Former Infosys chief’s Hang Ten Systems raises another $53 million

Hang Ten Systems, an AI startup founded by former Infosys CEO Vishal Sikka, has raised another $53 million only five weeks after closing its initial seed round, according to the company. The financing brings its total funding to $85 million and gives the four-month-old company capital to expand its engineering, consulting, and sales operations.

The new round was led by Xora, Temasek’s early-stage investment platform, with Mayfield—lead investor in the earlier $32 million seed round—also participating. Aramco Ventures, Intel CEO Lip-Bu Tan, Micron CEO Sanjay Mehrotra, and Yahoo co-founder Jerry Yang were also named among the investors. Hang Ten has not disclosed its valuation.

A services model built around AI-led software work

Palo Alto-based Hang Ten is targeting large enterprises, generally companies with more than $10 billion in annual revenue. Its offering combines AI strategy advice with the design, construction, and modernization of business software rather than selling an AI model or a conventional software product.

Sikka told TechCrunch that the company believes AI is changing the economics of software development. In his account, generating code is becoming less costly and time-consuming, while defining requirements, testing systems, and validating business outcomes are becoming more important parts of the work.

That positioning puts Hang Ten in a space between enterprise AI consulting, systems integration, and software development. The company is competing with established service providers while also facing pressure from model companies such as OpenAI and Anthropic, both of which are expanding their enterprise offerings.

Hang Ten says its engineers use an internal framework called Hobie. Co-founder and chief design officer Sanjay Rajagopalan described Hobie as a way to package reusable AI “skills” for regulated industries and complex enterprise projects. The company’s stated aim is to apply those capabilities to production systems, rather than limiting engagements to demonstrations or proof-of-concept projects.

Funding follows reported early customer traction

According to Sikka, Hang Ten is working with existing customers and late-stage prospects across 21 major enterprises. Some of those companies are still in proposal or contract negotiations, so the figure does not necessarily represent 21 signed customers.

The company identified Fresenius Kabi, Saudi Aramco, and Siemens Energy among its customers or engagements. It also said its work spans the United States, Europe, the Middle East, and Asia. Hang Ten claims to have won multiple seven-figure contracts and to be pursuing deals worth eight figures, but it did not provide independently verified contract totals or revenue figures.

Sikka told TechCrunch that one customer signed a multimillion-dollar contract within 25 days of its first meeting with the startup. He characterized that pace as unusually fast based on his experience in enterprise technology. The reported customer activity, he said, helped prompt Xora to approach Hang Ten and offered a potential path to introductions within Temasek’s portfolio.

These adoption signals remain company-reported. The available evidence does not establish how much of the announced pipeline has converted into recurring revenue, how many deployments have reached production, or whether the reported contract pace can be repeated across other large enterprises.

Small teams, large claims, and enterprise constraints

Hang Ten currently has roughly 20 to 25 employees distributed across the United States, the Middle East, and Australia, according to Sikka. The startup plans to hire in Europe and India as it expands.

Rajagopalan said some projects can be handled by teams of two to four people that might previously have required about 30. He also said the company promises customers a tenfold improvement in cost, speed, or a combination of both. Those are vendor claims rather than independently audited benchmarks, and the comparison depends on the type of project, the incumbent provider, and the amount of work performed by the customer.

The company acknowledges that customers or independent third parties still perform final quality checks and certification. That qualification is significant for regulated and mission-critical software. AI may reduce the amount of manual coding, but requirements management, security review, integration, compliance, and operational accountability remain necessary before an enterprise can rely on a system in production.

Sikka said more than half of Hang Ten’s current opportunities involve new projects that companies had previously postponed, rather than work replacing an existing provider. Other engagements, he said, are replacing traditional systems integrators. That mix suggests the startup is pursuing both efficiency gains in existing budgets and projects that become economically viable because of AI-assisted delivery.

What the raise means for enterprise AI buyers

For enterprise technology leaders, Hang Ten’s approach reflects a shift in how AI software development is being sold. Instead of asking customers to adopt a new general-purpose model, the company is offering to take responsibility for a business outcome while using AI internally to accelerate delivery.

That model can be attractive to companies that lack the staff or specialist expertise to redesign legacy systems themselves. It may also appeal to buyers that want an independent implementation partner rather than a service tied to one model provider. Sikka argues that large companies still need trusted partners working in the customer’s interest and independently of the underlying AI platform.

The harder question is whether Hang Ten can maintain delivery quality as it scales. Enterprise software projects often fail or slow down because of fragmented data, unclear ownership, old integrations, security controls, and procurement requirements—not only because code takes too long to write. A smaller delivery team can improve economics, but it can also concentrate operational and domain risk in a young company.

The funding gives Hang Ten room to add people and pursue more contracts, but it also raises the standard for proof. Buyers and investors will likely want evidence that the company’s claimed productivity gains survive production deployment, regulatory review, maintenance, and the long sales cycles typical of major enterprises.

What to watch next

The clearest follow-up signal will be whether Hang Ten converts its reported pipeline into publicly attributable production deployments and repeat contracts. More detail on customer scope, implementation timelines, and measurable cost or speed improvements would help distinguish early sales momentum from a scalable delivery model.

Hiring is another indicator. The startup plans to expand engineering, consulting, and sales teams in Europe and India, so its ability to recruit while preserving a small-team operating model will be important. Its use of Hobie will also merit scrutiny: the value of the framework will depend on whether its reusable components work across regulated sectors without creating new security or maintenance liabilities.

The competitive response will matter as well. Traditional systems integrators are adding generative AI to their services, while OpenAI and Anthropic are building more enterprise tooling. Hang Ten’s position will depend on whether customers value its independence and delivery expertise enough to pay for it when larger providers can offer broader implementation capacity.

Creati.ai perspective

Hang Ten’s financing is notable less because it is another large AI startup round than because it backs a specific theory about enterprise adoption: companies may pay for AI-enabled execution before they are ready to manage AI transformation internally. The company is betting that the scarce resource is shifting from code production to requirements, validation, and accountability.

That thesis is plausible, but the company’s reported contracts and productivity claims are still early evidence. The next stage will test whether Hang Ten can turn a founder-led sales advantage and a small number of fast-moving engagements into repeatable, auditable enterprise delivery. For builders and buyers, production outcomes—not the size of the seed round—will determine whether this model can challenge established services firms.