Apex.Trace

Apex.Trace was born of a concrete commercial challenge.

A company with a premium product wanted to develop relationships with restaurants of high reputation, in a process that required identifying potential clients, understanding their positioning, finding the relevant people to speak to and preparing personalised approaches.

Each opportunity involved research, the organisation of information, communication and follow-up.

A large part of this work was repetitive, but not necessarily simple.

The information was spread across different sources. The context of each potential client needed to be understood. Previous interactions had to be preserved to avoid losing knowledge and continuity.

The problem was not only the time needed to find new contacts.

It was the difficulty of turning all that information into a commercial operation that was organised, consistent and able to evolve.

Apex.Trace was born of this observation: a significant part of commercial effort happens before there is any conversation with the client.

The traditional answer to this difficulty might have been adopting a CRM, integrating several tools or developing a specific application.

But, in July 2025, a different question arose.

To what extent would it be possible to use the capabilities of artificial intelligence already available to support a commercial operation, without resorting to traditional development?

Apex.Trace explored this possibility through an approach based on the specialisation of different functions and on organising the knowledge the commercial process requires.

The ambition was not to replace the sales professional.

It was to reduce the effort associated with preparation, with research and with the management of information, allowing more attention to be concentrated on what still depends on people: understanding the client, building relationships, negotiating and deciding.

The aim was not to automate the commercial relationship. It was to reduce the work needed to make it possible.

One of the particularities of Apex.Trace lies in the moment at which it was developed.

In July 2025, artificial intelligence tools already made it possible to carry out relevant tasks of research, analysis and content production, but they had significant limitations in the continuity of information, in the articulation between different contexts and in carrying out more complex processes.

The project sought to work with those limitations, exploring new ways of organising the use of the technology available.

The solution was made available to a company, which began using it at the end of that month.

The experience made it possible to observe concrete possibilities of application, but also difficulties related to maintaining the information, to the continuity of the processes and to the dependence on human intervention.

In the months that followed, the evolution of artificial intelligence models and tools significantly changed the technological context.

Capabilities that previously demanded more laborious approaches became progressively more accessible.

This evolution does not take away the relevance of Apex.Trace.

On the contrary, it makes it possible to distinguish what depended on the technological limitations of the moment from what remains valid in the original vision.

The technology has evolved. The question that gave rise to the project is still relevant.

Apex.Trace began as an answer to a problem of commercial prospecting.

But the experience revealed a broader possibility.

Many business processes depend on distributed information, individual knowledge and activities that need to be coordinated over time.

When these elements are not properly related to one another, repetitive tasks, loss of context and difficulties of continuity appear.

Apex.Trace showed that part of these problems could be approached differently, drawing on the artificial intelligence capabilities already available.

This learning went beyond the commercial context and contributed to a broader reflection at Aetheris on the relationship between technology, knowledge and the organisation of work.

The value of the experience did not lie only in the tasks it was possible to support.

It lay too in the discovery that the way we think about and organise a problem can be as important as the technology we choose to solve it.

Apex.Trace revealed the potential of using artificial intelligence to reduce preparatory work, to improve the organisation of information and to support the continuity of commercial processes.

But it also made significant limitations visible.

The quality of the information available conditioned the results. Its use demanded discipline and follow-up. And the existence of several technological capabilities did not, in itself, guarantee a coherent process.

The experience reinforced a conclusion that remains relevant for Aetheris: adopting artificial intelligence should not begin with the search for the most advanced tool.

It should begin with understanding the problem and identifying the simplest way of creating value.

Apex.Trace represents an important stage in the Aetheris laboratory’s path of technological experimentation.

It was a practical application of artificial intelligence to a concrete business process, developed at a time when the available tools still imposed limitations that technological evolution would go on to reduce.

The project should not be understood as a commercial product currently available, nor as a solution whose original architecture necessarily remains suited to today’s technological possibilities.

Its value lies in the experience carried out and in the questions that resulted from it.

If artificial intelligence makes it possible to try out new ways of working before developing software, how many business problems might be rethought along the same lines?