Key takeaways
- Wonderful’s primary advantage today is deployment: local teams and forward-deployed engineers help enterprises move from AI pilots to working production systems.
- The moat is not simply connecting agents. It is owning the context, evaluations, governance and workflows that turn unpredictable models into dependable enterprise work.
- Wonderful must convert fieldwork into repeatable software while defending its position from model providers and incumbents such as Salesforce, Microsoft and ServiceNow.
It is becoming easier to build an AI agent, but making one work reliably inside a bank, telecom company or energy provider remains extremely difficult.
That gap helps explain why Israeli-Dutch startup Wonderful raised $550 million yesterday at a $5 billion valuation, more than double its valuation six months ago. The company is often described as an AI-agent platform, but investors are underwriting a much bigger ambition: becoming the operational layer that turns unpredictable models into governed, measurable enterprise work.
Wonderful, founded in 2025 by Bar Winkler and Roey Lalazar, has now raised more than $800 million to date. As reported by The Wall Street Journal, Insight Partners led the Series C with a $150 million investment, while Salesforce joined existing backers Index Ventures, IVP, Vine Ventures, 9Yards and Bessemer Venture Partners. In little more than a year, Wonderful has expanded into more than 35 markets, grown to approximately 650 employees and reportedly increased its annual revenue run rate from around $1 million to $70 million.
Those figures are extraordinary, but they do not yet prove that Wonderful has built an enduring software company. Its reported gross margin is approximately 52%, reflecting a business that still relies heavily on local teams and forward-deployed engineers. The $5 billion bet is that Wonderful can turn this labour-intensive deployment advantage into a scalable, independent software layer before foundation-model providers and incumbent SaaS companies absorb the opportunity.
To succeed, Wonderful must prove three things: that forward deployment can become repeatable software, that its orchestration layer retains value as agent protocols become standardised and that it can own the enterprise control plane before model providers and systems of record claim it.
1. Can deployment become repeatable software?
Wonderful’s primary value proposition today is not simply that it builds capable agents. It gets those agents into production.
Access to intelligence is no longer particularly scarce. Enterprises can choose between models from OpenAI, Anthropic, Google, Meta and a growing selection of open-source providers. What remains scarce is the ability to connect those models to proprietary data, permissions, legacy systems and real business processes, then make the resulting system reliable enough to act on behalf of the organisation.
Wonderful addresses this through forward-deployed engineers who work directly with customers, sometimes on-site. They map workflows, connect internal systems, establish tests and guardrails, monitor agents in production and continuously improve their performance. Implementation is not something that happens after the software has been sold. It is central to the product.
Wonderful’s strategic collaboration with McKinsey makes this explicit. The partnership combines Wonderful’s platform and forward-deployed engineers with McKinsey and QuantumBlack’s transformation and change-management capabilities. McKinsey says that while 79% of organisations are experimenting with generative AI, fewer than 10% have scaled AI agents. The bottleneck is not ambition or access to models. It is execution.
Wonderful’s geographic strategy compounds that deployment advantage. While Sierra and Decagon concentrated heavily on the US customer-service market, Wonderful built its beachhead across Europe, Latin America, Asia and the Middle East. The company told Geektime that it deliberately focuses on languages other than English and Mandarin, where the market is already crowded with large players.
This was more than geographic arbitrage. Language, regulation, local culture and legacy infrastructure all affect whether an agent works in production. In a Google Cloud case study, Wonderful explains that producing a voice agent for Italy can require multiple variations to account for dialects, gender and regional differences. A generic English-language agent cannot simply be translated and expected to deliver the same performance.
Customer service also gave Wonderful a strong initial wedge. It contains a large volume of repetitive work, has clear human benchmarks and offers an ROI that can be measured quickly. Once Wonderful connects to an organisation’s systems and learns how its processes operate, it can expand into adjacent workflows.
At Colombia’s Banco Caja Social, Wonderful says it took a collections agent from zero to production in 19 days. Within six weeks, the agent completed more than 43,000 calls, increased contact-to-promise conversion from 45% to 65% and reduced average handling time by 33%. The bank then used the same foundation to launch a second agent for inbound customer service.
At Telefónica, Wonderful reports that a billing agent resolves 77% of the relevant issues it handles. Other deployments include Petrol Ofisi, where the company says it reduced IT call-handling time by 75% in three weeks, and ELTA Systems, where an internal service agent reportedly eliminated peak waiting times and quadrupled call capacity within five weeks. These are company-published results rather than independently audited figures, but they show the commercial playbook Wonderful is trying to repeat.
