Who We Are
We Don't Just Advise.
We Build.
Most consultants hand you a strategy deck. We hand you a running system, integrated into your infrastructure, used by real people, producing measurable results — with your team trained to own it after we leave.
That's the difference between advisory and execution. We're the second kind.
We've Run the Systems You're Trying to Automate
The gap between AI strategy and AI production is an execution gap. We close it because we've been on both sides — as the system operators and as the builders of the systems that replaced manual work.
Deep hands-on experience with Oracle, SAP, NetSuite, and bespoke ERP environments. We understand how enterprise data flows between systems, where it degrades, and how to build AI that connects to — and survives in — complex integration landscapes without creating technical debt.
We've designed data pipelines at scale — connecting ERP, CRM, data warehouses, and cloud platforms across organisations with hundreds of users. AI systems that can't survive real enterprise integration environments never go live. Ours do.
We've operated and optimised commercial pipelines — CRM implementation, lead scoring, forecasting, sales process design. AI tools we build for revenue functions are designed by people who've run those functions, not just modelled them from the outside.
Platform experience across Shopify, WooCommerce, and custom storefronts — from architecture to fulfilment automation. AI for eCommerce we build is grounded in the operational reality of managing inventory, orders, and customer data at scale.
Extensive implementation experience with n8n, Make, Zapier, and Celigo — building and maintaining multi-system automation workflows in production. We don't just know how these platforms work in demos; we've debugged them at 2am when they broke in production.
Why it matters
Most AI consultants have never run the systems they're automating. They know how large language models work. They don't know how a NetSuite integration breaks at month-end, or why a CRM pipeline hasn't updated in 48 hours.
The consequence is AI systems that work in staging and fail in production — because they were built without operational context. Our team has the scars. That's what makes our builds different.
Platforms & systems we know in production
Four Principles That Govern Every Engagement
Principle 01
Speed as a discipline, not a feature.
We compress time deliberately. Not because we cut corners, but because we've designed a methodology that eliminates the delays that slow most implementations: scope creep, stakeholder misalignment, infrastructure surprises, and endless discovery phases that produce documents instead of software. Four to six weeks isn't a promise — it's a process.
Principle 02
One use case, built right, beats three built fast.
We resist the temptation to expand scope mid-sprint. A single production-ready system that your team actually uses generates more organisational value than three half-built prototypes that never reach users. We're ruthlessly focused on the outcome we scoped together — and on making that outcome undeniable before we discuss what's next.
Principle 03
We transfer capability, not dependency.
Every sprint ends with your team trained and capable of owning what we built. We don't design systems that require us to maintain them — we design systems that your people understand, can extend, and can improve without us. Because the signal that a sprint succeeded isn't when we deliver the system. It's when we become unnecessary.
Principle 04
We share the risk, so we have skin in the game.
Our fees are structured around the KPIs we define together before the sprint starts. Not time. Not effort. Not project milestones. This aligns our incentives with yours at a structural level — and it changes how we make every build decision.
Read about the RaaS model →How a Typical Engagement Unfolds
No two engagements are identical — but the sequence is consistent.
You tell us about your business, your data, and the problem you're trying to solve. We ask pointed questions and give you an honest early assessment of whether AI is the right tool.
We evaluate your organisation across five dimensions: data quality and accessibility, leadership and change capacity, technical expertise and training requirements, infrastructure readiness, and budget reality. This produces a single prioritised use case — the one with the highest probability of measurable business impact given your actual constraints. We deliver this as a brief, not a 60-slide deck. You'll have everything you need to make a go/no-go decision in about 20 minutes of reading.
We define the KPIs that will determine success before writing a line of code. These are specific, measurable, and agreed by both sides. We define scope, confirm infrastructure access, identify the internal team members we'll work with, and agree the sprint timeline. This document is the contract — not in a legal sense, but in the sense that everything we build is evaluated against it. If scope expands, so does the brief. Nothing happens quietly.
We build. Fast, with weekly checkpoints, iterating on real feedback from real users against real data. By the end of the sprint, you have a system deployed to your production environment, tested, documented, integrated with your existing stack, and in active use. Not a prototype. Not a pilot that needs six more months before it's "real". A live AI system producing results against the KPIs we agreed.
Parallel to the build, we develop the training and change management programme that gets your team from "passive recipients of a new system" to "active users who know how to get value from it". Executive briefings, hands-on user training, and technical upskilling for the team members who'll maintain and extend the system. Our target is 80%+ active adoption within six months of go-live — and we build the programme to achieve it.
We measure KPI performance against the baselines we captured at the start. We build the governance frameworks your enterprise needs to run AI safely at scale — audit trails, human-in-the-loop controls, EU AI Act compliance architecture. And if the sprint results justify the next sprint, we plan it together based on what we learned. The organisations that get the most from AI build it iteratively — one validated use case at a time, compounding.
We've run the systems
You're trying to fix.
Tell us what you're building, what's blocking you, and what a result looks like. In 30 minutes we'll tell you honestly whether we can help — and what that looks like in practice.
No upfront cost · Italy · Malta · Europe · English & Italian