AI Governance Architecture
Clear Gate Systems designs and implements AI governance architecture for SMEs introducing AI into important workflows. We map your AI use cases, design the governance layer, and make every significant decision traceable: so you can answer your customers, your board, and any regulator with confidence. Every engagement is aligned with GDPR requirements and the direction of travel of the EU AI Act.
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EU AI Act High-Risk Deployer Checklist
If your organisation is deploying or planning to deploy AI, this checklist maps the governance controls that responsible deployment requires. Each item is linked to the relevant EU AI Act Article so you can see exactly what the regulation expects and what your governance needs to include.
Six service areas for governed AI in practice
You need to know what AI systems you have, govern how they run, and ensure every output can be traced to an authoritative source. These six service areas cover the full implementation picture.
Map, automate, and govern the AI-assisted workflows running across your organisation, with human oversight built in at every decision point.
Design the operating model, accountability structures, and documentation framework your AI governance actually needs to function.
Assess where your AI systems sit against EU AI Act requirements. Identify risk tiers, gaps, and the steps needed to reach compliance.
Build a single source of truth your organisation can query and defend, using retrieval-augmented generation (RAG) to trace every output back to a verified document.
Assess the potential impacts of your AI systems on people's rights and interests, and produce the documented assessment your deployment record requires.
Retain a governance partner for continuous oversight, incident review, regulatory monitoring, and annual compliance cycles.
Enterprise Ireland
If your organisation is Enterprise Ireland-supported, you may be able to recover up to 80% of project costs via the Digital Discovery Grant. Book a discovery call and we will confirm your eligibility as part of the conversation.
About
Eileen Weadick, PhD is an AI governance architect and EU AI Act professional based in Cork, Ireland. Her practice is built on one principle: organisations need working governance systems, not advisory reports. If you have been told you need an AI governance framework but are not sure what that actually involves, that is exactly the gap this practice is designed to close. Work is scoped to what your organisation needs, whether that is a single bounded assessment or ongoing governance support. Either way, the output is the same: implemented controls, named owners, and a governance architecture you can operate.
About Eileen Weadick, PhDInsights
Most AI incidents are not evidence of rogue machine behaviour. They are governance failures: capability connected to live systems without adequate permissions, approval gates, or oversight. Here is what Irish SME owners and directors need to understand before connecting AI to their business systems.
by Eileen Weadick, PhD•10 min read
Founder, Clear Gate Systems
Over 80% of AI projects fail before delivering value — not because of the technology, but because of what happens before it. Here is the evidence-based eight-step framework Irish SMEs need to get AI adoption right from the start.
by Eileen Weadick, PhD•10 min read
Founder, Clear Gate Systems
Irish financial services firms using AI for credit scoring, insurance pricing, or fraud detection have compliance obligations under the EU AI Act's Annex III high-risk framework. The deadline moved to 2nd December 2027 on 7 May 2026. This article explains what the law requires, what the Central Bank will look for, and how to build an AI register.
by Eileen Weadick, PhD•12 min read
Founder, Clear Gate Systems
Practical guidance on EU AI Act compliance, governed AI workflows, RAG* knowledge systems, and operating models for regulated organisations in Ireland and the EU.
* RAG: retrieval-augmented generation. A technique for building AI systems that retrieve information from an authoritative source before generating a response, rather than relying on a model's training data alone.
