Decision Architecture

Level 1 — Structuring your management

For companies that want to organize how they run.

• Reliable data
• Connected information
• Documented processes
• Better coordination

Level 2 — Deciding with better context

For leaders who want to sharpen their decision-making.

• Company memory
• Decision preparation
• Continuity of knowledge
• Genuinely contextualized AI

Level 3 — Building a durable system

For companies turning their data into a strategic asset.

• Data governance
• Lasting architecture
• Platform independence
• Continuous evolution

What the diagnostic identifies:

• How information flows, and where it gets lost
• Which decisions are poorly prepared
• Which knowledge isn't captured
• Which friction slows the company down
• Which tools are useful — and which aren't

The deliverable is a decision architecture, independent of any specific technical solution. It then becomes the roadmap for the whole engagement: understand, design, deploy, anchor. Implementation is handled directly by Cap & Cadran when the project is straightforward; for more complex projects, we coordinate a network of specialized partners while staying accountable for the coherence of the system.

And where does AI fit in? It isn't sold as technology. It's used to speed up access to information, reduce low-value tasks, improve decision preparation and strengthen company memory. The goal isn't to replace your judgment — it's to sharpen it.

The situation. Your sales, stock, customers, non-conformities: it's all already there, in your files. The problem isn't collecting the data. It's accessing it without losing half a day.

Today. Answering a simple question means opening the file, filtering, cross-referencing two tabs — then starting over for the next question. Often, only one person in the company knows how. If they change roles, that know-how becomes orphaned.

What we put in place. We don't replace anything. Your files stay your files, where they are, in your current tools. We add a layer on top that makes them queryable in plain language — as if you were asking a colleague. That's exactly what levels 1 and 2 cover: connecting information, then giving decisions better context.

Concretely, you ask:

• "Which machine caused the most downtime last month?"
• "Which supplier accounts for the most non-conformities?"
• "Which items have been sitting in stock for over a year?"
• "Which customers haven't ordered in six months?"
• "How much overtime per department this quarter?"
• "Which expense line has drifted the most this year?"

Production, quality, purchasing, stock, sales, HR: wherever you keep a spreadsheet, you can ask a question.

Why not just an ERP. An ERP would answer this need. But it's a heavy, costly project that forces you to change how you work. This approach costs a fraction of the price, goes live in days, and doesn't ask anyone to learn a new tool.

The situation. In most companies, AI is already there — without anyone deciding it should be. A customer email pasted into ChatGPT, an agent tested on LinkedIn, a form auto-filled by a consumer tool. No one has vetted these uses. And often, no one really knows what happens to the company's data once it does.

The risk. Since 2 August 2026, the EU AI Act formally regulates these uses within the European Union: a chatbot must disclose it's an AI, generated content must be traceable — even with a free tool. Penalties run up to €15 million or 3% of global turnover. If you operate in or sell into the EU, this already applies to you. Outside the EU, the same principles — transparency, traceability, control over your data — are fast becoming standard due-diligence expectations, even without a direct legal obligation. There's also a less visible risk: these tools increasingly request access to your accounts and passwords, without you keeping control of the data once access is granted.

What we put in place. We audit what you're already using or planning to use, choose an architecture that stays under your control, and build compliance in from the start: AI disclosure, traceability, access security. This vigilance isn't limited to one level of governance: as soon as AI enters your management — even at level 1 or 2 — the same requirement applies. The goal is the same at every level: turning your data and AI usage into a managed asset, not a hidden liability.

Concretely, we check:

• Do your AI tools tell customers they're talking to an AI?
• Is your generated content traceable?
• Who has access to what, with which passwords, on which accounts?
• What happens if your AI vendor cuts access tomorrow?

Why not just a free AI agent. These tools aren't bad — for occasional use, without sensitive data, they do the job. The problem is plugging them in, unchecked, on a customer relationship or sensitive data that puts your company at risk. An audit costs a fraction of the risk it helps you avoid.