We zoom in on the AI your business needs — and we build it into production.
We develop AI systems on Claude, Google Gemini and OCI Generative AI, wired into the ERP and databases you already run. From the first case chosen to a system in production, usually six to twelve weeks.
If getting your AI into production is the problem, we have the solution.
Most AI projects that go wrong do not have a model problem. They have an engineering problem: nobody defined where the AI may decide on its own, what happens when it gets it wrong, and how you later prove what was decided. That is where we zoom in.
Quick to demo, expensive to keep
It impresses in a presentation and then never reaches production — or it does, and nobody can explain it.
- Fabricated answers nobody catches in time
- Internal data sent outside without knowing which
- Per-call costs that only show up on the invoice
- No way to reconstruct why it decided that way
- Change the model, change the behaviour, break everything
Slower to start, genuinely sustainable
It goes live with the same rigour as any other critical component of the system — because that is what it becomes.
- Explicit scope: what it decides and what it routes on
- Answers grounded in your data, with the source cited
- Cost and latency measured per operation
- Complete log of inputs, outputs and decisions
- Regression tests before switching models
Six principles we do not negotiate.
These are not good intentions. They are technical requirements, written down before the first line of code.
Scope before model
First we define what the AI may decide on its own and where it must hand over to a person. Only then is the technology chosen.
Grounded in your data
Answers built from your own sources, with a verifiable citation. If an answer has no origin, it does not go out.
Fully auditable
Every call stores input, output, cost and model version. Months later you can still explain a decision to a client or an auditor.
Fail safe
When confidence is low, the system does not guess: it escalates to human review. An AI that can say “I don’t know” is worth more than one that is almost always right.
Replaceable by design
The model sits behind a layer of our own. Switching vendors is a cost decision, never a rewrite.
Measured against the alternative
Before we start, the baseline and the number that justifies carrying on are written down. If it does not beat the process that was already there, we switch it off.
From diagnosis to a system in production.
Opportunity assessment
Where AI genuinely pays off in your business — and where it only adds cost and risk. You leave with a prioritised list, an effort estimate and the cases we recommend you don’t pursue.
Assistants over internal data
Search and answering over your documentation, database or process history. With the source cited in every answer, and without exposing what must not leave.
AI agents
They carry out multi-step tasks inside the systems you already run — with scoped permissions, a log of every action and human confirmation where the decision carries weight.
Analytics & dashboards
Dashboards wired to the ERP and databases you already run, with plain-language questions and the query behind every number in plain view.
Process automation
We remove repetitive work — classifying, extracting and routing documents, e-mails or requests — with a confidence threshold and a human review queue for the doubtful cases.
Integration into existing systems
The part that usually gets underestimated. Connecting the model to the ERP, to Oracle, to the internal API and to the workflow people already use, without forcing anyone to change habits.
Governance and audit
Usage logging, cost control, data policy and the documented evidence of how the system decides — before anyone asks for it.
Custom development
Websites, systems and applications built from scratch for your process — no generic solutions, no waste. Backend, database and AI layer as a single system.
No big bang. One case at a time, measured.
We would rather prove value on one small, real process than design an AI strategy that stays in a slide deck. Short cycles, visible deliverables from the first week.
Understand the process, not the technology
A conversation about how the work is done today, who decides what, and where time is lost. Technology comes later — and sometimes the right answer does not involve AI at all.
Pick a case with a metric
A bounded process where before and after can be measured — time per request, error rate, cost per operation. Without a metric there is no honest way to say whether it worked.
Prototype with real data
With your data, not a demo sample. This is where the odd cases show up that decide whether the idea is viable — and it is better they show up now.
Production with a safety net
Phased rollout, human review on low-confidence cases, cost and quality monitoring. The net tightens or loosens as usage data shows where it is actually needed.
Hand over control
Documentation, team training and access to the logs. The goal is that you can operate and question the system without depending on us.
Tools we work with.
The choice always depends on the problem and on what is already in the house. Integrating well with what you have is worth more than introducing new technology.
Ready to zoom in on your next project?
Describe the process in two lines and we will tell you how we would approach it, with a timeline and a first step. Answer within 1 working day.
zoompositivo@zoompositivo.ptSee our AI in action — this assistant was built by us, with the same technology we sell.