The pitch is blunt: you can't turn a flip phone into a smartphone just by adding an app store. A well-known Bengaluru-based entrepreneur is betting that the same logic applies to workplace software in the AI era — and he's willing to self-fund the experiment to prove it.

This isn't the founder's first rodeo. Over roughly twenty years, he has built and backed several companies spanning web infrastructure, domain services, and fintech, typically funding the early stages himself before opening the door to outside capital. His newest venture follows the same script, this time aimed squarely at the workplace productivity category long dominated by giants like Microsoft and Google.

The new company, quietly running internally since the spring, bundles project management, document editing, and file storage into a single AI-native workspace. Rather than treating AI as a bolt-on chat feature, the product is designed so that AI is embedded directly into how work gets created and organized — and it's built to work with multiple AI models instead of locking users into one provider.

One of the more striking details is how the product itself was built. The founder says the initial version came together in roughly three months, with AI tools used extensively throughout development — work he estimates would have taken well over a year with a much larger team before generative AI tools existed. That compressed timeline is part of the broader argument he's making: AI doesn't just change what software can do, it changes how fast new software can be built and shipped.

The product has spent the past several months in internal use across the founder's existing companies, serving as a live testing ground before a planned rollout to outside customers. The initial go-to-market push will target mid-sized businesses, starting with knowledge workers in technology, consulting, and professional services.

This founder isn't alone in choosing to self-fund an AI-era enterprise bet before bringing in outside money. Other prominent investors and operators have taken a similar path recently, launching AI-focused ventures with personal capital first and raising larger institutional rounds only after proving early traction. It suggests a broader pattern among experienced operators: in a field moving as fast as enterprise AI, speed and control in the earliest months may be worth more than an early valuation bump from outside funding.

Whether this particular approach to workplace software catches on will depend on execution as much as timing. Large incumbents have enormous distribution advantages, and plenty of well-capitalized startups are chasing the same opportunity. But the underlying thesis — that AI-native design beats AI bolted onto legacy systems — is one that's likely to keep shaping how enterprise software gets rebuilt over the next few years.