Before buying AI, decide where it is needed.

Vendors, software houses and consultants all have something to propose. Choosing takes someone who knows the technology and works on the company's side, a few days a month, without having to hire them.

For
SMBs without a CTO or an AI team
With
owner, management, IT
Commitment
a few days a month

The problem

AI has found its way into every sales pitch: the ERP has its own assistant, so does the CRM, a consultant proposes a six-month project, and an employee already uses ChatGPT with customer data. Nobody in the company has the time or the skills to bring order to it.

The risk is not only wasted budget. It is buying three tools that do the same thing, overlooking the process that would have paid off most and having company data in services nobody has assessed.

How the data flows

  1. Where it comes from: Processes (where time is lost); Tools in use (subscriptions and licences); Vendor proposals (to be assessed).
  2. What happens, Fractional AI/CTO: Map of use cases (by value and feasibility); Buy or build (case by case); Rules on data (tools, data, contracts).
  3. Who checks: Management (sets the priorities).
  4. Where it ends up: Roadmap (in order of value); Projects started (prototype on real data); Projects stopped (before they cost).
Steps marked AI are the model's. The rest is ordinary automation or people.

How we solve it

04
01

A map of use cases

We start from processes, not tools, looking at where time is lost, with what data and with what risk. The result is a list ranked by value and feasibility.

02

Buy or build

For each case we decide whether an existing product is enough or something custom is needed. Often the right answer is the cheapest one.

03

Clear rules on data

Which tools can be used, with which data, under which contracts. A page people actually read, not a policy nobody opens.

04

Vendor proposals, read together

With the right technical questions, even when the answer is that you do not need it.

What we measure

03

These are the numbers we set in discovery, before writing any code. The starting value is measured on your data, and the prototype is judged against it.

Use cases started and stopped

How many move from the list to a prototype, and how many are stopped before they cost anything. Stopping the wrong project is a result.

AI spend in one place

Subscriptions, licences and usage, with one owner. In the first month a duplicate or two usually turns up.

Approved tools versus personal ones

How many people use the tools the company chose and how many use their own, with company data. It is the number that tells you whether the rules work.

When it is not worth it

If there is already a CTO or an IT lead with the time and the AI skills, they can do this work, and we are only needed on the projects. And if there is a single, clear use case, you start directly from that.

Services involved

Other use cases

11 scenarios
01 · Process automation

Order requests

Orders arriving by email, as PDFs, on WhatsApp. An agent reads them, checks prices and availability in the ERP and prepares the order for confirmation.

02 · LLM integration

Quotes and estimates

Specifications, drawings, three-line requests: for anyone working to order, every quote is a job in itself. AI finds similar past jobs and proposes line items, times and costs to validate.

Tell us the process. We will tell you if it makes sense.

A free 30-minute call, with no commercial follow-up unless you want it.

Request a call

Companies that have already worked with us

  • Velux
  • ISU
  • MovePay
  • AB Group
  • Archiva Group
  • Onda SpA
  • PDE
  • Bynary System
  • COOPI
  • Gimasi
  • Creactives SpA
  • Rev AI
  • Odditt Inc.
  • Si Sociale
  • Stones
  • Turboden
  • Schindler