ENGAGE-META
COMMUNITY
Accumulating knowledge to achieve sustainable success with AI
Think Tank Focused on AI at Scale
- TRAIDA – IT architecture for the implementation of AI systems, NoCode, and data solutions based on the TRAIDA framework (Transformative AI and Data solutions).
- AI KNOWLEDGE – Knowledge management for training AI systems.
- MINDSET – Promote the positive use of AI systems.
A fourth sphere complements the system to address financial aspects. The practices of these spheres are universal and adapt according to the company’s context.
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Direct Access
Master Class: AI at Scale
This master class presents the AI approach and data management for scaling up. Our vision is based on the TRAIDA framework (Transformative AI & Data Solutions).
Master Class: AI Mindset
This master class aims to strengthen teams’ mindset for a constructive use of AI. Our vision is based on the META framework (Motion, Engagement, Treasury, Assurance).
Our Services
In this presentation, you will discover our comprehensive support offering for scaling AI and data management, regardless of the size of your company.
RAG Overload – Govern or Drown
(2025-03-03) – RAG here, GRAPH-RAG there. Agents here and agent-based systems there… We even see AI popping up in self-sufficient ontologies. It feels like an entire generation of computer scientists and users is rediscovering semantic modeling and automation through workflows and agents. Of course, all this machinery is incredibly powerful when combined with LLMs… but beware of the overall architecture and governance at the enterprise system level. Here are some critical questions that can cause serious problems if not properly addressed:
1)-How do you delete knowledge from an LLM? Bad luck… there’s no such thing as a “Delete data” command.
2)-What level of granularity should ontologies have? Too high, and we remain at a purely conceptual level (at best, business concepts and their relationships); too fine, and we end up with incomprehensible, unmanageable graphs (one could even go as far as considering every word in a text document as a graph node…). A compromise between these extremes is surely needed.
3)-In an LLM-driven user context, how can we ensure that business concepts are properly captured to reliably query graphs and other databases—without increasing hallucinations when the goal is to reduce them?
4)-What architecture is needed to maintain control over all automation processes that manipulate AI agents to varying degrees? Proper management of their testing, deployment, versioning, archiving, and configuration—ensuring consistency with ontologies and data—is essential. This is an extremely complex ecosystem, well known to enterprise and information system architects. Missing out on governance and methodology here would mean a guaranteed loss of control over your automation and AI agents.
So, have I made myself clear? I’m starting to feel a bit of an overdose from all these fancy graphical representations of AI agents, RAG, and Graph-RAG in synchronized, orchestrated, horizontal, vertical, oblique modes… It all seems far too much like marketing hype and far removed from the reality of large-scale technical deployments.
Our advice: don’t just hire skilled technicians in RAG, ontology, or Graph technologies—make sure to activate, reactivate, or reaffirm complex systems engineering if you want any chance of surviving software implementations without adequate architecture. This is not about technical expertise alone but rather an intellectual excellence tailored to the situation.
Pierre Bonnet, the founder of the community
With over 30 years of experience in the computer industry as an expert in Enterprise Architecture and data governance, Pierre Bonnet is the founder of Engage-Meta.
Since 2022, he has been working with AI experts based in Vietnam. He is the originator and principal author of the TRAIDA framework for Transformative AI and Data Solutions. He is also an experienced entrepreneur in the tech and beverage industries. He has formalized an innovative approach to accumulating knowledge through a framework called META, which stands for Motion, Engagement, Treasury, and Assurance. This framework includes an additional process named WASI, which stands for Write, Analyze, Share, and Innovate. This process is essential for formalizing the knowledge needed to train AI systems and fostering a positive mindset to optimize AI use.
To contact me: pierre.bonnet@hlfl-consulting.com
My PROFESSIONAL PROFILE (PDF)
My last book (Available on Amazon)
A lot of books on entrepreneurship are biographies of successful entrepreneurs or best practices on how to make a fortune. The former recount inspiring journeys but ones which are difficult to reproduce; the latter are often too academic. Some propose original methods for business management, but the practices are more targeted to specific entrepreneur profiles like start-ups.
These points of view are complementary, and I wanted to collate them in a single guide. To do so, I have had to step back from my own entrepreneur story to be able to derive a sufficiently universal framework from it that is not limited to my individual experience. As I like to formalize my knowledge, it was a good challenge to put pen to paper.
If you want to PARTNER WITH US
Using the WASI process (Write, Analyze, Share, Innovate) to transform tacit knowledge (both individual and collective) into explicit knowledge across the organization and to better train AIs. More information HERE.
In addition to the evolution towards AI and associated data management, it is also worthwhile to study the contribution of NoCode solutions to accelerate return on investment and enhance the power of deploying new solutions. Although NoCode is not a new approach, its combined use with AI is a game-changer. Discover how with the example of the startup Drinkizz HERE.
The origin of the community by Pierre Bonnet
My dual skills in entrepreneurship and data management with digitalization feeds on my engineering spirit that loves method and formalization. However, to be able to model and put knowledge in writing is not common in engineering. My education taught me that analysis and written formalization are assets for success. In a context where agile methods are often not well-used (too intense trial-error process) and digitalization is too IT oriented, this ability to model and write is rare. You probably have seen it around you and perhaps in your own way of working.
The Engage-Meta community places more emphasis on formalizing knowledge in order to increase the relevance of analysis. Once the formalized approach is adopted, the execution is carried out with less stress, and more resilience on the road to success. It was therefore natural for me to set the example of this formalization in my own fields of expertise. I then created the META Framework. Each letter of the META forms an element in which the knowledge of an area of expertise is listed: Motion (to launch), Engagement (to act), Treasury (to finance) and Assurance (to protect). The Greek prefix meta expresses the idea of stepping back and taking time for analysis to better manage and accumulate knowledge. It is part of the DNA of the Engage-Meta community.
Today, the ability to clearly articulate knowledge in writing is fundamental for effectively utilizing AI. Your AI prompts should not be reduced to just a few lines but should resemble detailed briefs that help reduce hallucinations and increase the quality control of AIs. The META framework will help you organize your knowledge to better share it with others and with AIs. By enhancing your writing, analysis, sharing, and innovation skills, you will increase your ability to leverage AI. This is the process we refer to as WASI: Write, Analyse, Share, Innovate.
Because the META framework and the WASI process are universal, they can be reused in all areas of expertise. In a world of increasing complexity, knowledge is the fuel to improve your efficiency, better engage your teams and achieve your goals.
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