Technology to turn real problems into new capabilities.

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I’m Diego Ibáñez, a Systems Engineer and technology entrepreneur.

For more than 15 years, I’ve worked across data, technology, architecture, and artificial intelligence, turning complex problems into technology solutions that can be used, measured, and evolved in the real world.

My approach is built around a simple idea:

Understand the problem. Experiment. Measure. Learn. Build. Evolve.

My territory

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Data + Technology + AI

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I don’t see these disciplines as isolated pieces.

I connect them to explore new ways of understanding information, automating processes, making decisions, and building digital capabilities.

I work with companies and individuals who have a problem, an opportunity, or a question that does not yet have a clear technology solution.

My Lab

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I don’t simply experiment with technology.

I’m building a technology experimentation lab: an environment with an up-to-date architecture, tools, data, AI models, and technology components that allows me to quickly test different solutions against real problems and use cases.

The Lab is a space to explore, build, and validate.

It allows me to test an idea, connect data, evaluate AI models, build a prototype, measure results, and understand which direction makes sense before turning it into a larger solution.

The Lab enables experimentation across:

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• Data — integration, processing, analysis, and knowledge generation.
• Technology — applications, APIs, automation, cloud, infrastructure, and architecture.
• AI — models, agents, RAG, assistants, intelligent automation, and emerging use cases.
• Architecture — different components and patterns to find simple, sustainable, and scalable solutions.

How I work

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I don’t start with a predefined solution.

I start with a real problem and use the Lab to build a focused experiment that allows us to learn quickly.

The process can begin with a simple question:

Can we solve this differently using data, technology, or AI?

From there:

Problem → Hypothesis → Experiment → Measurement → Learning → Solution → Evolution

The experiment is not the final goal.

It is a way to reduce uncertainty before investing more time, money, and resources into a solution.

From experiment to capability

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The outcome I’m looking for is not simply a prototype or a technology demo.

I want each project to have the potential to become a new capability.

A capability to:

• understand data better;
• automate a task;
• make a decision;
• solve a problem;
• experiment with AI;
• build a new product;
• or develop a new way of working.

When a solution demonstrates value, it can evolve:

Experiment → Implementation → Evolution → Product

The Lab becomes a bridge between a question and a technology capability that can remain within the organization.

What I’m building

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I’m building a technology practice around Data + Technology + AI, where every problem can become an opportunity to learn, build, and evolve.

The Labs documented here show that process: problems, hypotheses, architecture, experiments, decisions, results, and lessons learned.

Some will be personal projects. Others will explore business problems. All of them share the same principle:

Start with real problems to discover what new capabilities we can build with technology.

My principle

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I believe technology should increase the ability of people and organizations to understand, decide, and act for themselves.

That is why I build.

Technology that generates new capabilities to evolve.