At Daus Data and Grupo Mática Partners, we have been working for years in data engineering, artificial intelligence and data governance. Over this time, we have seen the rise of copilots, AI-powered intelligent development environments and IDEs capable of boosting the individual productivity of any developer. And yes, today we are roughly twice as productive as we were just a few months ago.
But no matter how good these tools are, there is an uncomfortable truth: No matter how many copilots we add, I am not going to be five times more productive — I have two hands, two eyes, and a limit. No professional can multiply their output by five simply by adding more assistants.
The real transformation is not about stretching individual productivity, but about increasing the collective capacity of the team. And that idea is where Matix was born — our AI agent ecosystem for software development.
Matix is not a theoretical concept or an experiment. We have put it into operation and it is already running in real client projects. And as far as we know, we are pioneers: no one else is applying a model like this across the entire development lifecycle.
The real challenge: getting AI to understand data semantics
Anyone who works with data knows that code is not everything. In an ETL, a migration, or a data science project, data is both part of the challenge and part of the problem: its structure, its meaning, its availability, its quality. Today’s AI writes excellent code, but it does not yet interpret data semantics with the depth our projects require.
If you ask it for a test dataset, it will generate a few rows; but to validate a real model you need time series with seasonality and realistic behaviours, for example. That is the kind of challenge that makes the difference between a demo and an enterprise project. And we work on real projects.
The origin of the aiShore© model, our natural evolution of nearshore
A couple of years ago, we designed a hybrid model for clients who needed partial outsourcing. That context forced us to define methodologies, artefacts and collaboration dynamics that guaranteed quality even with distributed teams.
That experience led us to a key question: What if, instead of outsourcing tasks to offshore teams, we could outsource them to a virtual team of agents?
This is how the AI-Shore concept was born: a model that replicates the same organisational rules as a traditional nearshore project, but applied to virtual agents. A hybrid structure that combines human talent from Mática with AI capabilities under the same rules, with the goal of multiplying capacity without compromising quality.
And once again, this is not a concept. It is an operational model that we already use. And for now, we are the only ones applying it in this way.
The key to success: agents working as a real team
The challenge was not getting agents to “know how to code”. The challenge was coordinating human and non-human intelligence as a single team. To achieve this, we had to standardise our knowledge: best practices, coding patterns, when an architect steps in, when a junior needs support, how we trace requirements, code and tests, and how we collaborate internally.
Our goal was to build a scalable hybrid working structure, capable of growing from 1 to 40 agents without losing consistency, quality or control. That is exactly what Matix enables today.
Our own “Matrix” for software development
I sometimes explain Matix by saying it is something like a “Matrix” for software development. And I say that because we have genuinely modelled a virtual representation of a complete Mática team.
Matix includes:
A virtual Product Owner
It generates user stories, acceptance criteria and technical requirements; understands the functional side and connects code and data.
Architecture and design agents
They define tasks, artefacts and technical designs following our policies, always validated by humans.
Virtual junior and senior developers
They work exactly like a real developer pair:
- the junior implements,
- the senior reviews and ensures quality,
- if they do not align after three iterations, they escalate to humans.
Specialised deep agents
Each main agent is backed by internal agents that carry out more specific tasks: secure database connections, sensitive data validation, report generation, and more.
This ecosystem is already used in production. Sometimes we activate only the product owner; other times, the design phase; and sometimes the entire lifecycle. Today we are 90 people, but we operate with almost unlimited capacity, without offshoring or compromising the quality of our work.
Capabilities that only Matix offers today
Everything we have built responds to real problems in applying AI in corporate environments:
- Dynamic tool selection depending on the phase of the development lifecycle
- Multi-repository connectivity
- Dynamic prompting that loads only the required knowledge
- Compatibility with multiple AI models (OpenAI, Claude, Gemini)
- Integration of client policies and rules
- Fine-grained control of usage and costs (critical, as AI-driven development consumes many tokens)
- Native integration with project and team management tools
These capabilities mean that Matix does not just generate code — it understands what needs to be done, why, and in what context. And it is already doing so in real client environments.
AI is leading some organisations to consider reducing junior roles. But we know that if we stop training juniors today, there will be no seniors in five years’ time. AI amplifies our capabilities, but it does not replace the natural evolution of talent.
Matix has been designed to empower human teams, not to replace them.
One step ahead
At Daus Data, we are building this for one reason: AI-driven development will be the norm in the future. Both technical profiles and business leaders must prepare for that scenario.
We have moved ahead of the curve and set a precedent with Matix. Our value proposition is clear:
- More capacity without increasing structure.
- Higher quality without increasing supervision.
- More speed without losing rigour.
- Greater competitiveness without sacrificing talent.
The future is already underway. The hard part is already solved.
Now the interesting part begins.
If anyone would like to see it live, we would be delighted to show it.