How Onix Uses Semantic Twin Technology to Scale Enterprise AI in Europe
Onix is deploying Wingspan and its Semantic Twin technology across the UK and Europe to move enterprises from AI pilots to production. According to Onix EMEA managing director Vittorio Sanvito, the platform addresses data fragmentation and GDPR compliance to accelerate data modernization by 3x and reduce manual effort by 50% to 80%.
Why are European enterprises stuck in “AI pilot purgatory”?
Most large European firms have the ambition for AI but lack the underlying data foundation to scale. Fragmented legacy systems and strict GDPR obligations often trap IT leaders in repetitive modernization cycles. Sanvito describes this as “pilot purgatory,” where AI projects remain in sandboxes rather than reaching production.
The struggle stems from a conflict between the need for global competitiveness and the requirement for digital sovereignty. Many organizations fear that adopting advanced AI requires handing sensitive, localized data to foreign cloud infrastructures, which creates a bottleneck for deployment.
How does the Semantic Twin solve the AI “black-box” problem?
The “black-box” refers to the lack of transparency in how AI reaches a conclusion, a major risk for compliance teams. Onix uses a proprietary technology called the Semantic Twin to act as a living intelligence layer. It maps an organization’s data landscape, system relationships, and operational dependencies directly to KPI levels.
According to Sanvito, this mapping provides the “connective tissue” that AI agents need to execute tasks without guessing. This grounding allows AI agents to operate with 99.9% data validation accuracy. Because the system enables full lineage tracking and governance-aware orchestration, the outcomes are auditable and explainable, which minimizes the risk of hallucinations.
Comparison: Traditional AI vs. Semantic Twin Grounding
| Feature | Traditional AI Pilots | Semantic Twin Approach |
|---|---|---|
| Data Context | Static or unstructured | Continuously updated map |
| Compliance | Bolted on after development | GDPR compliant by design |
| Accuracy | Prone to hallucinations | 99.9% validation accuracy |
What is the operational impact of agentic AI on data modernization?
Moving toward “agentic AI”—AI that can take autonomous action—requires a shift in the standard Software Development Life Cycle (SDLC). By combining AI agents with enterprise context, Onix claims it can move data into an “AI-ready” state in weeks instead of years.
This shift results in a 3x acceleration of modernization. For customers in highly regulated sectors like financial services and healthcare, this means a 50% to 80% reduction in manual effort. The goal is to replace static automation with high-accuracy decision-making grounded in real corporate data.
How is the AI services business model changing?
The delivery of AI transformations is moving away from the “bloated” consulting model. Traditional firms often rely on time-and-materials billing, which can lead to endless project timelines without guaranteed results.
Onix has shifted 75% of its engagements to an outcome-based model with fixed-milestone projects. By using AI-assisted delivery pods, the company guarantees ROI through rapid execution rather than billable hours. This aligns the service provider’s incentives with the client’s need for production-ready AI.
Frequently Asked Questions
What is a Semantic Twin?
It’s a continuously updated intelligence layer that maps an organization’s data landscape, system relationships, and business context to ground AI agents in verified data.
How does Wingspan handle GDPR?
Wingspan activates data locally and securely, supporting multi-country deployments with residency requirements built into the core architecture rather than added later.
What is the difference between an AI pilot and AI in production?
A pilot usually sits in a “sandbox” for testing. Production AI is governed, measurable, connected to business outcomes, and integrated into live operational workflows.
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