How Rocket Is Tackling The Depth Crisis In Vibe-Coded Software Universe

Speak your mind in the language that does the job best and let AI write the code for you. Rest assured that the vibe is coded right so that the tech can read your mind with complete clarity. 

The rise of vibe coding in less than a year comes as a relief for software developers from services fatigue that perpetuates from the long, meandering journey from ideation to implementation. Use of GenAI completes the task in minutes. India’s surging digital economy that’s likely to make up a fifth of the GDP this year, quickly responded to the latest vibe in the world of AI innovation.  

Founders like Vishal Virani, Rahul Shingala, and Deepak Dhanak began using AI coding tools to prototype their products, build internal tools, and cut engineering costs. Smaller teams are shipping faster, development cycles have compressed, and the barrier to building software has fallen sharply.

But as adoption moves from experimentation to everyday use, a structural limitation becomes hard to ignore. Much of what is being built looks like software but does not behave like it. Interfaces exist, but workflows break down. Applications demo well, but stagger when deployed.

It is this gap between speed and substance that vibe coding startup Rocket believes will define the next phase of the category. Virani, Shingala and Dhanak set up Rocket in 2021 as an AI-powered app-building platform that lets users create dashboards, apps, websites, and internal tools in a matter of minutes by providing natural language instructions. 

“Think it. Type it. Launch it,”  Rocket promotes itself on its website, as AI in India is set to bloom into a $17 Bn opportunity by 2027.

Thought Sparks When Speed Turns To Shudders 

The idea of Rocket didn’t take off from the AI euphoria – it emerged from years spent operating technology-led services and product businesses where the distance between idea and outcome was consistently the biggest constraint.

According to COO Dhanak, traditional software development is fragmented by design. “An idea moves through product specification, design, engineering, quality assurance, deployment, and maintenance. Each stage introduces new dependencies, specialised roles, and delays,” he said.

In many cases across the startup ecosystem, companies have raised seed or even Series A rounds primarily to reach a usable prototype. Generative AI appeared to offer a reset. But in Rocket’s view, most platforms addressed only the most visible bottleneck.

“The biggest roadblock as we have seen is that there is too much time, hassle and friction between an idea and the outcome.” Dhanak told Inc42. “We didn’t want to stop at prototyping or MVP creation. We wanted to ultimately build production-grade apps.”

Dhanak’s view is supported by the fact that code generation and software creation are not the same thing.

Read The Vibes Before Blasting Off 

Rocket takes the most deliberate product decision only after realising the hurdles and the vibes given by the user. 

When a user enters a prompt, Rocket does not immediately produce an interface. Instead, it tries to gain clarity of the intent. Is the user building a mobile app or a web app or a CRM or an internal tool or a website – each of these possible requirements demands different architectural choices and user flows.

If the prompt is ambiguous, the system asks follow-up questions. It seeks clarity on the persona, scope, and the desired behaviour before moving forward.

Rocket suggests a technology stack only after these questions are answered. Unlike many vibe coding tools, this stack is not fixed. Users can accept Rocket’s defaults or choose alternatives based on their own preferences. The platform then outlines a realistic list of features, pages, and workflows required for the application to function. 

Once approved, Rocket moves through copywriting, design system creation, and code generation, before compiling everything into a functional preview. The output is typically a multi-page application with navigation, placeholder data, and usable flows. 

“Rocket doesn’t rush to code. Rocket is not designed to wow you with speed – it is designed to serve your use case with thoroughness, comprehensiveness, and use-case-based solutions,” said Dhanak.

It’s Design That Makes The Distinction 

Design philosophy places Rocket in a quiet opposition to much of the vibe-coding narratives.

Dhanak said many platforms claim to generate full applications. In practice, they often design single-page dashboards or landing pages, even when users ask for complex systems. A request for a CRM may result in a static interface, rather than a system with onboarding, analytics, permissions, and workflows.

The software is defined less by visual polish and more by operational depth. A functional application must simulate real journeys and interactions, and not just display components.

This distinction explains Rocket’s internal positioning. The company describes itself as a “functional factory”, and not a feature factory. Coding, in this framing, is only one part of a broader solutioning process. 

