Working out where AI genuinely pays off in your organisation, then building it. From assistants inside the tools you already license to custom agents wired into your own systems.
Most organisations we meet are not short of AI ideas but lack experience and capability internally to make ideas become a reality.
At Mosaic, we start with the work rather than the tooling. Which processes are expensive and repetitive? Where does knowledge sit in one employee’s head and needs derisking? What actually happens today when an answer is wrong? We help you to produce a shortlist you can defend to a board, investors or the rest of your exec team.
We do this with mid-sized and large organisations across Ireland and the UK, from our teams in Dublin, London and Hungary.
Fewer pilots
More things in production
The three tests on our homepage tell you where to look for AI work. The next question is the one that decides whether anything ships: who checks the output, and what happens when it is wrong. Almost everything we build sits at one of three levels of autonomy, and choosing the right level matters more than choosing the model.
An employee still does the work, with the searching and the drudgery taken out. Answers come from your own policies and documentation instead of from interrupting a senior colleague. Nothing moves without an employee.
The agent does the work and produces a draft. An employee reads it and sends it. Right for anything a customer or a regulator will see, and the review step is precisely what makes it safe to run at volume.
The agent completes routine cases end to end and escalates the ones it cannot resolve, with its reasoning attached.
Once you know what to build there is a second decision, and it is the one most often made by accident: whether the product lives inside the AI platform you already pay for, or as a custom agent of its own. The cost, the lead time and the ceiling are all very different.
Configured assistants that live in ChatGPT, Claude, Google Gemini or Microsoft 365 Copilot, using the licences and the tenant you already have.
What we build: Custom GPTs in ChatGPT, Projects in Claude, Gems in Gemini and agents in Microsoft Copilot, grounded in your own documents and instructions, with the knowledge base, the instructions and the guardrails treated as deliverables rather than as something a keen employee does on the side.
Agents we build against the Anthropic Claude, OpenAI, Azure OpenAI and Google APIs, integrated directly with the systems the process actually touches.
What we build: the agent, the integrations either side of it, the evaluation harness that tells you whether it is still behaving, and the interface for whoever handles the exceptions. It runs in your own cloud accounts and you own it.
The first is frequently the cheapest way to prove the second is worth funding. Configure an assistant, let the team use it for a month, and you will know which parts of the process genuinely deserve a custom agent. That is the same prototype-first logic we apply to custom development and prototyping, and AI work is often where a bolt-on around an existing platform earns its keep.
Two examples of what this looks like once it is running, one at each of the higher autonomy levels.
A finance team was matching supplier invoices by hand against purchase orders and receipts before anything could be entered.
Autonomy level: running with exceptions.
A customer-facing team was writing similar responses repeatedly, each one needing the account context assembled first.
Autonomy level: reviewed before it leaves.
Data is not the entry fee for AI, but it is the ceiling on what AI can do for you:
Policies, contracts, previous work and documentation, indexed so an agent answers from what your organisation actually decided rather than from what the model read on the internet. This needs your documents findable, not a warehouse.
Anything that reconciles, reports or forecasts depends on a data model that agrees with itself. This is where the older discipline still applies: extraction, pipelines, a schema somebody owns, and definitions the business has agreed on.
Where the foundations are not there yet we do that work as well: reviewing the current estate, designing the architecture, and building the pipelines and models. We will also tell you which of your use cases do not need to wait for any of it.
The common and expensive mistake is treating a data programme as a prerequisite for all AI work. It is a prerequisite for some of it. Getting that sequence right is usually worth more than any individual technology choice.
The first two steps cost you nothing, and they are the two that matter most.
We begin by engaging one of our Senior Client Partners to run a discovery session with your key stakeholders. The objective of this conversation is to gain a deeper understanding of your business, its objectives, and its challenges. Together, we identify one or two areas where AI can deliver immediate impact. This step is entirely free of charge.
Next, we host a workshop with our senior technical team to explore the identified areas in greater detail. By walking the process end to end and examining the relevant data, we can start to shape an approach, agree the right level of autonomy, and outline a plan. This stage is also provided at no cost, giving you early visibility without risk.
At this stage, we make a short-term cost commitment together. A small team of our senior experts embeds within your business to work through the process and the data behind it. Through a working prototype, we deliver the first “light-bulb” moments: demonstrations of what AI can unlock for your organisation. From here, we create an AI roadmap with clear objectives linked to revenue growth, cost savings, and automation opportunities.
Finally, we deploy a full team to execute against the roadmap. This ensures momentum is maintained, business value is realised, and your organisation is equipped with a clear actionable plan for implementation.
For work that lives inside a platform you already own, that means ChatGPT and Custom GPTs, Claude and Claude Projects, Google Gemini and Gems, and Microsoft 365 Copilot agents, configured inside your own tenant.
Custom agents are built on the Anthropic Claude, OpenAI and Azure OpenAI APIs, in Python or Node.js, integrated with whichever finance, ERP, CRM or ticketing system the process actually runs through. We are not tied to one model provider, and we build so that changing one is a configuration change.
Everything runs in your own AWS or Microsoft Azure accounts, with source control and CI/CD on GitHub and Cloudflare in front. Data work is Python and SQL against the warehouse you already run, with Django or React where a pipeline needs an interface around it.
A program that uses a language model to do a piece of work that used to need a person’s judgement, and that can take actions in your systems rather than only produce text.
The useful distinction is not agent versus chatbot. It is how much autonomy the work allows: whether a person checks every output, or the agent completes the routine cases itself and escalates the rest.
For some things yes, for most things no. Anything that reconciles, reports or forecasts depends on a data model that agrees with itself, and getting there is real work.
But a large share of genuinely useful AI work is language work over documents you already hold, which needs those documents findable rather than warehoused. Part of what a discovery session produces is a straight answer on which of your use cases are actually blocked on data and which are not.
Start inside the platform you already license if the work can live there. It is faster, cheaper, and it sits where your people already are, which is what adoption usually turns on.
Move to a custom agent when the job needs to read from and write into your own systems, run on a trigger or a schedule, or produce an audit trail someone will be held to. Plenty of organisations end up with both, and the configured version is often the cheapest way to prove the custom one is worth funding.
Mostly Anthropic’s Claude, OpenAI and Azure OpenAI, with Google Gemini and Microsoft Copilot where a client is already committed to that stack. We are deliberately not tied to one provider: the models change every few months, so the sensible architecture is one where swapping them is a configuration change rather than a rebuild.
Three ways, in order of importance. Choose work where a wrong answer is detectable, keep a person in the loop wherever the output is seen outside the business, and build an evaluation set so you can measure whether behaviour has drifted instead of relying on impressions.
We also design the escalation path before the happy path, because an agent that reliably says it is unsure is worth far more than one that always produces an answer.
A discovery session with your stakeholders, then a technical workshop with our senior team looking at the real process and the real data. Both are free of charge. You commit budget at the point where we build something, not before.
Yes. We are based in Dublin and London with team members in Hungary and the Middle East, and we work with organisations across Ireland, the UK and Europe. Discovery and key milestones are usually in person where geography allows; the rest of delivery is remote.
That is what the discovery session is for. Bring the process that costs you the most and we will tell you whether AI helps, at what level of autonomy, and what it would take to prove it. Free of charge.
