See how established Irish businesses have used AI to solve real operational problems. Every project here was grant-funded, delivered by senior engineers, and measured against the client's own KPIs.
Every project on this page started the same way: an Irish business owner who knew something was not working, but was not sure whether AI was the answer or just another expensive distraction. That is a reasonable question. Most of the AI noise out there is aimed at tech companies, not at the 60 person contractor or the family-run engineering firm trying to get quotes out the door faster.
These are real AI case studies from Irish businesses. Companies in building services, engineering, manufacturing, and construction, running field teams, managing supply chains, processing paperwork, and quoting for work every day. They are established operations with 30 to 150 people, not startups, and the problems were costing real time and money.
Each case study documents a real AI project delivered for an Irish business: the problem they faced, the custom AI software we built, the measurable outcomes, what it cost, and how government grants reduced the investment.
What you will notice is that the AI is not replacing anyone. It handles the repetitive, time-consuming work that was slowing down the people who actually know the business. Pulling historical data out of an ERP system so an estimator does not have to spend an hour doing it manually. Spotting patterns in five years of job records that nobody had time to analyse. Producing a first draft of a quote in minutes instead of half a morning. Your team still reviews everything, still makes the final call, still owns the client relationship. The system just removes the bottleneck.
Every engagement follows the same path. We start with a structured diagnostic, not a sales pitch. We spend time with the people who actually do the work and figure out where AI can make a measurable difference. Sometimes the answer is that AI is not the right tool and you would be better off fixing a process or cleaning up your data first. We will tell you either way.
Most of our clients start with a structured AI assessment. It is a €5,000 engagement that Enterprise Ireland funds at 80 per cent, making your out of pocket investment €1,250. From there, if there is a clear opportunity, we scope the build and help you access grants that typically cover 50 to 80 per cent of the project cost. We handle the technical side of the grant applications, including the project description and supporting documentation.
Then we build custom AI software integrated with the systems you already use, whether that is your ERP, job management platform, CRM, or spreadsheets. Senior engineers only. No outsourcing, no junior developers learning on your project.
Every case study on this page includes specific, measurable outcomes. Not vague claims about "digital transformation" or "operational efficiency." Actual numbers: how long a task took before, how long it takes now, how many hours per week were freed up, what the client invested, what grant funding covered, and the payback period.
We measure these because that is what matters to the kind of business owner we work with. If you are an MD or operations director, you want to know whether it worked, what it cost, and whether a business like yours could get similar results.
Every system we build runs on EU or EEA based cloud infrastructure, with Ireland as the default where required. Your business data is never used to train AI models, never shared with third parties, and is hosted in the agreed jurisdiction. This is contractually documented from the start, not just a policy statement. If data sovereignty matters to your business, or to your clients, it is built into the engagement from day one.
These case studies cover building services, engineering, and more to come across construction, manufacturing, logistics, and professional services. But the pattern is universal. If your business relies on experienced people doing repetitive knowledge work, quoting, estimating, scheduling, reporting, compliance checks, and those people are your bottleneck, the underlying problem is the same. The data sits in your systems. Nobody is using it systematically. And the people who hold the knowledge in their heads will not be around forever.
If any of that sounds familiar, start with the case study below that looks most like your business problem. The call is at the bottom of the page. No pitch.
60-person company.
Read Case Study5 AI use cases identified. 80% grant funded.
Read Case StudyIdentified which production attributes drive product quality using ML on 150+ attributes.
Read Case StudyBook a 20-minute call with Brian Egan. No pitch. Just an honest look at where AI could help.
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