AI opportunity assessment
A map of your operation with opportunities ranked by impact and effort, and the real cost of each process today.
Professional AI services
AI projects rarely fail on the model. They fail on the process.
Scullino implements artificial intelligence inside operations that already work. We measure the current process, redesign what needs redesigning, and put the system into production with your team running it.
- Process assessment
- Automation
- Conversational agents
- Data and integrations
- AI governance
- Adoption and training
From the pilot that went nowhere to the system that works every day.
Almost every company has tried AI by now. A pilot, a proof of concept, a licence somebody bought and nobody uses. The problem is rarely the technology: it is that the solution was never connected to the real process, to the systems already in place, and to the people who have to operate it.
We work the other way around. First we understand how the operation runs today and what it costs. Then we decide which part is worth automating and which part is not. Only then do we choose the technology.
The outcome is a system in production, with metrics you can compare against a baseline, and an internal team able to maintain it.
Services
Six areas of work
Engage them individually or chained into a full programme. Most projects start with the assessment.
Process automation
We orchestrate the flows that today run by hand across spreadsheets, email threads and systems that do not talk to each other.
Conversational agents
Assistants that answer, qualify and resolve on the channels your customers already write to.
Data and integrations
Without accessible data no AI works. We connect the sources and build the knowledge base.
Governance and adoption
Technology installs in weeks; habits take months. This is the part that decides whether the project survives.
Continuous optimisation
An AI system is never finished, it is tuned. We stay on to measure and correct.
Approach
Four phases, no surprises
Every phase has defined deliverables and an exit criterion. If a phase does not close, we do not move to the next one.
Discovery
We understand the operation as it is, not as it is documented. We interview the people doing the work, observe the process live, and measure volumes, times and costs. We come out with a numeric baseline: without one there is no way to demonstrate results later.
Design
We define the solution before building it: architecture, technology selection, acceptance criteria, risks and a mitigation plan. We agree in writing on what working means, so that argument does not arrive at the end of the project.
Implementation
We build in short iterations, with real users testing from the first week. Rollout is gradual: a subset of the volume first, widening as quality metrics hold.
Scale
We measure against the discovery baseline and adjust. Once a use case stabilises, we take the next process off the roadmap. In parallel we transfer capability to the internal team until they can run it without us.
Sectors
Where we work
We are not a vertical firm, but these are the sectors where we have accumulated the most operational judgement.
Is a process costing you more than it should?
A thirty-minute call is usually enough to tell whether there is a case here. If there is not, we will say so.