PARTNER CONTENT: Onix's Wingspan platform promises to move enterprises from pilot purgatory to governed, enterprise-wide AI deployment in weeks, not years

Most large European enterprises have no shortage of AI ambition, but they lack the data foundation to support it. Fragmented legacy systems, strict GDPR obligations, and anxiety about handing sensitive data to foreign cloud infrastructure have left many IT leaders running the same modernization projects on a loop, stuck in AI pilot purgatory before they reach production.

Onix, a leading services-as-software data and AI specialist, thinks it has the answer. The outfit is rolling out Wingspan across the UK and Europe this summer, built around a proprietary technology it calls the Semantic Twin: a continuously updated intelligence layer that maps an organization's entire data landscape, system relationships, and business context, then uses that foundation to give AI agents the grounding they need to work.

To find out what that means in practice, Onix's EMEA managing director, Vittorio Sanvito, answers IT and compliance leaders' most pressing questions.

Q: With Google Cloud seeing significant, high-growth demand, why is now the critical moment for Onix to make this unified push across the continent?

A: The European tech sector is at a pivotal moment. Market demand is undeniable: Google Cloud has a substantial backlog going into the coming year and continues to grow at pace, which reflects strong AI demand across every industry. Yet large enterprises in Europe are struggling to execute because they lack the proper data foundation, stuck in perpetual data modernization cycles that prevent them from scaling.

We're at the major Google Cloud Summits across Europe this summer with a single message: you don't have to stay trapped in pilot purgatory. The Wingspan rollout across Europe and our expanded strategic collaboration with Google Cloud, which is expected to drive over $500 million in cloud consumption, together reflect the scale of what we're trying to do here. We want to make clear that Onix is the execution engine for enterprises that want to turn their AI ambitions into measurable impact.

Q: When enterprise leaders speak about what keeps them up at night, data privacy and security are almost always at the top of the list. There are concerns that using advanced AI means sacrificing control over localized, sensitive data. How are Onix and Wingspan directly addressing this while keeping organizations compliant?

A: It's a valid concern, and the exact reason we built a localized, customer-first approach into the core of Wingspan. European businesses shouldn't be forced to choose between maintaining their digital sovereignty and remaining economically competitive on a global scale.

Wingspan is designed as what we call an Enterprise Intelligence Fabric. It activates data locally and securely, supports complex multi-country deployments, and complies with GDPR and regional data residency requirements by design rather than bolted on afterward. It operates across hybrid and multi-cloud environments without creating vendor lock-in. The Semantic Twin is central to all of this: because it maps your data landscape internally and continuously, you never push unverified or unstructured data outside your governance boundary to make AI work.

Q: How does Semantic Twin technology work under the hood to alleviate fears about the AI "black-box"?

A: A modern AI agent might be born today and put to work tomorrow, but it doesn't know how to execute tasks because it lacks instruction on standard operational steps. Traditional AI initiatives usually fail because they lack this deep business context.

The Semantic Twin solves this by acting as a living intelligence layer that continuously maps an organization's entire data landscape, system relationships, and operational dependencies directly to KPI levels. By providing this connective tissue up front, the Semantic Twin grounds AI agents in real enterprise data with built-in guardrails, so they operate with 99.9 percent data validation accuracy.

From a compliance perspective, this eliminates the AI black-box. The Semantic Twin enables full lineage tracking and governance-aware orchestration, so AI outcomes are grounded in corporate data, fully auditable, and explainable. This strict data grounding minimizes the hallucination risks that keep compliance teams awake at night.

Q: That level of governance-aware orchestration is mission-critical for highly regulated and data-intensive industries like financial services, healthcare, and the public sector. But beyond compliance, what does the operational impact look like for a customer who's deployed this?

A: Because the Semantic Twin provides the true enterprise context and meaning behind the data, our AI agents can move beyond simple, static automation and advance toward autonomous, high-accuracy decision-making. We're helping customers create a new AI operating model that will replace standard SDLC models.

This translates to faster time-to-value. By combining agentic AI with this enterprise context, we help organizations orchestrate data modernization and AI operations within a single framework. This accelerates modernization by 3x, moves data into an "AI-ready" state in a matter of weeks rather than years, and delivers a 50 percent to 80 percent reduction in manual effort.

Beyond the platform itself, we've also changed how we structure engagements. We're shifting away from traditional, bloated consulting models that rely on endless time-and-materials billing. About 75 percent of our engagements are now set up as outcome-based, with fixed-milestone projects. We guarantee exponential ROI by using AI-assisted delivery pods to execute these transformations rapidly.

Q: What does success look like for Onix in Europe over the next 12 months?

A: Success looks like the enterprises that came to us running consecutive AI pilots finally having something in production: governed, measurable, and connected to business outcomes rather than sitting in a sandbox. Europe has been cautious about AI for good reasons, and GDPR exists for good reasons. What we want to prove is that caution and ambition aren't mutually exclusive. The Semantic Twin is how we make that case technically; the rest is execution.

Contributed by Onix.