Vishal Sikka Launches Hang Ten Systems for AI-Driven IT Services

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Vishal Sikka’s startup targets AI in IT services

  • Former Infosys CEO Vishal Sikka has launched Hang Ten Systems to bring AI-driven development and automation to enterprise IT services.
  • The Bay Area startup raised a seed round led by Mayfield, with a strategic investment from Aramco Ventures and angel participation.
  • Hang Ten says it helps enterprises continuously build, modify, and operate software using AI, aiming to break the headcount-driven economics of traditional services.
  • Early customers are testing the model, as the broader IT services market debates whether AI expands or disrupts the sector.

AI Scaling Outsourced IT Services

  • In this article, “IT services” refers to outsourced work enterprises rely on to customize, integrate, run, and continuously change large software systems (ERP, HR, finance, data platforms, and the glue code between them).
  • Hang Ten’s bet is that modern AI can take over a meaningful share of that day-to-day delivery work—especially repetitive build/modify tasks—so outcomes scale more like software than like staffing.
  • Timing note: the details below reflect what was publicly reported around Hang Ten’s launch in late June 2026; early-stage company facts can evolve quickly.

Vishal Sikka Launches Hang Ten Systems with $32 Million Funding

For decades, IT services firms have built massive businesses by taking on the unglamorous but essential work of enterprise software: customization, integration, and ongoing maintenance. That model—often measured in billable hours and large delivery teams—helped companies outsource complexity at scale.

Vishal Sikka, the former CEO of Infosys, is now arguing that generative AI can do much of that work differently, and potentially far faster. His new startup has emerged with a $32 million seed round led by Mayfield, alongside a strategic investment from Aramco Ventures and participation from angel investors. The company’s board includes Yahoo co-founder Jerry Yang.

Hang Ten Systems Raises $32M

  • Company: Hang Ten Systems
  • Founder: Vishal Sikka (former CEO of Infosys)
  • Funding: $32 million seed round
  • Lead investor: Mayfield
  • Strategic investor: Aramco Ventures
  • Other investors: angel investors (not individually detailed in the reporting)
  • Board: includes Jerry Yang (Yahoo co-founder)
  • Named early customers (as stated by the company): Siemens Gamesa Renewable Energy; Fresenius
  • Primary reporting referenced in this article: TechCrunch

The timing is pointed. Hang Ten is launching into a market where incumbents—including Infosys—are racing to adapt through partnerships with AI model providers such as Anthropic and OpenAI. At the same time, investors and analysts are openly questioning whether AI will expand the addressable market for services or compress it by automating the very tasks that made the industry so profitable.

Mayfield Managing Partner Navin Chaddha told TechCrunch the company “just got started a month back” and already has customers—an early signal that some enterprises are willing to test an AI-native services model, particularly when it comes from a founder with deep enterprise credibility.

AI-Driven Development and Automation for Enterprises

Hang Ten describes itself as helping enterprises “continuously build, modify, and operate software” using AI. In practical terms, it is positioning as an enterprise AI services company built around agentic code generation, reusable AI “skills,” and domain expertise—an attempt to repackage services delivery around software-like leverage.

That framing goes straight at the core economic assumption of traditional IT services: scale comes from headcount. Mayfield’s thesis, as described in reporting, is that classic services businesses scale linearly with people, while Hang Ten is designed so its leverage increases with every project—because work done once can be reused as AI capabilities and delivery patterns accumulate.

From Intent to Shipped Change
Inputs → Workflow → Outputs (a simple way to think about “agentic delivery”)

  • Inputs
  • Business intent (requirements, tickets, change requests)
  • Existing systems context (codebase, configs, integrations, data models)
  • Guardrails (security policies, compliance constraints, testing standards)
  • Workflow
  • AI agents propose changes (code/config/integration steps)
  • Humans review and steer (domain experts + delivery leads)
  • Automated checks run (tests, policy checks, deployment gates)
  • Reusable “skills” are captured (patterns that can be applied to the next project)
  • Outputs
  • Shipped changes (features, fixes, integrations)
  • Operational runbooks/automation (how it’s monitored and maintained)
  • A growing library of reusable delivery patterns (the compounding-leverage claim)

The bet is not merely that AI can write code, but that it can compress the cycle of enterprise change: building new functionality, adapting existing systems, and keeping software running as requirements shift. This is the work that historically required large teams coordinating across business stakeholders, legacy systems, and vendor platforms.

Hang Ten’s pitch lands amid a broader industry debate. Jefferies analysts have argued IT services may be among the first sectors to face meaningful AI disruption. On the other side, Infosys chairman Nandan Nilekani has suggested AI could expand the industry’s addressable market. Infosys has told investors that “AI-first services” could represent a $300 billion to $400 billion market by 2030—an optimistic view that assumes demand grows as capabilities improve.

