About us – Veridez Solutions

World Between Words

Our Story

We are systems thinkers. And lately, we’ve been seeing a pattern. Systems across the globe are trapped in conflicting states. Struggling between shared challenges and separate priorities. Between moving fast and thinking clearly. Between going green and staying profitable. Between scaling and not breaking. Words have a way of freeing us—and trapping us. Today we live between the words that built this world: scale, profit, sustainable, go green, go AI, stay efficient.

In this noise, clarity disappears. The real question isn’t about choosing between contradictory demands.
It’s about finding what must remain constant so everything else can change.
That’s where Veridez begins:
helping you discover your “quality constant,” then building regenerative pathways around it.

The 'Aha' Moment

Our Approach

It started with a joke.
My brother and I were discussing experimentation strategies.
AI was still finding itself, and so were we.
“Why don’t we just ask it?” he said, laughing.
He was joking. But it gave me an idea.
I started asking AI to explain its own reasoning. A pattern emerged: AI could map reality brilliantly—if you framed the context right. But it couldn’t tell you what the map meant. Here’s what I realized: AI isn’t experiencing the world.

It can’t accumulate the non-negotiables that systems thinkers use to evaluate reality. It can catalog complexity, but it can’t assess what matters, what’s viable, or what’s constrained by “reality anchors” like gravity, thermodynamics, and ecological limits.
AI needed that framework.
By March 2024, we knew what to build.
The next eleven months became an unexpected collaboration: we used AI to help us build the reasoning engine that would teach AI to think systemically.

Seven Tests, Seven Successes

The Proof

The timeline seems short. That’s the point. AI’s intelligence, when given the right structure, moves exponentially.
Once functional, the reasoning engine enabled AI to map systems on its own—showing how stakeholders connected, how feedback loops interacted, how forces influenced each other bidirectionally. It was like having a map with systemic coordinates.
We validated it across seven complex challenges: plastic pollution, Middle East initiatives, Spain’s agricultural systems, Netherlands’ regeneration, climate change, Portugal, Belgium, and global education.
For each, I provided three things: topic, brief context, objective. Then: “Run this through the reasoning engine.”

AI outlined challenges we hadn’t known existed. It identified stakeholders we hadn’t considered. It discovered each system’s “quality constant.”
The Insight: ChatGPT identified global education’s “quality constant” as “teaching cognitive integrity.” For years, I’d thought it was “critical thinking.” The engine revealed something more elegant—not the tool, but the foundation that makes all tools work.
We researched independently for days. The AI got all seven right. In under 10 minutes per analysis.
That’s when we knew: this doesn’t just work. It sees what we can’t see through our own frameworks.

Late 2022-March 2024

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March 2024-April 2025

April 2025-present

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What We Discovered

Here’s the part that still amazes us.
Once the reality map showed us how everything interacted—how forces influenced each other, how stakeholders connected, how feedback loops closed—the solutions were just sitting there, right alongside the challenges.
AI, guided by the reasoning engine, cuts through noise like a lawnmower cuts through grass. The framework lets AI recognize significance when it encounters it in the data. It sees the patterns before we can even track the process.
Systems thinking isn’t just intellectually satisfying. It’s economically superior.

Want to See How We Discovered all This?

Explore Our Case Studies

Each case study shares what our testing and analysis revealed.

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