We bring AI into processes and products that already exist, focused on automating repetitive tasks and supporting decisions. And we create, train and test custom models and agents, from scratch or by adjusting an existing model, according to each customer's need. The agent queries your data instead of improvising an answer.
A chatbot that only answers nicely solves nothing. What we deliver queries your catalog, writes to your calendar, reserves your stock and returns a ticket number. The conversation is the interface, the work is the product.
Counting parts on a conveyor, finding a defect in a photo, sorting documents by type. That is not solved with a prompt: it needs a model trained on data from your process.
Finding and locating an item in an image or video: counting, presence, position, visual compliance.
Sorting by category, approving or rejecting by a visual criterion, identifying the type of a document.
Test cases written in natural language, executed in the browser, with replanning when the screen changes.
We train a new model when the domain is too specific, or adjust an existing one when it already gets close. The choice comes from the data you have.
Much of the work happens before training: collecting, cleaning, labeling and building a validation set that does not fool you.
Accuracy on its own hides problems. We report where the model gets it wrong, how often and on which kind of case.
Language models make things up with confidence. That is why the architecture matters more than the chosen model, and the architecture is where we work.
Price, lead time, availability and history come from a query to the system. The agent is not allowed to state anything without looking it up first.
When the output has to belong to a list, it is the code that checks and discards whatever falls outside. Asking the model to behave is not control.
In sensitive contexts, the output comes back marked as a suggestion and only becomes a record when someone approves it. And there is a deterministic path for when the model fails.
When confidentiality requires it, we serve the model on your own infrastructure, without sending data to an external AI service, with a guard that prevents the application from starting up pointing outside. And with mandatory human review of what the model generates before it becomes a delivery.
We use AI in our own quality service before proposing it to a customer: risk analysis, test prioritization and coverage expansion. What we offer has already been through our team.
Each of these is our own product, built by the same team. You can open them and check before hiring us.
An agent that answers, schedules, confirms and sends reminders over WhatsApp. RelatifyAI Beauty
An agent with catalog and stock reservation tools, not allowed to invent a price or a brand. PedeMarket
An agent that runs the triage and prioritizes warning signs, with the model on the customer's infrastructure. LarClínica
AI that plans the actions and our own driver that runs them in the browser. PilotQA AI
Not every problem needs artificial intelligence, and sometimes a well-written rule solves it for less. If that is the case, we say so before proposing a project.
Artificial Intelligence