Hang Ten is effectively testing the more disruptive hypothesis: that AI changes not just what gets delivered, but how delivery is priced, staffed, and repeated.

Strategic Partnerships and Early Clients

Hang Ten’s investor list and board composition are part of its early strategy to establish enterprise trust quickly. The seed round was led by Mayfield, with Aramco Ventures taking a strategic stake, and Jerry Yang joining the board—signals that the company is aiming for credibility in boardrooms as much as in engineering teams.

[[BLOCK_START]]
| What’s been publicly stated | Who | What’s known from reporting | What’s not yet detailed publicly |
|—|—|—|—|
| Lead investor | Mayfield | Led the $32M seed round; Mayfield’s Navin Chaddha said the company “just got started a month back” and already has customers | Specific terms/valuation; full cap table |
| Strategic investor | Aramco Ventures | Took a strategic investment in the seed round | Strategic commercial scope (if any) |
| Board member | Jerry Yang | Yahoo co-founder; on Hang Ten’s board | Board role specifics; governance details |
| Named customer | Siemens Gamesa Renewable Energy | Listed by the company as a customer working on “AI-native project delivery” | Project scope, timelines, measurable outcomes |
| Named customer | Fresenius | Listed by the company as a customer working on “AI-native project delivery” | Project scope, timelines, measurable outcomes |
[[BLOCK_END]]

Chaddha’s comment that the company already has customers despite being only about a month old underscores the current appetite for experimentation. Many enterprises are under pressure to modernize systems, adopt AI, and deliver faster—yet they are also wary of risk, compliance issues, and operational fragility. That tension creates an opening for new delivery models, but also raises the bar for reliability.

Hang Ten is entering a competitive landscape where incumbents are not standing still. Large IT services firms are actively repositioning around AI through partnerships with leading model providers, seeking to protect existing client relationships while offering “AI-first” engagements. The question is whether a startup can win meaningful share by offering a structurally different model—one that promises compounding productivity rather than incremental efficiency.

Sikka, in a separate blog post announcing the venture, said Hang Ten was already helping large enterprises “hang ten on the biggest wave of our lifetimes,” framing AI as a once-in-a-generation platform shift rather than a feature upgrade.

Headquarters and Hiring Plans

Scaling AI-Native Delivery Safely
How an AI-native services firm typically scales delivery (and where it can break)
1) Land a pilot with a narrow, high-value scope

  • Checkpoint: clear success criteria (cycle time, defect rate, operational stability)

2) Embed “forward deployed” delivery close to the customer

  • Checkpoint: access to real systems + stakeholders; without this, AI output can’t be validated in context

3) Build a repeatable delivery loop

  • Checkpoint: automated tests, security reviews, and deployment gates must be in place before expanding scope

4) Capture reusable patterns as “skills”

  • Checkpoint: reuse only works if patterns are documented, versioned, and proven across more than one environment

5) Expand to more teams/regions

  • Checkpoint: governance and reliability (not just hiring velocity) becomes the limiting factor in enterprise rollouts

Key details at a glance (as reported)

  • Startup: Hang Ten Systems
  • Founder: Vishal Sikka (former Infosys CEO)
  • Funding: $32 million seed round led by Mayfield, with a strategic investment from Aramco Ventures and angel participation
  • Board: Includes Yahoo co-founder Jerry Yang
  • Early customers named: Siemens Gamesa Renewable Energy and Fresenius
  • Primary reporting referenced: TechCrunch

Hang Ten is headquartered in the Bay Area, placing it close to both enterprise tech talent and the venture ecosystem backing its early growth. The company told TechCrunch it is hiring across delivery, engineering, sales, and leadership—an important detail for a startup that is simultaneously selling services outcomes and building AI-driven delivery capabilities.

The hiring focus also hints at how Hang Ten intends to operate. “Delivery” suggests it will run real projects for enterprises rather than purely shipping a self-serve product. “Forward deployed” roles—reflected in the title of one executive—imply teams embedded close to customers, a common pattern in enterprise AI where adoption depends on hands-on integration with existing systems and workflows.

At the same time, Hang Ten says it plans to expand across multiple locations globally to meet enterprise demand. That ambition mirrors the global footprint of traditional IT services firms, but with a different premise: instead of scaling primarily by adding large offshore teams, the company is betting it can scale by combining domain experts with AI automation and reusable capabilities.

The broader market context makes this expansion plan more than a routine growth statement. Infosys shares were reported down more than 35% this year, reflecting investor uncertainty about how AI will reshape services economics. In that environment, a startup promising a new cost and speed curve will face both heightened interest and heightened scrutiny.

If Hang Ten can demonstrate repeatable delivery with fewer people per project over time, global expansion becomes less about building massive benches and more about placing the right expertise near customers—while the AI layer carries more of the repetitive load.

