Agency expert inAI Consultingwith a business-first approach: what to adopt, what to defer and in what order.
We separate what AI can solve in your business today from what sounds good on LinkedIn. Honest diagnosis, prioritised use cases, sensible architecture and a roadmap that respects your current ops — rather than replacing them.
The useful question is not "do we use AI?". It is "what concrete problem do we want to solve, and is AI the best tool for it today?". When the problem is well framed, half the project is done. When it is not, no model fixes it.
Moments when external judgment on AI is needed
Typical situations where hype and market noise do not help decide.
Board pressure to "do something with AI"
You need to bring serious proposals to the table — not a symbolic pilot — with real risk and return reading.
Vendors promising the moon
You are receiving pitches from AI agencies and SaaS and need independent technical contrast before signing.
AI search is changing the game
ChatGPT, Perplexity and Gemini already send traffic. Time to decide what role your brand plays in that new channel.
Internal team saturated
Repetitive high-volume tasks are consuming hours from expensive people. Time to decide what to automate properly.
Data not being leveraged
You hold plenty of customer / operations / catalogue data and suspect AI could get more from it. But not sure where to start.
Compliance risk
Regulated sector (finance, health, insurance, legal). You need to introduce AI without exposing the company to GDPR or AI Act issues.
A realistic process for applied AI
Four phases with focus on business, not on tech for tech.
- 01
Opportunity mapping
Sessions with business and operations to identify candidate processes. We look for volume, repetition, available data and real value — not glamour.
- 02
Prioritisation
Use-case matrix ordered by impact, feasibility, risk and integration cost. We close what to tackle now and what to defer.
- 03
Technical design
Sensible architecture: which model (open / closed), where data lives, how it integrates with your stack, with what controls. No over-engineering.
- 04
Deployment plan
Phased roadmap with success criteria per use case. Ready to hand off to implementation with internal team or technical partners.
Tangible deliverables
Concrete pieces to decide on AI with judgment.
AI opportunity map
Inventory of realistic use cases in your context, with impact and feasibility assessment for each.
Reasoned prioritisation
The 3-5 cases worth starting and the 5-10 worth discarding or postponing, with written rationale.
Technical architecture
Document with technology options, objective comparison and recommendation. Models, stack, integration, governance.
Risk analysis
Reading of regulatory (GDPR, AI Act), operational and reputational risks. With concrete mitigations, not generic ones.
Deployment plan
Roadmap by quarter with go/no-go criteria at each milestone so you can cut without drama if something is not working.
Leadership session
Presentation of results to the committee with executive reading and answers to the uncomfortable questions on AI.
Honest questions before hiring
Are you an "AI" consultancy or a generalist one?
We are senior digital consultants who work with AI as one more tool. We do not sell "AI transformation" as a category. Digital diagnosis and strategy remain the frame — AI enters where it makes sense and stays out where it does not.
Will you always recommend GPT / OpenAI?
No. We recommend what fits: open vs. closed model, cloud vs. self-hosted, generalist vs. specialised. The decision depends on sensitive data, operating cost, latency and control. It is an architecture decision, not a trend one.
Can you implement what is decided?
We can coordinate and accompany implementation with your team or technical partners. We are not a model-building shop: our value is in framing the decision well and not losing the thread between strategic and technical.
How do you handle sensitive data during the process?
Before accessing anything we sign an NDA. We never move real data to external models without explicit authorisation. We work with synthetic or anonymised datasets whenever possible during design.
Would you ever tell us "do not do AI"?
If that is what fits your moment, yes. We do not charge for saying "go ahead" when the honest answer is "not yet". That bias is part of the current market problem.
Does AI have a real role in your next quarter, or is it just noise?
Tell us what is moving internally on AI and what pressure is there. If we see real use cases, we say so clearly. If not, we say so too.
Shall we talk?Confidentiality guaranteed. No generic proposal.