Ex Microsoft APAC President Launches kAIgentic With $10 Mn Funding To Build AI For Global Enterprises

Former Microsoft Asia Pacific (APAC) president, Ahmed Mazhari, has launched agentic AI startup kAIgentic in strategic partnership with $10 Mn  investment from SMBC Group, Japanese multinational financial giant .

Headquartered in Singapore with core engineering operations in India, the startup is building specialised in AI deployment infrastructure for large-scale enterprise operations. 

In a strategic move that sets it apart from typical early-stage ventures, kAIgentic will debut its tech stack within a live, highly-regulated environment, deploying its platform at SMBC Group. 

SMBC Group is a global financial group. Headquartered in Tokyo and with a 400-year history, SMBC Group offers a diverse range of financial services, including banking, leasing, securities, credit cards, and consumer finance. The group recently announced a three-year IT investment of approximately JPY 1 Tn in April 2026. The investment will be used to modernise its IT infrastructure and accelerate AI adoption by strengthening talent, upgrading the operating environment, expanding employee training, and establishing AI-native business processes. 

kAIgentic serves as an intelligence layer that captures the unwritten, day-to-day operational logic of how work actually gets done within a large enterprise. The platform then uses this contextual data to build domain-specific AI agents, which run in live production under continuous human supervision.

The startup enters a crowded but rapidly expanding agentic AI market, where companies globally are racing to move beyond experimental software pilots into real-world enterprise operations. 

kAIgentic’s true differentiation lies in building products for real-world operations. The startup uses institutional sandboxes to stress-test its complex AI workflows from day one.

The launch marks a high-profile return to entrepreneurship for Mazhari, a veteran of enterprise technology leadership. Prior to steering Microsoft’s APAC business, Mazhari spent over two decades anchoring major industrial and outsourcing players, most notably as part of the founding team and later the chief growth officer at Genpact.

Now, his new venture takes aim at what Mazhari identifies as a critical bottleneck in the current AI boom — safe, enterprise-grade deployment. He believes that the primary challenge for enterprises is no longer accessing powerful LLMs, but implementing AI models securely within the strict guardrails of governance, compliance and accountability.

“The bet behind kAIgentic is that enterprise AI will be won not by model access alone, but by the application layer that turns AI into governed operations,” Mazhari added.

This focus on governed operations explains the unique structural setup of the company’s first major corporate alliance.

kAIgentic’s ‘Dual-Engine’ Corporate Playbook

Most AI transformation teams are typically limited to one product or enterprise, while external vendors often lack the context that comes from working inside it. To address this gap, kAIgentic has two distinct operational engines in partnership with the Japanese giant.

The first engine serves as an internal transformation driver, deploying kAIgentic’s software directly within SMFG’s native banking workflows to overhaul its daily operations. Meanwhile, the second engine functions as an independent product platform, built to commercialise these battle-tested AI systems and sell them to the broader global enterprise market.

This structure addresses a classic enterprise dilemma, it gives kAIgentic a high-stakes ‘Customer Zero’ to validate its software in a real-world banking environment while allowing the startup to retain intellectual property ownership and scale independently.

Protecting this operational model requires a governance structure that balances startup agility with banking oversight. To maintain this balance, kAIgentic’s three-member board includes Mazhari as CEO, one SMBC Group-appointed director, and one independent director.

The $71 Bn Enterprise AI Opportunity

India’s AI ecosystem is rapidly transitioning from experimental pilots to production-first, workflow-native systems. According to Inc42’s ’Bharat AI Startups Report 2026’, the nation’s AI market is projected to cross $126 Bn by 2030, with the potential to unlock a massive $1.7 Tn in GDP impact by 2035.

While consumer AI is driving early mass adoption, the largest value pool is enterprise AI, which is set to skyrocket from $11 Bn to $71 Bn by the end of the decade.

In this landscape, competitive moats will increasingly depend less on frontier model research and more on execution, specifically the ability to ship, integrate, and scale AI systems where real enterprise workflows operate. 

To effectively capitalise on this opportunity, especially in the highly regulated banking space, kAIgentic’s thesis focuses on creating human-in-the-loop AI systems that absorb organisational knowledge rather than acting as standalone automation. Instead of building isolated domestic opportunities or early-stage consumer products, the startup is developing an Asian-born enterprise AI platform in partnership with a major global financial institution. 

“The bottleneck in enterprise AI is no longer model capability. It is whether large organisations can absorb intelligence into their most critical operations, at speed, with higher standards and policies they must maintain,” added Mazhari.

To fuel this, the startup is aggressively hiring Indian engineering talent that can build production-grade products for banking environments, where systems must operate under continuous, audit-heavy supervision. 

Once proven within banking operations, the startup’s long-term roadmap involves scaling the platform to tackle adjacent, heavily regulated global sectors, including healthcare, consumer packaged goods (CPG), retail, and telecommunications.

Ultimately, kAIgentic is betting that the true value of the AI era will lie not in creating intelligence, but in deploying intelligence. In a market crowded with model builders, the startup’s success will test whether operational execution is the ultimate moat in enterprise tech.

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