Hang Ten Systems has raised an additional US$53 million in seed funding, just five weeks after closing its first seed round, as large enterprises search for ways to move artificial intelligence from experimentation into core operations.
The new round was led by Xora, a Temasek-backed fund focused on AI and deep tech. Mayfield, which led Hang Ten’s earlier seed financing, and Aramco Ventures also joined the round. The latest cheque brings the Menlo Park-based company’s total funding to US$85 million.
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For a company still at seed stage, the number is striking. But it also reflects a broader shift in enterprise AI: the bottleneck is no longer whether large language models can write code, summarise documents, or automate parts of office work. The harder problem is making those capabilities reliable, secure, and useful inside complex organisations that run on legacy software, strict governance, and fragmented data.
Hang Ten is trying to position itself in that gap. The company provides advisory, transformation, and applied-AI services to large enterprises, using what it describes as an AI-native delivery model. In practical terms, that means combining agentic code generation, a reusable library of technical “skills”, and domain expertise to build, change, and operate enterprise software with smaller teams and shorter timelines.
Its work so far spans software development, finance analytics, enterprise migrations, human resources, and other internal business functions.
From thesis to signed contracts
According to the company, the two seed rounds closed five weeks apart. During that period, Hang Ten signed several multi-million-dollar contracts with global enterprises, covering both advisory and software development work. Some projects are already underway, while a few have been completed.
The engagements range from short advisory assignments to multi-year development programmes across multiple industries. The new capital will be used to expand delivery capacity, hire across engineering and consulting, and continue building the company’s platform, infrastructure, and agentic skills library.
“Enterprise technology has long been one of the largest cost lines in essential industries — slow to change, and slower still to translate AI into real economic value,” said Phil Inagaki, Managing Partner and Chief Investment Officer at Xora. “The bottleneck is implementation: deploying it securely, economically and at speed.”
That point is especially relevant in Southeast Asia, where large banks, telcos, manufacturers, energy firms, and government-linked companies have been testing generative AI over the past two years. Many have run pilots in customer service, software engineering, document processing, and knowledge management. Fewer have rebuilt workflows around AI in ways that materially reduce cost or improve productivity.
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Singapore has been one of the region’s most aggressive markets in pushing enterprise AI adoption, backed by national AI strategies, public-sector digitalisation, and a deep base of regional headquarters. But across the wider region, adoption remains uneven. Enterprises in Indonesia, Vietnam, Thailand, Malaysia, and the Philippines often face a different mix of constraints: fragmented legacy systems, talent shortages, language localisation, regulatory ambiguity, and the need to prove near-term return on investment.
That is where firms such as Hang Ten see an opening. Rather than selling a horizontal AI tool and leaving customers to figure out implementation, the company is offering a services-led model that embeds AI into delivery itself.
A familiar problem with a new toolkit
Hang Ten’s proposition is not entirely new. Enterprises have long hired systems integrators and consulting firms to modernise software, migrate data, implement enterprise resource planning systems, and redesign operations. What has changed is the toolset.
Agentic AI systems can break down tasks, write or modify code, call software tools, and perform multi-step work under human supervision. In theory, this allows engineering and consulting teams to do more with fewer people. In practice, enterprise use requires guardrails: security controls, audit trails, quality assurance, compliance checks, and a clear understanding of where human experts still need to intervene.
That distinction matters because large companies rarely lack access to AI tools. What they lack is the ability to combine those tools with production-grade workflows. A chatbot demo is easy. Refactoring a core finance system, migrating enterprise software, or automating HR processes without breaking compliance is much harder.
Hang Ten says its model is built for that second category. Its reusable skills library appears to be a core part of the pitch: instead of solving each customer problem from scratch, the company can reuse patterns, code, and process knowledge across engagements.
The company is already working with Aramco on applications supporting operations across several functions. Aramco Ventures CEO Mahdi Aladel said the appeal lies in the breadth of possible use cases inside a large organisation.
“The more closely we look at their approach, the more functions and use cases we find across our own operations where it would fit,” he said.
Siemens Gamesa Renewable Energy is also among Hang Ten’s customers. Its CEO, Vinod Philip, said the company began working with Hang Ten two months ago on a short list of outcomes, before widening the scope after early delivery.
A crowded field, but a large prize
Hang Ten will not have the market to itself. The company is entering a field dominated by global consulting and IT services giants such as Accenture, Deloitte, IBM Consulting, Capgemini, Cognizant, Infosys, TCS, Wipro, and HCLTech, all of which are investing heavily in generative AI delivery. In Southeast Asia, these firms already have deep relationships with banks, telecom operators, manufacturers, and public-sector agencies.
There are also software engineering specialists such as EPAM and Globant, as well as a growing crop of AI-native startups offering coding agents, workflow automation, and enterprise AI deployment tools. Hang Ten’s challenge will be to show that its AI-native services model is not just faster in early projects, but repeatable across sectors, regions, and heavily regulated environments.
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Its investor base may help. Xora’s link to Temasek gives Hang Ten a potential bridge into Singapore and regional enterprise networks. Aramco Ventures offers exposure to large-scale industrial deployment, particularly in energy and infrastructure. Mayfield brings early-stage company-building experience, while individual backers include Intel CEO Lip-Bu Tan, Micron chairman and CEO Sanjay Mehrotra, and Yahoo co-founder Jerry Yang. Yang has also joined Hang Ten’s board.
For Southeast Asian enterprises, the significance is less about another US startup raising a large seed round and more about what the financing signals. AI implementation is becoming a board-level concern, not an innovation lab experiment. Companies are under pressure to extract productivity gains, but they cannot afford failed deployments in critical systems.
Hang Ten founder and CEO Dr Vishal Sikka (who is former CEO of Indian software giant Infosys) framed the opportunity around that tension between ambition and dependability.
“AI can be the most powerful amplifier we have ever built. But in the enterprise, AI’s power must go hand-in-hand with reliability,” he said.
That is likely to be the defining test for Hang Ten. The market is full of AI promises. The enterprises it is targeting will care less about novelty than whether projects ship, systems hold up, and costs come down.
If Hang Ten can prove that its model delivers those outcomes consistently, its unusually large seed round may look less like hype and more like early infrastructure for a new generation of enterprise services.
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