AI & Data solutions

Framework for AI & Data Solutions

TRAIDA stands for: Transformative AI and Data solutions.

The TRAIDA framework consists of 20 cards and 65 topics to address AI and the associated data solutions. Here you will find 9 technical cards (30 topics), 6 governance cards (17 topics)  and 5+ business cards (18 topics).

Each TRAIDA card is accompanied by a concise documentation that explains its importance in improving data quality and the use of AI on a large scale within the company. With its 20 cards and 65 topics, it offers a comprehensive view of enterprise architecture approached through the lens of data management and AI.

Download the cards already released (pdf):




TRAIDA is based on an architectural vision that places a semantic platform at the center of the business system, essential for complete data quality control and scaling up AI.

The white paper: reconciling expertise in data governance, Enterprise Architecture (EA), and Artificial Intelligence represents a considerable challenge. This is the theme of our white paper, which proposes a comprehensive approach for the large-scale deployment of AI in companies. The white paper is available in both ENGLISH and FRENCH.

Watch this short TRAIDA introduction

Minimum Viable Scale


Rather than forcing the definition of technical and business EA targets, the company first compares itself to a set of essential topics for large-scale deployment of AI and associated data solutions.

The goal is not to try to describe targets on a wide range of topics, but to limit the analysis to AI and data management. We start from the principle that the minimally viable technical architecture is based on these two devices: AI and data management.

It is important to emphasize the significance of this concept of “minimally viable architecture”, also qualified as “Minimum Viable Scale – MVS”, which aptly illustrates the idea of progressively scaling the architecture.

Download the executive summary (PDF): In ENGLISH and FRENCH.


Transformative AI and data solutions demand a robust semantic management layer for scalable deployment. Within this slide deck, we aim to demonstrate the operation of such integration, elucidating key concepts including the digital twin, human-in-the-loop, AI governance, AI-powered governance (often dubbed the ‘second brain’), and semantic management. Unsurprisingly, at the core of this architecture lies a knowledge graph repository, essential for managing ontologies and facilitating the accrual of knowledge.

Vision (#1)

Slide deck with 61 slides, creative commons license, open-source, PDF 5,529Kb

  1. The context of our approach
  2. A vision for transformative AI and data solutions
  3. Alignment of our vision with the market
  4. The TRAIDA framework
  5. The final report deliverable
  6. Evaluation process

AI impacts (#2)

Slide deck with 104 slides, creative commons license, open-source, PDF 8,482Kb

  1. Impacts on the business
  2. Impacts on the IT system
  3. Integration with Enterprise Governance (EG)
  4. Integration with Enterprise Architecture (EA)
  5. Post-consultation services
  6. AI software list 

Data Architecture

Watch this additional video to find out more on Enterprise Data Architecture – As a seasoned Data Management expert with an extensive background in non-schema oriented databases and semantic modeling, I view the knowledge graph repository as a contemporary reimagining of the traditional MDM concept—more versatile and semantically aligned with the business systems of any organization.

It goes beyond the traditional scope of merely handling master and reference data, encompassing a wide array of data types, processes, and critical rules.

DB schema styles

Watch this additional video to find out more about the various database schema styles: strict-schema, meta-schema, dynamic schema, schema-less or document-schema, graph schema, and schema by convention.

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