Its Israeli customers reportedly include Bezeq, Bank Hapoalim and Paz, while Deutsche Telekom is among its international clients. The pattern is consistent: identify a constrained workflow, reach production quickly, demonstrate a measurable result and use the initial integration as a bridge into more of the enterprise.
The services-heavy model should not automatically be viewed as a weakness. In “Trading Margin for Moat,” Andreessen Horowitz partner Joe Schmidt argues that “sometimes human-intensive services are necessary to create transformative software.” Salesforce, ServiceNow and Workday all required significant implementation work before developing scalable platforms and partner ecosystems.
As I argued recently in “The $9 Billion Bet on Forward-Deployed Engineers,” fieldwork only becomes a moat when it becomes product. If every engagement starts from zero, the startup is building a consultancy. If each deployment produces reusable integrations, evaluation tools and knowledge that make the next one faster, the forward-deployed team becomes a product engine.
This is the first test for Wonderful. The important metric is not how many engineers it can place with customers, but whether the time, labour and cost required for each subsequent deployment decline.
2. Does orchestration remain valuable as connectivity becomes standardised?
As enterprises deploy more agents, they risk recreating the fragmentation of the SaaS era. Sales may adopt one agent, customer support another and finance a third, each connected to different models, data sources, applications and permissions.
Consider a customer requesting a refund. One agent might interpret the request, another retrieve the customer’s history, another review the relevant policy, another authorise the transaction and another update the CRM. The system must determine which agent should act, what information it can access, which model is appropriate and when a human needs to intervene.
Wonderful wants to provide that control layer. Its AI operating system is designed to coordinate agents, workflows, enterprise context, integrations and governed execution. Customers can choose different models for different tasks and deploy the platform in the cloud or on-premise.
Wonderful also says customers can export their agents, skills, tools and governance configurations, run external agents inside Wonderful or use Wonderful agents elsewhere. This “open by default” approach matters because enterprises will be reluctant to hand a critical operating layer to a platform that locks them into one model provider.
However, orchestration is important without being inherently defensible. Open standards such as Model Context Protocol and the Agent2Agent Protocol are standardising how agents connect to data, tools and other agents. Basic agent routing and connectivity could therefore become commodities.
The infrastructure opportunity is not merely connecting agents. It is owning the operational layer that turns unpredictable models into governed, measurable enterprise work.
That layer includes enterprise context, identity, permissions, evaluations, monitoring, exception handling and accountability. A company needs to know not only that an agent completed a task, but why it took a particular action, which information it accessed, whether it followed company policy and who remains responsible when something goes wrong.
Wonderful’s testing and improvement tools could become particularly valuable here. According to Geektime, its platform can create tests, automate aspects of prompt optimisation, monitor performance and use AI to analyse interactions. A platform observing millions of real conversations and workflows could build proprietary evaluation datasets that generic benchmarks cannot reproduce.
This is where the analogy with Cursor becomes relevant. Cursor initially created value on top of models from OpenAI and Anthropic. Its advantage came from controlling the developer workflow and designing the interface, context and tools around those models.
Once Cursor owned that workflow and accumulated data about how developers use AI to search, write, edit, run and debug code, it moved deeper into the stack. It introduced Composer, its own specialised agentic coding model, trained through reinforcement learning on software-engineering tasks. Cursor still supports third-party frontier models, but can now route work between external and proprietary models.
Wonderful could follow a similar path. Its advantage today is not a proprietary foundation model, but its proximity to production workflows and the data created while deploying and improving agents inside enterprises. Over time, it could use those interactions to build specialised evaluation systems, fine-tune models for particular industries and languages or train proprietary models where doing so improves performance or economics.
An application company does not need to own the entire stack from the beginning. It can first own the workflow and customer relationship, then move down the stack where its proprietary data gives it a right to win.
The second test for Wonderful is therefore whether it can build durable value above the connectivity layer. Its moat cannot be the ability to pass tasks between agents. It must come from understanding how the enterprise operates and proving that AI completed the work correctly.