While its competitors like Lovable and Bolt stress on rapid prompt-to-demo outputs and surface-level interfaces, Rocket claims to differentiate itself by building multi-page, functional applications with deeper workflows. “It is not a three-minute wonder. It is a comprehensive software development that is fully functional to serve the use cases you talk about,” Dhanak said. 

The implication is clear: Tools optimised for demos serve curiosity, while those optimised for functionality serve deployment.

Business Model Built Around Deployment 

A focus on deployment influences Rocket’s product decisions downstream. The platform allows applications to integrate with the existing backends through APIs, reducing the need for teams to rebuild the infrastructure. Users can deploy the applications through managed hosting or publish them on custom domains.

The code can be downloaded, exported, or synced with GitHub repositories, allowing the user to take ownership post-generation. In some cases, the user can also modify the code directly on the platform.

Data localisation and security controls are positioned as baseline capabilities rather than premium features. According to the founder, this reflects the reality that many customers operate in regulated or enterprise environments where governance is non-negotiable.

Rocket’s monetisation model follows a now-familiar AI infrastructure pattern where per-seat pricing is combined with usage-based consumption. Credits, tokens, or commands, Dhanak noted, are variations of the same underlying mechanism.

The startup offers a free starter plan with 1 Mn tokens and sells the subscription packages  in tiers like $25, $50, and $100 per month, with higher token limits, private projects, code downloads, and data-training opt-outs.

The distinctive approach of the Surat-based AI-powered app building platform helped it garner $15 Mn (around INR 132 Cr) in a seed round from Salesforce Ventures and Accel last year. The fund is being channelled primarily into product development, rather than aggressive go-to-market expansion. 

While the US is reportedly Rocket’s largest market, making up 26% of its revenue, with Europe contributing 15-20% and India 10%, the startup plans to maintain operational bases in India and the US and expand its American foothold with headquarters in Palo Alto.

The Hype, The Noise, And A Reset In Place

In the interaction with Inc42, Dhanak seemed sceptical of incremental AI adoption. “Simply layering generative features onto existing SaaS products may improve optics, but weakens foundations,” he said. 

In his view, AI-native software requires a rebuild, not cosmetic rewiring. Founders must unlearn assumptions formed during the SaaS era and avoid carrying architectural baggage forward. “The way SaaS was built, sold, and monetised is not how AI-native products will work,” he said. “Incrementalism will kill more startups than competition.”

This warning is particularly relevant in India’s startup ecosystem, where many companies are racing to retrofit AI into legacy products while newer entrants attempt to start from scratch.

On the trajectory of AI adoption, the founder was cautious, rather than predictive. He believes the ecosystem is still in a hype phase, with significant noise around capabilities and outcomes. As expectations harden, implementations become shallow, leaving marginal players struggling.

He expects the pace of change to accelerate further. What took decades in the past has unfolded in a few years. The next phase, according to the Rocket founder, may compress even more progress into shorter cycles.

What This Signals For Indian Startups

Rocket’s approach reflects a broader shift in India’s startup economy. AI is compressing the execution timelines and lowering the cost of building software in an ecosystem that’s home to more than 1,20,000 AI professionals and over 185 AI/ML global capacity centres. Smaller teams can now attempt problems that would earlier require large organisations. But, this efficiency also increases the importance of judgment.

When AI handles execution, the role of humans remains confined to the intent. What should be built? What does ‘working’ actually mean? Where does accountability lie when automation fails? Players like Rocket bridge the gulf, reading the human vibes and coding the solution. The tools for vibe coding are optimised purely for speed risk-scaling confusion. 

Rocket trusts its understanding that as the ecosystem matures, founders and enterprises will care less about how quickly something appears on screen and more about whether that can be deployed, trusted, and extended.

Along the way, the vibe coding platforms like Rocket are likely to face higher learning curves for non-technical users, slower perceived output versus demo-first tools, rising and unpredictable AI infrastructure costs, blurred accountability for security and architectural flaws, longer enterprise sales cycles, and the likelihood that faster competitors will gradually add depth, narrowing differentiation across the category.

Rocket bets on its deployment edge to be a disruptor in the field of AI-backed software development.

Edited by: Kumar Chatterjee