AI development that earns its keep

Chatbots, search across your own documents and automation that removes admin. Built to still be in use in six months, not to demo well.

Most AI is a demo. We build past that.

Plenty of what gets sold as AI right now is a good meeting and a slick screen recording. The only test that matters is whether anyone is still opening it in six months, and that’s the standard we hold our own work to.

We build AI development and automation for brands and startups from a studio in Dublin — custom AI tools, chatbots and assistants, search across your own documents, and workflow automation that takes the admin off someone’s desk. You deal with Jason, who builds it. No subcontracting, no account manager relaying your brief to someone else.

Most of it fails for the same reason: it was built to impress a room, not to survive contact with a Tuesday. We’d rather build something smaller that people actually use than something bigger that gets demoed once and forgotten.

We work with:

Startups building a new product and wanting AI part of it from day one
Established businesses modernising how they operate or serve customers
E-commerce brands adding personalisation and automation
Agencies who want to offer AI to their clients without hiring for it in-house
Web3, AR and gaming studios adding AI capability without starting from zero

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Three things we actually build

“AI” covers a lot of ground. In practice, almost everything we’re asked for falls into one of these.

Chatbots & assistants

Something that answers for you

A chatbot or assistant that talks to customers, answers the questions your team answers all day, and hands off cleanly to a person when it should. Useful when the same questions arrive on repeat and someone’s time is going on repeating the same answer.

Document search

Answers pulled from what you already have

Search that works across your own manuals, policies, tickets or product data instead of the open internet, so the answer comes from something you actually wrote. Grounding it in your own documents is also the biggest single thing that stops it making things up.

Workflow automation

Removes the admin, not the judgement

The repetitive parts of a process — sorting, drafting, chasing, entering the same thing twice — handed to automation so your team is left doing the part that actually needs a person.

How to tell a demo from a tool you’ll still use

A handful of honest questions, before you commit to anything.

Does it fit into how your team already works, or does it ask them to change their habits to suit the tool? Tools that demand a new routine tend to get used for a week and then quietly dropped.

Is it accurate enough that someone doesn’t have to check its work anyway? If checking it takes as long as doing it, it isn’t saving anyone anything.

What does it do when it doesn’t know the answer? A tool that says so and hands off is more useful than one that guesses confidently and gets it wrong.

Is there someone accountable when it gets something wrong, or does it just sit there unmonitored until a customer complains? That’s part of why we stay on after launch rather than handing over a build and disappearing.

What it costs and how long it takes

Both depend entirely on what you’re building, so here’s what actually moves them.

We quote per project rather than publish a number that would be wrong for most people. What moves it: how ready your data already is, whether it needs to plug into systems you already run, whether an existing model is good enough or it needs one trained specifically on your material, and how many places it needs to work.

Timeline follows the same logic. A proof of concept typically takes a few weeks. A full tool or system usually takes a few months. You’ll get an honest timeline once we understand the scope, not before.

What we need from you: your data, or a clear idea of where it lives, and your actual processes — not the process on paper, the one your team really follows. That’s usually where the real project starts.

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How a project runs

Five stages, and you’re talking to Jason at every one of them.

Discovery

We get into your business, your users and what you’re actually trying to fix. If AI isn’t the answer to it, we say so here.

Strategy

We work out where it delivers real value and agree what success looks like, in terms you can check later.

Prototype

Built fast, tested against real questions and real data rather than a curated demo, and refined until it holds up.

Build

The real system, integrated into what you already run and deployed properly, not left as a prototype wearing a production label.

Support

We monitor it after launch, review how it’s actually performing and keep improving it. If something’s wrong, you ring the same number and speak to the person who built it.

Common questions

Do we need a lot of data to get started?

No. We work with what you actually have, help identify the use cases it’s good enough for, and help structure what’s missing. Waiting for perfect data is usually the reason nothing ever ships.

Where does our data go?

It stays yours, handled with proper data governance and access control, not sent off to train someone else’s public model. If that matters for your industry, say so on the first call and it shapes how we build it.

Will it make things up?

Any AI can, if you let it answer from a general knowledge base with no grounding. Building it to search and answer from your own documents rather than guess is the main way we control that, and we test it against real questions before it goes live.

What happens when it gets something wrong?

It gets fixed. We monitor performance after launch rather than handing it over and disappearing, and for anything customer-facing we build in a clean handoff to a person for the cases it shouldn’t be answering alone.

Do we need to train our staff to use it?

Usually not much. The point of most of what we build is that it fits into how your team already works rather than asking them to learn a new system. Where there’s a genuine learning curve, we walk your team through it before we hand over.

Do you train a model from scratch or use an existing one?

Both, depending on what fits. Often a strong existing model, well integrated with your data, is faster and cheaper than training your own. Sometimes it genuinely needs a model trained on your material. We choose based on your case, not on what’s more interesting to build.

How long does an AI project take?

A proof of concept usually takes a few weeks. A full tool or system usually takes a few months. You’ll get a real timeline once we understand the scope.

What does it cost?

It depends on the build, so we quote per project rather than publish a number that would be wrong for most people. Ring and describe it and you’ll get a figure quickly.

Do you build it yourselves, or is it someone else’s tool with your name on it?

We build it. You deal with Jason from the first call to launch and after it, not an account manager passing your brief to someone else.

What happens after launch?

We monitor how it’s performing, review it against the goals we set at the start, and keep iterating. AI tools drift and change with use, so this isn’t a one-off build.

Rather just ask?

You do not need to read a blog post to get an answer. Ring and describe what you are trying to make.

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