Bringing AI into your business, step by step

The use cases that actually pay off in SMEs and mid-caps

You don't need flashy AI. The projects that pay are often the least impressive ones: they remove a repetitive task, save hours of searching, or keep support running overnight. Here are the cases I see pay off fastest, with an order of magnitude observed in anonymized settings in brackets.

Each of these maps to a building block I handle: LLM and RAG, agents and orchestration, automation and workflows, audit and optimization. We start with one case, the one that pays off fastest, and expand once the value is proven.

• An internal copilot over your documents that answers in plain language with its sources: a sourced answer in 2 seconds, 70% less time spent searching (consulting, legal).
• Reading and extracting your invoices, contracts and forms, structured automatically: 10x more documents processed per day, zero re-keying (insurance, finance).
• A support agent that answers around the clock and hands off on sensitive cases: 24/7 coverage, 50% fewer tickets escalated to a human (e-commerce).
• Back-office automation: sorting, data entry, follow-ups, reporting, with alerts when something jams.
• Optimizing what already runs: 30 to 70% saved on the audited LLM bill, at equal quality.

Is your data ready? The real failure point

The number one reason AI projects fail isn't the model. It's the state of the data. Scattered across ten tools, incomplete, duplicated or out of date, it will sink the best project. A brilliant model wired to messy data gives you messy answers.

That's why I look at your data before talking technology. Where it lives, as it is. I tell you what's usable right away, what needs cleaning or gathering first, and the shortest path there, without a pointless overhaul. You don't build on sand, and your data stays with you at every step.

Sovereignty and GDPR: a design choice, not a last-minute constraint

Compliance isn't bolted on at the end of a project, it's designed in from day one. EU hosting, private models deployed inside your own infrastructure, data processing agreements: depending on your constraints, we pick an architecture where your data never leaves your perimeter, not to answer, not to act, not to train anything.

Watch out for a common trap: many models, European ones included, run on US clouds and fall back under the US Cloud Act. Real sovereignty comes down to where the model and the connector acting on your tools actually run. Thanks to the MCP standard, we wire your systems once, and that connector can stay on your side, ready to feed a private model when confidentiality demands it. This is the subject of my cloud and on-prem and reliability and security work.

From audit to run: my method in 5 steps

A well-equipped solo consultant moves fast, but not at the expense of rigor. Here's how we go from idea to a system your teams actually run, without a months-long tunnel before the first useful result.

You can enter at any step and stop whenever you like. The full detail is on my method page.

• 1. Audit and scoping. A few days to identify the profitable cases, check your data and cost the expected return.
• 2. Prototype on your real data. A focused POC in a few weeks, to de-risk before you invest: a go / no-go decision backed by measurements.
• 3. Build in production. The system delivered, with evals, guardrails and observability: what separates a demo from a tool you can rely on.
• 4. Handover and documentation. You get a documented architecture with no dependency on me: one point of contact, but no lock-in.
• 5. Run and optimization. I keep it running, harden it and bring the bill down, month after month, if you want.

Costs and expected ROI: what I won't promise

I don't sell miracles, and I won't put a magic number on a brochure. Cost depends on scope, and you'll get an order of magnitude on the first call, for free. What I can commit to: I architect so the token bill stays under control, and on existing systems I often target 30 to 70% savings at equal quality.

What I won't promise: that AI is always the right answer. If the audit concludes that a well-built spreadsheet or a simple workflow is enough, I'll say so, and you'll save yourself a failed project. An honest no after a few days of audit beats disappointment after months of building. That's part of working with an independent: I have no license to sell you at all costs.