Confidential · Discovery Meeting · Forvis Mazars × Abu Dhabi Quality and Conformity Council

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Forvis Mazars × Abu Dhabi Quality and Conformity Council

Supporting ADQCC as you move from tactical AI pilots to a scalable, governed enterprise AI system across certification, laboratory testing, standards, and quality infrastructure.

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01 Agenda item 1

Global advisory with dedicated AI systems capability.

ADQCC leads Abu Dhabi's quality infrastructure: conformity assessment, central testing laboratories, metrology, standards development, and consumer market services. Your vision is to develop that infrastructure to enable global distinction for the Emirate.

Forvis Mazars brings audit-grade assurance together with semantic infrastructure, platform engineering, and sovereign AI delivery patterns we run internally on BOB and ABI. We operate as the trusted third party for organisations scaling AI with governance.

Global presence AI systems practice Ecosystem partnerships Trusted third party
02 Agenda item 2

From tactical pilots to one enterprise AI system.

You have started with tactical AI use cases across inspection, testing, and operations. The next step is a cross-organisation system that is sustainable: one enterprise data model, governed agents, and knowledge graphs that connect standards, lab results, and certification workflows.

Your published standards and open data are a strategic asset. Semantizing specifications unlocks agents that answer questions with traceable citations, and connects what today lives in PDFs, siloed tools, and tacit expert knowledge.

Enterprise AI system Standards semantization Knowledge graphs Governed scaling
03 Agenda item 3

Market signal and cross-sector evidence for semantic AI.

Search interest in ontology-backed AI is accelerating. Across energy, pharma, retail, and public sector, the same pattern repeats: when decisions are costly and must be traceable, organisations invest in governed knowledge graphs and ontology-backed AI. This is not experimental. It is deployed at scale.

NATO, NIH, DoD, Palantir, TotalEnergies, and others encode meaning in open standards before they scale agents. The pattern is consistent across sectors: durable meaning outlasts systems.

Ontology AI trends Cross-sector evidence Emerging stack Market signal
04 Agenda item 4

One AI operating system, a disciplined stack, and proof we deliver.

The platform layer connects your people and AI agents on one semantic spine: shared services, domain modules, sovereign infrastructure. We spell out what that could mean for ADQCC, then anchor it in a TotalEnergies case study and this live portal.

We run this internally on BOB and ABI, and we build client portals the same way you are viewing now.

Potential for ADQCC AI operating system Disciplined stack TotalEnergies case study Live delivery proof
05 Agenda item 5

Let's align on priorities.

Items 1-4 are designed for a ~15 minute presentation. The majority of our time is for you: your mandate, your current architecture, your live use cases, and where alignment creates the most value.

We prepared this brief around who ADQCC is: a certification and quality infrastructure body with open standards, laboratory scale, and a mandate to integrate AI with governance. Tell us what resonates and what we should go deeper on.

Live use cases Architecture review Priority alignment Collaboration model