# Nucleo

> Nucleo is a reviewed AI agency in Dublin, Ireland, listed in the AIAgencies.eu register and last assessed 2026-09-16.

Canonical page: https://www.aiagencies.eu/agency/nucleo
Generated: 2026-09-21 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
- Location: Dublin, Ireland
- Website: https://nucleogroup.com
- Hourly rate: not published
- Team size: 50-249
- Founded: 2019
- Services: AI Consulting, AI Automation, AI Development
- Industries: FinTech, Public Sector, SaaS & B2B
- LinkedIn: https://www.linkedin.com/company/nucleoie

## About
Nucleo is a Dublin advisory and technology consultancy whose Data & AI division builds automation, analytics and AI systems for finance, public-sector and enterprise clients.

## Curator assessment
Reviewed 2026-09-16 by Gabor Kiss against published evidence.

The published portfolio is data and platform work rather than AI delivery: a national utility's capital-projects reporting rebuilt on a governed Azure data mart, and a separate Power BI migration that cut one reporting team's manual effort from 30 hours a week to 6.

AI sits above that groundwork as advisory—a two-week AI Readiness Scan returning 2–3 feasible use cases, AI governance frameworks, and EU AI Act, GDPR and NIS2 obligations—so what the evidence shows is readiness and data foundations, not a model running in production.

The financial-services page quotes fraud detection "up to 50% faster than traditional rules-based systems", which reads as a sector claim rather than a Nucleo result, so weigh the numbers on service pages differently from the numbers inside the case studies.

Best for asset-intensive and regulated organizations that want their data, reporting and governance in order before an AI build, with IBM Maximo, Salesforce or Power BI already in the estate.

### Key strengths
- The capital-projects study names every source system it reconciled—Unifier, P6, Oracle EBS, RCMS, Copperleaf and Maximo—plus the lineage, validation and audit trail built around them
- The asset-management article states that Maximo condition-based maintenance only works on assets already feeding sensor data, and that anomaly detection flags drift rather than deciding on a fix
- The reporting migration reports its numbers at the level of the work—10 Excel reports migrated, 8 new dashboards delivered, reporting audience doubled—not as a single headline percentage

### Good to know
- No published case study describes a machine-learning or LLM system running in production—ask for an AI engagement reference before treating the Data & AI division as a build team
- Every client in the published work is anonymized—ask which of the utility and financial-services engagements you can be introduced to
- The team page names seven people, all in leadership, change-delivery and Maximo roles—confirm who would staff the data-science side of an AI project

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Source: AIAgencies.eu — the curated register of European AI agencies. Rate provenance: "agency-confirmed" = disclosed via submission or claimed profile; "directory-listed" = Clutch or two agreeing B2B directories (lone unverified sources are never written). Full method: https://www.aiagencies.eu/methodology · Rate index: https://www.aiagencies.eu/rates