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Meinhardt AI Centre of Excellence

RAG platform deployed across 6,000+ engineers.

Engineering teams across multiple regions were spending significant time on routine information retrieval, design review, and documentation cross-referencing. The challenge was deploying an AI system that engineers trusted, that met enterprise security requirements, and that improved measurable output without creating dependence.

Built a domain-specific RAG platform trained on Meinhardt's internal design standards, project documentation, and regulatory references. Human-in-the-loop review gates kept engineers accountable for AI-assisted decisions. Governance framework aligned with Singapore's IMDA MGF-Agentic guidelines.

Over 20% reduction in engineer time on routine tasks. AI-assisted design review deployed across six regional offices. Zero security incidents. Governance model became the template for the Group's AI deployment standards.

SME Takeaway

A retrieval system trained on your own documentation — product specs, proposals, service manuals — consistently outperforms a generic AI on your actual work. The governance rule is the same at any scale: classify every task by whether the AI decides alone, requires a human check, or cannot act without approval.

Decision Framework

AI Task Classification Decision Matrix

The three-tier classification system used to govern every AI action on the platform: decisions the AI takes autonomously, decisions requiring a human review before execution, and decisions that require explicit human approval. Includes the classification logic, escalation criteria, and the audit trail structure for each tier.

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