// pillar 05 of 08
Artificial intelligence applied to concrete tasks.
Smart applications, automation of repetitive tasks and AI/LLM integration into the product you already have.
// what it is
What it is.
// context
The problems we usually find.
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Repetitive work consuming qualified time
Expensive people copying information from one system to another, with the errors that brings and no record of the changes.
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Knowledge that cannot be queried
Years of contracts, reports and manuals in folders. The information exists, but finding it takes longer than redoing the work.
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Pilots that never leave the demo
It worked in the presentation and never reached production. What was missing was real data, integration and someone responsible for maintenance.
// scope
What we deliver.
// proof
Projects in this area.
Cases and post-mortems from our work. The post-mortems include what went wrong, by editorial choice.
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case study
We Turned Complex Insurance Rules into a Fully Automated Comparison System
A fully automated system that could search, compare, and recommend the best insurance deals for each client, using robots that perform real-time web scraping on the websites of Portugal’s leading insurers.
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case study
From Portugal to the World: Travel with Meaning
The client reached us with the idea to create a travel platform that would unite traveling, tourism with social impact, serving as a bridge between voyagers and NGOS.
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case study
Bookings Simplified
RW Interactive created a booking system that simply works anywhere. It’s flexible, scalable, and easy to use.
// frequently asked
About Automation & AI.
Is our data used to train third-party models?
No. That is an architectural decision, not just a contractual one: we use the usage modes that exclude training, we bound what leaves your infrastructure and, where the case demands it, we run models in a controlled environment.
How is the result measured?
The metric is defined before any code is written — time saved, errors avoided, share of cases resolved without human intervention. If the pilot does not reach it, our recommendation is not to proceed.
What happens when the model gets it wrong?
We assume it will. The design includes human oversight wherever the decision has impact, cited sources for verification, and a log of every request.
// next step
Shall we talk about your case?
Thirty minutes on automation & ai: you describe the problem and we tell you the path we recommend — including when that path is not us.
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Diagnosis
30 min. We identify the right pillar.
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Priorities
Explicit trade-offs · realistic phasing.
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Proposal
Scope, timeline, team and investment in writing.