What Is Wingspan 2.0 and Why You Should Care
Onix is an American company specializing in data, cloud, and AI services. It has been on the market for over 20 years and ranks among Google Cloud's closest partners — it has won the Partner of the Year award eighteen times. Its clientele includes 30 of the top 100 companies on the Fortune 100 list and it currently runs over 500 AI agents in production deployment. This is not a startup still testing its product.
Wingspan 2.0 represents what Onix calls an Enterprise Intelligence Fabric. In practice, it is a platform that uses agentic AI (i.e., autonomous software agents) to automatically map, modernize, and manage corporate data. The platform is built on Google Cloud infrastructure and promises to take companies from AI experimentation to live operation within 4–6 weeks.
For Czech companies considering cloud migration or data warehouse modernization (typically from on-premise solutions like Oracle or Teradata to the cloud), this is an interesting signal. Since February 2026, Onix has been expanding its presence in the EMEA region (Europe, Middle East, Africa) and appointed Vittorio Sanvito as director for the region. The platform is thus becoming accessible to European companies — albeit without Czech localization for now.
Semantic Twin: The Brain of the Platform That Thinks in Context
The biggest innovation of Wingspan 2.0 is the Semantic Twin. While a regular digital twin mirrors a physical object (such as a production line or a building), the Semantic Twin goes one level higher: it maps meanings and relationships across a company's entire data ecosystem.
Simply put: imagine your company has data in ten different systems. The marketing CRM calls a customer "Jan Novák," the accounting system lists them as "Novák Jan," and the warehouse system tracks them under ID "CUST-4591." A typical data engineer spends weeks mapping which records belong together. The Semantic Twin creates this mapping autonomously — and what's more, it maintains it continuously.
The platform uses a knowledge graph that connects metadata, taxonomies, data lineage, and business context into a single living map. Onix claims this enables 99.9% data validation accuracy. This is crucial: according to research cited by Onix, roughly 80% of corporate data is not ready for AI deployment, and up to half of AI projects fail precisely because of poor data quality.
Hard Numbers: What Wingspan 2.0 Actually Promises
Onix reports the following metrics achieved by the platform in real-world deployments:
- 3× faster data modernization — migration from legacy systems to the cloud runs at triple the speed compared to traditional approaches
- 50–80% reduction in manual work — agentic AI takes over routine tasks previously done manually by data engineers
- 99.9% data validation accuracy — critical for eliminating errors during data transfer between systems
- 25% average cloud cost savings — thanks to an automatic FinOps layer that continuously optimizes resource utilization
- 4–6 weeks to "AI-ready" status — compared to 6–18 months with traditional consulting approaches
CEO Sanjay Singh says: "Most enterprises are trapped in a cycle of perpetual modernization because they lack a cohesive data foundation. Wingspan 2.0 breaks this cycle by creating a living semantic layer that gives AI agents the context and meaning of data."
How It Works in Practice: The IBX Case
One of the first customers of Wingspan 2.0 is IBX, an American healthcare company serving millions of members. Its CIO Sushma Akunuru describes the platform's impact clearly: "Wingspan gives our team direct access to data for faster and cost-optimized decision-making. The impact is substantial — it reduces manual effort and enables us to deliver deeply personalized healthcare."
This is an interesting demonstration of practical impact: in healthcare, where GDPR and strict regulation demand extremely precise data handling, the platform has proven itself. For European companies in regulated sectors (banking, insurance, telecommunications), this is a significant signal of trustworthiness.
Onix vs. Traditional Consulting Firms: Where's the Difference?
Onix is not positioning Wingspan as just another tool for data engineers, but as a replacement for the traditional consulting model. The classic approach of large firms like Accenture or Deloitte relies on large project teams that manually map and move data — typically 12–18 months with price tags in the millions of dollars.
Wingspan 2.0 instead deploys agentic AI that:
- Automatically discovers data sources and their dependencies
- Converts legacy code to cloud formats on its own
- Continuously validates data transfer with 99.9% accuracy
- Continuously monitors and optimizes operations — this is not a one-off project, but an ongoing operational mode
According to Onix, the result is 2–3× faster ROI at half the cost. The partnership with Google Cloud is, according to an April 2026 announcement, expected to generate over 500 million dollars in cloud services and hundreds of millions in service revenue for Onix.
What This Means for Czech Companies
For the Czech market directly, Wingspan 2.0 has limited relevance for now — the platform has no Czech localization, offers no Czech-language support, and its primary target audience is large enterprise companies (Fortune 500 and similar). Yet there are three reasons why Czech CIOs should pay attention:
- Onix's EMEA expansion — the company is actively building a European presence, which increases the chances of the platform reaching Central Europe. For Czech branches of multinational companies already using Onix, deployment could be a matter of months.
- A shift in the consulting paradigm — Wingspan shows the direction enterprise AI is heading: from manual projects to autonomous agents. Even if you are not using Onix today, competing platforms from Microsoft, AWS, or Google Cloud will copy this model.
- Data as a strategic weapon — the Semantic Twin is a concept that Czech companies can adopt even without Wingspan: start building a unified semantic model of their data so they can effectively deploy AI on it.
Pricing and Availability
Wingspan 2.0 is available immediately, though Onix does not publish a flat price list. It is an enterprise platform with individual pricing based on the scope of deployment, number of data sources, and number of AI agents. The platform runs on Google Cloud, so infrastructure consumption is added to the Wingspan price.
For comparison: traditional data modernization at a large company typically costs single-digit to tens of millions of dollars and takes 12–18 months. Onix promises half the cost and one-third the time — but specific numbers depend on the individual scope.
Onix does not yet have a Czech representative or sales office in the Czech Republic. The nearest point of contact is the European headquarters within the newly established EMEA division.
What exactly does "agentic AI" mean in the context of Wingspan 2.0?
Agentic AI refers to autonomous software agents that independently perform complex tasks without the need for human intervention. Within Wingspan 2.0, these agents, for example, automatically discover data sources, convert code between platforms, validate data transfers, or optimize cloud costs — all based on the context they draw from the Semantic Twin.
Is Wingspan 2.0 suitable for smaller companies as well, or only for large corporations?
Wingspan 2.0 is primarily designed for large enterprise organizations with extensive data infrastructure — Onix boasts clients from the Fortune 100. For smaller companies with just a few data sources, deployment would likely not make economic sense. Smaller organizations can use simpler cloud tools from Google Cloud, AWS, or Microsoft Azure, which offer partial automation of data migrations.
What is the difference between a Semantic Twin and a classic digital twin?
A digital twin is a virtual copy of a physical object — for example, a production line, a building, or a vehicle. A Semantic Twin goes a level further: it creates a virtual map of meanings and relationships within data. It doesn't care what a server room looks like, but rather that the customer "Jan Novák" in the CRM is the same person as "Novák Jan" in the ERP system. For AI agents, this semantic layer is essential — without it, they work blindly.
