AI engineering portfolio

Production operational AI

Systems that run continuously and that an operations team depends on. Failure has a real-world cost, so the engineering is dominated by fallback behaviour, escalation paths and human oversight rather than by the model itself.

• 24/7 AI incident triage and dispatch (Verdé Environmental Group) — Continuous AI incident triage across four Irish depots: natural-language and image severity classification, Eircode geolocation, dispatch routing and duty-principal escalation.

Applied AI product engineering

Generative and analytical AI shipped as a product surface end users touch directly, with server-side inference, cost control and deterministic output contracts.

• tools.digitalbridge.ie — a 50-tool applied AI platform (Digital Bridge (in-house)) — Fifty-plus shipped tools on one platform, including two proprietary scoring algorithms and production generative AI running server-side.
• MicroYards — AI garden design and live price engine (MicroYards) — Generative redesign from a single photo, paired with a live multi-retailer price engine that keeps the result inside the user's budget.

Custom platform engineering

Multi-role platforms with commercial logic, file pipelines and ordering flows. No AI required to be difficult — these are the builds where data modelling decides whether the thing survives.

• Printhouse — custom web-to-print platform (Printhouse.ie) — A full web-to-print platform with an agentic AI product quoter, live design configurator, print-file automation and twenty-nine-plus structured product pages.

Digital product & web engineering

Marketing and booking estates where the engineering is in performance, structured data, accessibility and conversion, not in model selection.

• The wider digital product estate (Multiple clients) — Jo McAteer, Forever Home Sanctuary, Riversdale House, AI Triangulate, Equi Consignment, RCP Psychotherapy and Time To Change — grouped by what the engineering actually required.

Why is this grouped by complexity instead of by industry?

An industry label tells a buyer nothing about capability. Two projects in the same sector can differ by an order of magnitude in engineering difficulty, so the estate is grouped by what the build actually required: continuous operation, multimodal classification, deterministic scoring, file-format compliance.

How do you de-risk putting AI into a live operation?

These commitments apply to every engagement, not only the large ones.

• Seven-day shadow-mode pilot before go-live — Every production AI system runs in shadow mode for seven days first. It processes your real workload in parallel with your existing process and records what it would have decided, without acting on anything.
• Full source-code and prompt ownership — You own the source code, the prompts, the evaluation sets and the infrastructure configuration at handover. Nothing is held back as leverage.
• No vendor lock-in — Builds are deployed to infrastructure you control or can take control of. Model providers are abstracted behind an interface so a provider can be swapped without a rewrite.
• EU hosting by default — Data and inference stay in EU regions unless you specifically direct otherwise in writing.
• Provider training disabled — Every model provider connection is configured with training on your data disabled, and we will show you the configuration.
• Signed data processing agreements — A DPA is signed before any client data is processed, covering us and each sub-processor in the chain.
• Confidence-gated human escalation — Every decision path has a defined confidence threshold below which the system escalates to a named human role rather than proceeding.
• Fixed, pre-scoped pricing — Scope, price, timeline and acceptance criteria are agreed in writing before work starts, against published engagement bands.

A note on what is published here

Case studies on this page describe work Digital Bridge delivered. Client-confidential detail, credentials and internal data structures are deliberately excluded. Where a client has not authorised publication of measured figures, outcomes are described qualitatively rather than quantified — we do not publish numbers we cannot evidence. Last reviewed 2026-08-18.

How to read these engineering examples

Each entry describes a system that exists and what it does, without invented performance claims. Where a client has not agreed to be named, the sector is given instead of the business. Where a build is internal to Digital Bridge — the prerender pipeline, the audit tooling, the lead-scoring workflow — it is labelled as internal, because using our own tools in production is evidence of a different kind than client work.

The engineering pattern behind them

AI projects start with a paid discovery assessment: the data available, the decision being automated, and the failure cases. Then a narrow first version goes live with a human review step, and the review step is only removed once the error rate is measured rather than assumed. Models are used through the platform's gateway rather than a pile of separate accounts, prompts and rules are stored in version control, and client data is never used to train public models. Engagements start at €2,500 after the assessment.

What kind of work sits in this portfolio?

These are working AI and automation builds we have delivered, not case studies written up after the fact from a brief. Each one solved a defined problem for a real operation — an environmental services company needing incidents triaged faster, a print business needing a web-to-print pricing engine, a manufacturer needing a design-and-quote tool, and our own internal AI platform used to run parts of this studio. We show what was built, the data it works on a fixed quote, and what the business kept ownership of afterwards, because a portfolio entry with no technical detail is just a claim.

How should this portfolio be read against a new project?

None of these examples is a template to be resold unchanged; each solved a specific business's problem and the scope, timeline and data involved would differ for a different business even in the same industry. What carries across is the discipline: define the exact task before choosing a tool, keep the data flow documented and GDPR-compliant, build in a human review step for anything outside the system's defined scope, and hand over ownership of the code and accounts rather than keeping a client locked into our infrastructure. AI projects of this kind start from €4,500, with the final scope set after we understand the actual task, not before.

What kind of AI and automation work is actually shown in this portfolio?

The work here reflects the kind of practical AI engineering we do for real businesses — systems that automate repetitive enquiry handling, tools that connect a website’s forms and bookings directly into how a business already runs, and AI-assisted processes that draft or summarise information faster than doing it manually. Rather than showcasing flashy demos disconnected from real use, we focus on systems built to solve a specific, defined problem that came out of a paid discovery assessment with the client. Every project starts the same way: we look honestly at a business’s actual processes, work out where an automated or AI-assisted system would genuinely save time or reduce error, and only then design and build something to fit. That discovery-first approach means the work shown here isn’t generic — each system was built for a specific business’s workflow, tools and constraints, not adapted from an off-the-shelf template. Founder Joey Bray leads the engineering directly on these projects, which means the same person scoping the work is the one building and testing it, keeping the process honest and grounded in what’s actually achievable rather than overselling what AI can do.

How do you decide whether an AI project is technically feasible before committing to build it?

That’s exactly what the paid discovery assessment is for, and it’s a step we treat as non-negotiable before quoting any AI or automation work. During the assessment, we map the business’s current process in detail, look at what data and systems already exist, and identify honestly whether an AI-assisted solution can realistically improve on the current approach or whether simpler automation, or even no automation at all, is the better answer. We’d rather tell a client that AI isn’t the right fit for their situation than build something that looks impressive but doesn’t hold up against real-world use, inconsistent data, or edge cases the business will actually encounter. Feasibility also depends heavily on the quality and structure of a business’s existing information — a system built on messy, inconsistent inputs will produce messy, inconsistent outputs no matter how well it’s engineered. That’s part of what we assess before quoting. This approach means every project in this portfolio was technically vetted before a line of code was written, which keeps expectations realistic and avoids the common failure mode of AI projects promising far more than they can deliver.