AI consulting services, including what not to build
Our AI consulting services put a senior engineer inside your problem for two to four weeks, looking at your real data. You get a sequenced, costed roadmap — and an honest list of the ideas that aren't worth it yet.
A plan you can defend.And a build spec.
No frameworks from an analyst. No deck you can't act on. A costed roadmap grounded in your real data, detailed enough to hand straight to a build team.
A scored opportunity map
Every candidate use case on one impact-versus-effort grid, each tied to a real process, an owner, and a measurable metric — not a brainstorm list.
A costed ROI model you can edit
Build cost in engineering weeks, recurring inference and infra cost at real volume, and the value side with every assumption stated and a break-even line. A spreadsheet, not a slogan.
A build-vs-buy call per use case
Build custom, wrap an existing model or API, buy off-the-shelf, or do nothing — with the specific tools and vendors we'd actually choose, and why.
A data-readiness report
What data exists, where it lives, and the quality, coverage, and labelling gaps to fix before any model earns its keep. Usually the real bottleneck, surfaced early.
An EU AI Act and GDPR risk note
Each use case classified by risk, with lawful basis, data residency, and DPIA triggers flagged — and a clear note on what needs your lawyer. Practical, not a legal opinion.
A sequenced 6–12 month roadmap
Phased, costed, dependency-ordered, and detailed enough to be the build spec — designed so the first shippable result lands fast. Plus an explicit do-not-build list.
Five steps,first win lands fast.
From scoping call to live readout — most candidate ideas get cut along the way, and that's the point.
Thirty minutes, an engineer on our side. We cover the business pressure, the data situation, and the budget. Sometimes you already know what to build and should skip straight to development — we'll say so.
Week one to two: we interview the people who own the processes and the people who own the data, get read access to the relevant systems, and look at the actual schemas, volumes, and quality — not a description of them.
Week two to three: each candidate gets a feasibility verdict, a cost model, a compliance classification, and a build-buy-or-do-nothing call. This is where most candidates get cut.
Week three to four: we sequence the survivors into phases with dependencies and a fast first win, attach a metric and a cost ceiling to each, and write the do-not-build list.
A working session walking you through the plan, the numbers, and the trade-offs — argued live, not just emailed. You own every artifact. If a build follows, the roadmap is the spec.
We'll tell younot to build it.
If any of these sound familiar, we should talk.
Founders with a board mandate
On the hook to "have an AI strategy"
- A deadline to have a plan and no shortlist
- Pressure to defend it to investors
- Real risk of spending six figures on the wrong idea
Outcome: A plan you can put in front of the board, with the bad bets already cut.
CTOs with more ideas than budget
Whose team is pitching agents, copilots and RAG
- More proposals than budget to fund them
- No outside read on what's feasible against your data
- Compliance landmines hiding in the backlog
Outcome: A sequenced shortlist you trust, with the landmines flagged.
Leaders whose pilot stalled
A POC that demoed well and died in production
- Data that wasn't ready for production
- Costs that blew up at real volume
- No one ever defined what success meant
Outcome: A grounded second attempt that survives real users this time.
Senior engineers. No handovers. No fluff.
What an AI strategy consultant actually does
An AI strategy consultant looks at your business, your data, and your real constraints, then tells you where AI is worth the spend and where it isn't. For an AI consultant, business outcomes come before the tech: the output is a decision, not a demo. Most don't write code; they write the plan you build against. Ours is run by a senior engineer who has shipped AI in production, so the advice is grounded in what actually works. We assess your data and systems, then return a costed, sequenced roadmap: the use cases ranked by payback, what each one needs to be feasible, and the order to build them in. We also tell you what NOT to build — the ideas that look good in a deck but won't earn their keep. You leave with a plan you could hand to any competent team. Building it is a separate decision.
AI consulting for small businesses
AI consulting for small businesses has to be different from the enterprise version: practical, costed, and honest about what isn't worth it yet. We work with SMEs that want a clear answer before they spend. We look at how the business actually runs, where time and money leak, and which problems AI can move now versus which are better solved by a simpler fix. The roadmap is priced in real numbers and sequenced so the first thing you build pays for the next. Where the better move is strategic software development rather than an off-the-shelf tool, we say so and explain the trade-off — but the deliverable here is the plan and the reasoning behind it, not the software. Building anything is a separate, optional step.
Start your deployment.
Talk directly to a principal engineer.
No sales team.
No discovery workshops.
No procurement circus.
We scope, build and ship.
- Reply within 24h
- Engineer-led assessment
- Written proposal
- Portugal / EU timezone
No commitment. Just an engineer.