3. Can Wonderful own the control plane independently?
Wonderful’s long-term competitors are not limited to Sierra and Decagon. Model providers including OpenAI, Anthropic and Google are moving into enterprise applications and deployment. Systems of record including Salesforce, Microsoft and ServiceNow already control valuable enterprise data and workflows, while consultancies such as McKinsey, Accenture and Deloitte own transformation budgets and senior customer relationships.
This raises a fundamental question: why should a separate orchestration company own the enterprise AI control plane?
The foundation-model companies can combine orchestration with proprietary intelligence. Salesforce can build agents around customer data already stored in its platform. ServiceNow can coordinate agents around established IT and enterprise workflows, while Microsoft can distribute agents through Office, Teams, Dynamics and Azure.
Wonderful’s strongest answer is neutrality. It can sit above different models and enterprise applications, allowing customers to select the best technology for each workload without replacing their existing stack. As models improve and prices fall, a neutral platform should theoretically benefit rather than be disrupted.
Salesforce’s investment in Wonderful is therefore both validation and a source of tension. Salesforce wants Agentforce to coordinate work around the customer data it already controls, while Wonderful wants to sit across multiple models and enterprise applications. The investment could represent a partnership, a hedge or an option on a company that might otherwise become a strategic threat.
Wonderful’s international footprint may give it time to establish this position. Local engineers, language expertise, regulatory familiarity and relationships with large enterprises are harder to reproduce than an agent demo. By avoiding a head-on fight for the same early US customers, Wonderful has built a credible alternative while the category is still forming.
Its extraordinary funding also contributes to that advantage. A $550 million round signals to customers, employees and partners that Wonderful is likely to remain in the market. Financial durability matters when a CIO is choosing infrastructure that may eventually coordinate hundreds of business-critical agents.
Capital can also crowd out competitors. A smaller startup may have strong technology, but customers will question whether it can match Wonderful’s geographic coverage, local deployment capacity and balance sheet. Employees may prefer the perceived category leader, while investors may hesitate to fund another company directly in its path.
This is venture capital as kingmaking. Insight Partners has now led three consecutive Wonderful rounds and describes its latest investment as “tripling down” on the company. The objective is not only to finance growth, but to establish Wonderful as the default independent platform before the market fully settles.
Capital, however, is not the same as defensibility. A large balance sheet can subsidise bespoke deployments, aggressive hiring and geographic expansion for longer than a smaller competitor could afford. It can also conceal whether the underlying business is becoming more efficient.
Wonderful’s reported 52% gross margin and approximately $108,000 in annualised revenue per employee do not yet resemble mature software economics. At a $5 billion valuation, the company is valued at more than 70 times its reported revenue run rate. Investors are underwriting a future in which its deployment work becomes more repeatable, customers expand into multiple workflows and gross margins rise as implementation knowledge is converted into software.
There are historical precedents. Andreessen Horowitz notes that ServiceNow had a gross margin of 63.2% at IPO and Workday’s was 54.1%, well below the margins they later achieved. Salesforce also invested heavily in implementation before developing the partner ecosystem that helped it scale.
The third test is whether Wonderful can make the same transition while remaining an independent platform. It must become sufficiently embedded to be indispensable, yet sufficiently open to avoid being absorbed into the ecosystems of the companies beneath and around it.
The real $5 billion bet
Wonderful has already proved that there is enormous demand for getting AI agents out of pilot mode and into production. It has also demonstrated that an international, deployment-led strategy can create a meaningful alternative to competing head-on for the same US customers.
What it has not yet proved is whether that deployment machine can become repeatable software, whether its orchestration layer will retain value as agent protocols become standardised and whether an independent company can own the enterprise control plane before model providers and systems of record absorb it.
That is the real $5 billion bet. Wonderful is not merely trying to build better agents or connect them together. It is trying to own the operational layer that makes enterprise AI reliable, governed and economically useful.
Deployment has given Wonderful a credible head start. The question is whether it can turn that head start into infrastructure.
He is a former General Partner at Google Ventures (GV) in Europe, former head of Google for Entrepreneurs in Europe, and founding head of Campus London, Google's first startup hub. Eze writes on Israeli tech, venture capital, artificial intelligence, and founder strategy.
He is also the founder of Techbikers, a nonprofit that brings together the startup ecosystem on cycling challenges in support of Room to Read.
- Wonderful’s $5B Bet to Own the Enterprise AI Layer - September 3, 2026
- Why Israeli Unicorns Are Built in America - September 2, 2026
- The Right Model for the Job - September 1, 2026
