The process, the technical decisions and the numbers. No percentage claims we cannot show you the source of.
A group with 8 properties across 3 regions replaced its email quote request with a seven-step journey that returns a proposal to the client and a qualified lead to the sales team. 135 event spaces, 269 catering items, six back offices.
Delivered project. Catalogue figures, September 2026.
Read the caseAn architecture for councils and parish authorities: an agent that reads the complaint, summarises it, extracts the location, classifies the responsible service, geolocates the incident and drafts a reply — with three levels of human supervision, chosen by risk.
Architecture description, not a delivered project.
Read the caseAn insurer replaced its «Call Me» button with an assistant that talks, gathers what the quote needs and returns a result in the same interaction — at 11pm or on a Sunday, without waiting for office hours.
Delivered project. No conversion figures published.
Read the caseA property development that answers by itself over email, with AWS Lambda and an LLM using the project's real knowledge — and the separation between instructions, knowledge and email content that stops a message from becoming a rule.
Delivered project. No conversion figures published.
Read the caseMore cases in preparation. We publish a project when there is technical substance worth explaining — not with every delivery.
BigLearn is a Portuguese artificial intelligence consultancy, founded in 2017 and based in Lisbon. We work in hospitality and tourism, local government and public administration, insurance, real estate, healthcare, manufacturing and professional services — with organisations that already have working processes and want to know where AI improves them.
Four of those sectors have a case published here: hospitality, local government, insurance and real estate. The others do not — and until we have a project worth explaining, they will not. A sector listed without a case is a sector we work in, not one where we have public proof. It is the same distinction we draw between a delivered project and an architecture description.
What the cases share is not the technology, it is the shape of the problem: someone doing by hand what is already written down somewhere, and a wait between the intent of the person searching and the first useful answer. If that sounds familiar, the route starts with a proof of concept, with a written success criterion agreed before we begin.
Clients are not named. We describe the sector, the size and the problem with enough precision for the work to be understood, and we stop there. Naming a client requires written permission, and we would rather publish without a name than wait for it.
The numbers are catalogue figures, not results. We say how many rooms, how many items, how many languages — verifiable things, with a date attached. We do not publish revenue-increase or cost-reduction percentages we cannot show the source of.
We tell you the decisions, including the ones that ruled out AI. A case study where everything went well and the technology solved everything teaches the reader nothing. The interesting choices are the ones that had an alternative.
We say when it is architecture rather than delivery. Some of these describe completed projects; others describe what one of our architectures can do, written in the conditional. They are different things and they are labelled as such — in the list above and at the head of each page. A catalogue of capabilities presented as a track record is the fastest way to lose the reader.
BigLearn is a Portuguese artificial intelligence consultancy, founded in 2017 and based in Lisbon. It works in hospitality and tourism, local government and public administration, insurance, healthcare, manufacturing and professional services, on AI agents, business process automation and digital transformation.
Three: hospitality, with an event configurator for a group of 8 properties; local government, with a complaint-handling agent for councils; and insurance, with a life insurance quoting assistant. We work in other sectors, but a sector listed without a case is one we work in, not one where we have public proof.
Naming a client requires written permission, and we would rather publish without a name than wait for it. We describe the sector, the size and the problem with enough precision for the work to be understood, and we stop there.
No. We publish only verifiable, dated figures — how many rooms, how many items, how many languages, how many steps. We do not publish revenue or cost percentages we cannot show the source of.
A delivered project reports completed work for a real client, with verifiable catalogue figures. An architecture description is written in the conditional and says what one of our solutions can do, with no concrete deployment behind it. Each case says which it is, in the list and at the head of its own page.
With a proof of concept and a written success criterion agreed before we begin. We look at what people do today, what information is already written down and what the bottleneck costs, before discussing technology. If the proof does not pass, it was cheap to find out.