Our Commercial Model
We Win When
You Do.
Traditional consultants bill by the hour and leave when the project ends. We co-invest our time, tools, and expertise — and our fees are linked directly to the outcomes we deliver together.
Results-as-a-Service
- No upfront costs or fixed day rates
- KPI-based fees tied to measurable outcomes
- Joint execution or full process ownership
- From zero to working pilot in 30 days
Consultants Get Paid Whether It Works or Not.
The traditional model rewards hours billed, not results delivered. That misalignment is why so many AI projects end as expensive experiments. We built our entire model to eliminate it.
The Four Principles Behind RaaS
Shared risk. Shared reward.
We don't get paid for effort — we get paid for outcomes. That means we carry real skin in the game on every engagement. If the KPIs aren't hit, neither is our fee. That's not a clause buried in a contract; it's the core of how we operate.
Speed is a first principle.
We run on a test-measure-scale loop designed from the start to prove value fast. Every engagement delivers a working AI system within 4–6 weeks — not a slide deck, not a prototype that needs rebuilding, but a production-ready pilot integrated into your real environment.
We build. We don't just advise.
Advice is cheap. Execution is what matters. We are not a strategy consultancy that hands off a slide deck and disappears. We design, build, integrate, and operate AI systems — then transfer capability to your team so you're never dependent on us long-term.
No vanity metrics. Outcomes only.
We tie every engagement to business outcomes that actually matter — reduced processing time, increased revenue, improved conversion, lower error rates, headcount reallocation. Not "AI maturity scores." Measurable business impact, full stop.
As Involved as You Need Us.
Some clients have strong technical teams and want a thinking partner. Others need us to own the entire process end-to-end. Both work — and we can shift along the spectrum as your organisation matures.
Level 01
Advisory & Co-design
Your team builds; we provide strategic direction, use-case selection, architecture review, and quality gates. Best for organisations with strong in-house technical capability who need an experienced AI lens.
Level 02
Joint Execution
We embed inside your team — building alongside your developers, data engineers, and product managers. We provide AI expertise, accelerate delivery, and run the enablement programme in parallel.
Level 03
Full Outsourcing
You define the outcomes; we own the entire process — from design and engineering through to live operations. Ideal for organisations that want to move fast without building an internal AI team first.
What Starting
Actually Looks Like
Free 30-min assessment call
No commitment. We identify your highest-potential use case, assess your readiness across data, infrastructure, and culture, and give you an honest picture of what a sprint would look like for you.
KPI agreement & engagement design
We agree on what success looks like before a single line of code is written. KPIs are defined, baselines are measured, and the engagement model is confirmed. Our fee structure is locked in here.
Sprint: pilot to production
We build. In 4–6 weeks your first AI system is live in your production environment — integrated with your real data, tested by real users, hitting the KPIs we agreed. Enablement runs in parallel.
Scale, govern & compound
One working system creates momentum for the next. We support an AI roadmap that compounds — each sprint strengthening your data foundations, team capability, and architecture for future initiatives.
Fair Questions.
Direct Answers.
If you have a question that isn't here, the fastest way to get a straight answer is a 30-minute call.
We define them together, before any work starts. In the KPI Agreement phase (week 1–2), we sit down with you and your stakeholders to translate your business priorities into measurable outcomes — things like reduced manual processing hours, increased conversion rate, faster bid response times, or headcount reallocation. We insist on quantifiable, time-bounded metrics so there's no ambiguity at the end of an engagement. If a metric can't be measured, we won't tie our fee to it.
This is the point of the model. If the agreed outcomes aren't delivered, our fee is reduced or waived — depending on how the engagement contract is structured. We take this seriously because it's the entire basis of our credibility. That said, we select engagements carefully: if we don't believe the conditions are right to deliver results, we'll say so in the assessment call rather than take the engagement.
Yes — this is actually the most common starting point. We don't require clean data warehouses or existing AI capability. Part of our assessment process is understanding exactly where your data stands and what can be activated without a major infrastructure overhaul. In many cases, one well-scoped use case can be built on a subset of existing data in much less time than teams expect. We'll tell you honestly in the first call if foundational work is needed before a sprint makes sense.
Three key differences. First, we carry commercial risk — a freelancer or dev agency gets paid for code shipped, not outcomes hit. Second, we bring end-to-end capability — from use case design and data readiness through to enablement and governance — not just engineering. Third, we work at the strategic level: we're deciding which AI system to build in the first place, not executing a brief someone else wrote.
Yes. We build for integration, not isolation. We have experience across a wide range of enterprise environments — from legacy ERPs and CRMs to cloud-native platforms — and build systems that connect to your existing infrastructure through APIs, webhooks, and data pipelines. We work with over 350 integrations. If your stack is genuinely exotic, we'll scope that during the assessment and be upfront about any constraints.
No. We work with mid-market businesses across Italy, Malta, and Europe — from 30-person scale-ups to multi-thousand-person organisations. The RaaS model is actually well suited to mid-market because the risk-sharing structure removes the budget barrier that typically prevents smaller companies from accessing serious AI implementation expertise.
No retainer.
No slide decks.
Just results.
Let's find your first high-impact AI use case — and agree on what success looks like before we touch a line of code.
No upfront cost · Italy · Malta · Europe · English & Italian