AI engineering portfolio

Digital Bridge builds production AI systems, applied AI products and custom platforms for Irish organisations. This portfolio groups the estate by engineering complexity rather than by industry, from a 24/7 incident triage system running across four depots to a fifty-tool applied AI platform carrying two proprietary scoring algorithms.

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 a 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.