AI Manifesto
From theoretical AI to concrete, useful, deployed tools
In a few months, Domaine du Net has built a concrete approach to turn AI into genuinely useful tools: publishing, support, analytics, page generation, orchestration and industrialization. This page shows what has been built, what is already in production, and how this method can serve other organizations.
Observation
The market talks a lot about AI.
Too few make it useful.
For months now, AI is everywhere. Announcements multiply. Tools pile up. Narratives spin out of control.
But in practice, many organizations remain stuck: too many stacked tools, little coherence between them, little visible daily value, many promises, few proofs.
The real challenge is not to "do AI". It is to turn it into concrete tools, useful use cases, reliable systems and visible gains over time. This is exactly the direction Domaine du Net has taken.
Approach
We don't sell narratives.
We show systems.
Our approach rests on a simple principle: not talking about AI for the sake of it, but using it to build useful, lean, mastered and genuinely usable tools.
Convictions
What we learned about AI, once the fascination faded
Eight principles, born from our daily practice, guide every project we run with AI. They are the backbone of an editorial series.
Reserve 30% of your AI for control
Don’t spend all your AI on producing. Part of every budget, every agent, every hour must go to checking, auditing and fixing — not building more.
Audit the workflow before automating it
Automating a bad process doesn’t fix it: it runs its flaws faster. Remove and simplify first, automate second.
The agent that builds doesn’t validate its work
A model that just built a solution defends it, it doesn’t challenge it. Producing and reviewing are two roles — never the same agent.
Creation cost is not ownership cost
AI collapses the cost of development, not of maintenance. A feature written in an hour keeps costing every month after.
The best code is sometimes the one you don’t write
Technical ease doesn’t create a need. AI’s value is also measured by the developments it lets you avoid.
Ask AI what habit hides from you
After years on a product, you no longer see its inconsistencies, duplicates and dead dependencies. An AI still finds them.
Measure value, not tokens
A token is a billing unit, not a value unit. What matters is not what a use consumes, but what it returns.
Never decide without organizing dissent
No major decision should be made without a challenger — another agent, a peer — given the mandate to refute it.
In production
A complete chain, already in use
What has emerged in recent months is not a collection of tools. It is a coherent chain, capable of publishing, responding, measuring, launching, orchestrating and feeding.
Studio
Studio
Sovereign digital publishing platform. Turn your documents into clean, shareable web publications with modern distribution and real quality of experience.
Support
Support
Multi-channel AI support. Respond on your website, via WhatsApp or email from a real business knowledge base, with ticketing and personalization. Not a gimmick chatbot — the ability to provide useful, contextualized and immediate answers.
Pulse
Pulse
Large-scale knowledge ingestion, structuring and exploitation to make it usable by agents and concrete tools. Pulse is the knowledge and AI exploitation engine of the entire stack.
Appliendirect
Appliendirect
AI-agent-driven CMS to quickly design structured editorial sites, connected to data and fully manageable. Concrete case: 3eme-etoile.fr was designed and published with this CMS — a dedicated editorial structure, historical content, player sheets and sports data, within one complete, manageable product.
Method
A typical project
Five steps, in this order, every time: from idea to a system that stays in production.
- 1
Identify a high-impact task or product.
- 2
Check the data and tools available.
- 3
Build a first usable system.
- 4
Test it with real users.
- 5
Industrialize only what genuinely creates value.
Results
What this approach actually changes
Launch faster
A business need no longer requires a long classic cycle before becoming visible.
Share intelligently
Building blocks and logic can be reused instead of being recreated endlessly.
Reduce dependencies
Take back control of key functions when it makes sense.
From test to deployment
The point is no longer to demo, but to put into production.
Maintain coherence
Tools fit into a suite, a journey and a system.
Show proof
Every visible building block can be shown, tested, explained and linked to a real use case.
AI engagement
Three formats to take action
Audit, first system in production or industrialization — each format has a concrete deliverable and a measurable result. Publisher expertise put to work for your organization.
AI Day - 2 half-days
Identify your organization's most valuable use cases, prioritize them and define a concrete first action plan.
€1,500
First system - 8 half-days (2 to 4 weeks)
Deploy a first tool in production: AI support, publishing, analytics or automation.
From €5,000
Industrialization - ongoing
Scale up: shared building blocks, orchestrated agents, sustainable trajectory.
Custom
Let's talk, concretely.
30 minutes with the founder. Direct framing, no middleman, to identify the right starting point.