Vishal Sikka’s Background and Experience

Hang Ten’s credibility is tightly linked to Vishal Sikka’s track record in enterprise software and IT services. He spent 12 years at SAP building enterprise software and later served as a board member for Oracle, giving him deep exposure to the platforms that dominate corporate IT.

Sikka’s Fit for This Bet
Why Sikka’s background maps to this specific bet

  • Enterprise software depth: 12 years at SAP building enterprise software (the kind of complex platform work IT services teams often customize and integrate).
  • Services economics exposure: CEO of Infosys (2014–2017), a company known for industrial-scale delivery models built around large teams and long-running enterprise engagements.
  • AI venture experience: Founded VianAI after Infosys, giving him prior reps building an enterprise AI company before launching an AI-native services model.
  • Governance/network signal: Board-level experience (including Oracle) and a board that includes Jerry Yang can matter when selling into risk-aware enterprise buyers.

Sikka is also known for leading Infosys as CEO from 2014 to 2017—experience that matters because Infosys is one of the world’s best-known IT services firms, and because Hang Ten is explicitly challenging the traditional services model that companies like Infosys helped industrialize.

After stepping down from Infosys, Sikka founded VianAI, which emerged from stealth in 2019 with $50 million in seed funding and later raised $140 million in a 2021 round led by SoftBank Vision Fund 2. Mayfield’s Navin Chaddha has emphasized that Hang Ten is distinct from VianAI: the earlier company focused on enterprise AI applications and analytics tools to support decision-making, while Hang Ten is positioning as an AI-native services company focused on how software is built and operated.

The early team includes executives who have worked with Sikka across SAP, Infosys, and VianAI, according to their LinkedIn profiles. Named leaders include co-founders Navin Budhiraja (CTO) and Sanjay Rajagopalan (chief design officer), as well as Tao Liu (senior vice president of forward deployed engineering). That continuity suggests Hang Ten is not assembling a team from scratch, but re-forming a group with shared operating history—often a key advantage when the product is as much “delivery system” as it is technology.

The Future of IT Services: A New Era with Hang Ten Systems

Transforming Enterprise Software Development

Hang Ten is launching into a moment when the IT services industry is being forced to explain its future. One camp argues AI will expand demand—more automation means more projects become feasible. Another argues AI will compress margins and reduce the need for large teams that historically executed customization and maintenance.

Hang Ten’s model is aligned with the second view: that AI changes the unit economics of delivery. By emphasizing agentic code generation and reusable AI skills, it is trying to turn services work into something closer to a compounding asset—where each engagement improves the next.

Whether that transformation sticks will depend on outcomes enterprises care about: speed, reliability, and the ability to operate software continuously as requirements change. If Hang Ten can show that AI-native delivery reduces cycle times without increasing risk, it could pressure incumbents to rethink how they staff, price, and structure engagements.

Balancing Upside and Constraints
Potential upside vs. real constraints (what enterprise buyers will likely weigh)

  • Upside
  • Faster change cycles: shorter time from request → shipped change.
  • Reuse leverage: patterns captured once can reduce effort on future work.
  • Lower marginal cost: fewer people needed per incremental change if automation holds.
  • Constraints / risks
  • Reliability burden: enterprise systems need predictable behavior; AI-generated changes must be testable and auditable through strong engineering gates.
  • Integration reality: the hardest work is often messy interfaces, data quality, and legacy constraints—not just writing new code.
  • Security and access: AI-driven delivery still depends on safe access to code, data, and environments; governance can slow rollouts.
  • Vendor and incumbent response: large services firms can bundle AI into existing contracts and relationships, raising the bar for switching.

The opportunity is clear, but so are the constraints implied by the market Hang Ten is entering. Enterprises are cautious by design, and the work Hang Ten is targeting—core enterprise software—tends to be deeply integrated, regulated, and business-critical. That makes “AI-driven automation” both attractive and sensitive.

Hang Ten also faces a competitive reality: incumbents are already partnering with leading AI providers and pitching “AI-first services” to existing clients. The startup’s advantage may be structural—building from day one around AI-native delivery rather than retrofitting legacy models—but it will still need to prove it can deliver consistently at scale.

For now, the company’s early funding, recognizable backers, and initial customer names suggest it has opened the door. The next test is whether Hang Ten can turn early pilots into repeatable delivery—and whether its promise of growing leverage with every project holds up in the messy, high-stakes world of enterprise IT.

This analysis is written from the perspective of Martin Weidemann (weidemann.tech), drawing on hands-on experience building and scaling technology-driven businesses where delivery economics, automation leverage, and operating in regulated, multi-stakeholder environments are central constraints.

This piece reflects publicly available information about Hang Ten Systems around its June 2026 launch and includes analysis of its relevance to enterprise IT services. Some details—such as customers, hiring, and product capabilities—may change as the company shares more information or expands. Any market-size figures are attributed estimates and should not be treated as definitive.

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