~/services/ai

Artificial intelligence integration

We put AI inside the systems you already use, at the specific points where it saves hours or catches what a person misses. With the saving measured before and after, not promised.

Free diagnosis

The problem

Most AI projects that stall start backwards: the technology is chosen first and the place to put it is looked for afterwards. The result is a demo that impresses in a meeting and that nobody uses the following Tuesday.

The other failure is not measuring. If you do not know how many hours the process took before, you cannot know whether AI improved it, and you end up paying a subscription for something you cannot tell works.

Where we put it

Process automation

Classifying, extracting and routing what someone reviews by hand today: invoices, email, tickets, documentation. The repetitive work that eats the day.

Agents wired to your data

Assistants that answer about your own information rather than the internet, with control over who can see what. The hard part is the permissions, not the model.

Anomaly detection

Finding what falls outside the pattern in operations, accounting or usage: the odd entry, the unexpected spike, the transaction that does not add up.

Model choice and cost

Which model for which task and what each call costs. The most expensive model frequently does not improve the result and does multiply the bill.

You can check this

The AI module inside Navalón

In Navalón, AI is not a separate product: it is a module inside the suite, doing anomaly detection over the accounting and operational data already held in the system. That is the pattern we argue for — AI lives where the data lives, not in a tool alongside it.

See the project →

What you usually ask before signing

Does my data end up training someone else's model?: Not if it is built properly. What leaves your infrastructure and what does not is decided up front, and some tasks we solve with models running on your own server. It is an architecture decision, and it is yours.
What happens when the AI is wrong?: It will be wrong. That is why we automate the reversible first, with a human review behind it. A model deciding alone on something that cannot be undone is a design error, not a model error.
Will this be obsolete in six months?: The model will; the integration will not, if it was built to swap it. The model sits behind an interface, so replacing it is a configuration change rather than a new project.

Tell us which repetitive task eats your day

We will tell you whether AI solves it, how much it would save and how that would be measured. And if the answer is that it is not worth it, we will tell you that too.