiction.

Most systems are quick to categorise. OCEAN, MBTI, colour types, value charts. Easy to name, harder to act on. Especially when real teams are shaped by context, not fixed traits. Instead of wrapping these models in new packaging, we built something deeper.

We took an eclectic approach - drawing from behavioural science, organisational psychology, workplace theory, socioeconomics, and cultural dynamics to name a few. We mapped how influence travels. Where friction tends to show up. How trust is built or broken in small moments.

We took this insight and tested it rigorously against hiring decisions, and kept only what helped people understand each other better.

Some ideas didn’t translate. Some looked useful but weren’t. Over time, we shaped those into a wider system.

Many tools aim to sound intelligent. We focused on building something that learns - because everything about work is dynamic.

We needed something that could hold complexity without flattening it. A system that interprets traits in context, surfaces influence as something emergent, and reflects how people actually contribute - not just how they describe themselves.

You might not notice the theory at first. That’s by design.

Behind every Protu insight are over 100 behavioural indicators, modelled and refined through real-world hiring. The system runs in the background - translating behaviour into insight teams can actually use. How someone shapes clarity, momentum, cohesion - or friction.

We combine evidence-based theory with machine learning to move from experimental models to production-ready intelligence.

So we built a system where:

• Traits are interpreted in context, not isolation
• Influence is emergent, not fixed
• Contribution is framed through real team dynamics, not assumptions
• We don't categorise on skills, 'fit', or value mapping

Because insight without rigour isn’t insight. It’s decoration.

We’re still improving - especially in how influence patterns show up across roles, teams, and decisions. But the system gets sharper with every hire, every team or every contradiction it has to resolve.