Why one of India's most valuable bootstrapped SaaS bets isn't in a hurry to go public

For a company that analysts have quietly valued at close to $1 billion, Netcore Cloud is behaving strangely. It has no venture capital investors pressing for an exit. It is profitable. By most conventional measures, it has already cleared the bar that sends founders reaching for bankers and roadshows. And yet Rajesh Jain, who has run the company for nearly three decades, keeps declining to set a listing date. The reason has less to do with market timing than with a bet he is making on how software itself gets sold.

"The reason we haven't rushed into an IPO is AI," Jain says. Public markets reward predictable quarterly earnings. Building AI-first products demands the opposite — investment ahead of revenue, constant experimentation, and profitability that can wobble for a few quarters at a time. Without outside investors dictating a timeline, Netcore can make bets that take years to pay off instead of one that must clear a ninety-day bar.

That patience is now being pointed at a specific, unglamorous inefficiency that has quietly defined digital marketing for a decade: brands routinely pay to acquire the same customer twice.

The rent nobody questions

Split marketing into two disciplines, Jain suggests. The first is acquisition — the domain of Google and Meta, whose business is bringing new customers to a website or app. The second, where Netcore operates, is what happens after that first transaction: engagement, retention, lifetime value.

Most companies over-invest in the first and under-invest in the second. A customer converts, goes quiet after a few months, and the same brand returns to the same ad platforms and pays again to win them back — even though it already holds their purchase history, preferences and trust.

"Why should I keep paying rent to external platforms for customers who already know my brand?" is how Jain frames the problem. It is a fair question, and one most consumer businesses have simply never asked, largely because the tools to act on customer data at scale didn't exist. CRM and retention marketing have long been treated as a cost centre — a support function bolted onto the "real" work of acquisition — rather than as the more profitable lever it can be.

That gap is precisely what Jain believes AI finally closes.

Four agents, one operating layer

For years, marketers wanted true one-to-one communication — the right message, to the right customer, through the right channel, at the right time — but lacked the means to deliver it at any scale beyond a handful of hand-built segments. Netcore's answer is to break the operational work of retention marketing into four categories of AI agents that work in sequence rather than isolation.

An insights agent replaces the ritual of opening multiple dashboards each morning, surfacing anomalies and flagging what needs attention. An audience agent does the segmentation a human team never could at scale — not the ten or fifteen buckets a marketer might realistically build by hand, but hundreds of behaviourally-defined micro-segments. A content agent then generates messaging for each of those segments while staying within a brand's tone and policy guardrails. Finally, a decisioning agent determines who receives which message, on which channel, and when.

None of this is presented as a replacement for marketers — a claim that would be easy to make and hard to defend. The pitch instead is closer to what has already happened in software engineering: AI coding assistants didn't eliminate developers, they made some of them dramatically more productive. Jain's shorthand for the equivalent shift in marketing is the "10x marketer" — someone freed from building reports and shuffling data between systems to spend time on positioning, pricing and customer psychology instead.

Why the model itself isn't the moat

There's an obvious objection here: every marketing platform now has access to the same handful of large language models. Gemini, Claude and ChatGPT are becoming commodities, and Jain doesn't dispute it. His argument is that differentiation was never going to sit in the model layer — it sits in the proprietary data and judgment layered on top. Netcore's claim to that layer comes from nearly three decades of executed customer interactions: which subject lines perform better, which channels work where, how journeys evolve across industries. Combine that with a customer's own first-party data, and — in Jain's telling — you get something more useful than a generic AI assistant bolted onto a CRM. Whether that advantage survives as foundation models improve is a question the industry hasn't settled, and one Netcore has an obvious interest in answering in its own favour.

Repricing the relationship

If retention economics is the argument, pricing is where it gets tested. Software has historically been sold against inputs — seats, messages, API calls — because it functioned as a productivity tool and inputs were what could be measured. Jain's contention is that customers are increasingly indifferent to inputs and focused on outcomes: did conversions rise, did retention improve, did revenue actually move. Netcore's internal term for pricing against that is "alpha pricing," borrowed from the investing distinction between beta (market return) and alpha (the return generated above it). Applied to a customer relationship, alpha comes from two levers: getting customers to repeat-purchase sooner, and cutting the "hidden tax" of paying to reacquire people who were never really lost.

This is a materially different vendor relationship than a software licence, and it shifts risk on to the vendor — outcome-based pricing only works for a company confident its product moves the metric it's being paid against. That's a wager a bootstrapped company, free of quarterly pressure, can afford to make patiently. A venture-backed rival under pressure to show growth now may find it harder.

Jain applies a simpler internal framework to the same idea: best, rest, test and next. Prospective customers ("Next") become first-time buyers still being evaluated ("Test"), and the objective is to move them into "Best" — repeat, engaged customers who generate most of a company's long-term value — while simultaneously recovering the "Rest," those who have gone inactive, before the business has to spend heavily to win them back from scratch. Even in categories with long purchase cycles — he cites automobiles, where a customer may buy once every five years — the logic holds: the post-purchase period is simply the pre-purchase period for the next transaction, or for a referral that substitutes for one.

The through-line

Strip away the frameworks and acronyms, and Netcore's bet is a fairly narrow one: that the biggest opportunity in AI-driven marketing isn't speed, it's stopping brands from paying twice for something they already own — the relationship with an existing customer. Jain's preferred description of the marketer's job title in this future is blunt: not growth hacker, not brand custodian, but chief profitability officer. Marketing remains one of the largest variable costs in most consumer businesses, and every rupee saved on reacquisition drops straight to the bottom line.

It's a thesis that will be tested less by what Jain says about it than by what happens when Netcore, or a rival, has to prove the outcome-based pricing actually holds up under a customer's own P&L — and by whether an IPO, whenever it eventually comes, still looks like the right structure for a bet that was built, deliberately, to not answer to a quarterly clock.