Swish Club has pivoted from device leasing to SwishX, an agentic AI platform focused on pharma’s sales, distribution, contracts and tendering workflows.
SwishX’s pivot underscores the growing opportunity in vertical AI, where specialised platforms build AI around industry-specific workflows and proprietary domain knowledge rather than offering generic enterprise copilots.
SwishX has launched four products for pharma workflows, crossed $1 Mn ARR and is targeting $5 Mn in contracted ARR and 100+ enterprise customers by FY27.
When Swish Club raised $4.5 Mn in early 2024, it looked like yet another startup trying to solve enterprise device procurement and leasing. The company rented laptops, leased smartphones and helped businesses manage IT assets. It was a market with growing demand but limited room for differentiation.
Less than two years later, it was clear to the founders that the business needed a revamp. They shut down the original offering, rebranded the startup as SwishX and started building an agentic AI platform for pharmaceutical and medtech companies.
In its new avatar, SwishX wants to become the software layer powering commercial operations across the pharmaceutical industry, starting with sales, distribution, hospital contracts and government tenders.
At a time when most AI startups are building horizontal copilots or productivity tools, SwishX is betting on a different thesis: the biggest opportunity in enterprise AI lies in deeply specialised, industry-specific workflows.
That conviction is already translating into business. SwishX claims to have already crossed $1 Mn in annual recurring revenue (ARR), works with some of India’s largest pharmaceutical companies and expects to reach $5 Mn in contracted ARR and over 100 enterprise customers by the end of FY27.
For founder and CEO Dushyant Sapre, however, the story did not begin with AI.
An IIT Delhi graduate, Sapre spent nearly two decades overseas building enterprise businesses. From being one of the earliest employees of Google Cloud in India to scaling a Singapore-based enterprise startup to more than $500 Mn in revenue, he has held multiple leadership roles across Asia-Pacific markets. He eventually returned to India in 2023.
Back home, he, along with Suraj Kumar and Jai Anand, cofounded Swish Club in 2024 around a relatively straightforward thesis: enterprises needed a better way to procure, lease and manage devices. But as the business scaled, the founders encountered a structural limitation.
Device leasing is a capital-intensive business, as growth requires constant access to financing, inventory management and debt. More importantly, the founders realised that differentiation in the category often came down to pricing and interest rates rather than technology.
“We wanted to build something that could serve 10,000 customers globally… The device business would always remain largely India-focused and heavily dependent on capital,” Sapre said.
At the same time, an unexpected signal was emerging from the startup’s customer base. A significant portion of Swish Club’s customers came from the pharmaceutical and life sciences sector. These companies increasingly began asking whether the startup could help automate workflows beyond device management. This demand coincided with the rise of generative AI.
Rather than competing head-on with OpenAI, Anthropic or other foundational model providers, the founders concluded that the next wave of enterprise value creation would come from vertical AI.
The pharmaceutical industry’s scale in India is enormous. Estimated to be worth over $65 Bn, the homegrown pharma sector supplies roughly one-fifth of the world’s medicines and exports products to more than 100 countries.
Yet despite its size, many pharmaceutical companies continue to rely on spreadsheets, fragmented systems and legacy software for critical commercial operations, according to Sapre, who noted that this is exactly where the opportunity to build lay for SwishX.
The startup also identified another market gap. Unlike developed markets, emerging markets such as India, Brazil, Mexico, Africa and parts of Eastern Europe remain highly fragmented, creating complex operational challenges for drugmakers.
To solve this, SwishX has launched four products:
Collectively, the four products address what the startup views as the most critical bottleneck in pharma: commercial execution. As a result, the startup is already seeing healthy early traction.
According to Sapre, customers using Tender IQ have seen bid preparation time fall by roughly 60%, while its fastest growing product, Marketing IQ, powers campaigns that reach more than 1.5 Lakh healthcare professionals every week in India alone.
Meanwhile, the startup claims that Contract IQ customers have seen hospital contract renewal rates improve by nearly 8X, while Channel IQ users have seen primary sales increase by roughly 20%.
Under the hood, SwishX leverages Anthropic’s large language models (LLMs) for its AI stack, alongside specialised offerings from providers such as Cartesia for voice capabilities. However, Sapre argues that foundation models themselves are not the moat.
Instead, he argues that the startup’s advantage lies in understanding pharmaceutical workflows, which often require multiple approvals across manufacturing, finance, regulatory and quality teams. Building those workflows into enterprise software is significantly more complex than simply wrapping an LLM inside a user interface.
As Sapre puts it, SwishX hopes to avoid the trap of being merely a “wrapper” by embedding itself deeply within customer operations.
Over the past year, SwishX claims to have trained its systems on more than 70,000 pharmaceutical tenders in India alone, alongside similar datasets from the US and Brazil. Unlike horizontal tender automation platforms that ingest procurement data across dozens of industries, SwishX’s datasets are focused entirely on healthcare and life sciences.
But the larger differentiator, according to Sapre, comes after deployment. Each customer operates within a sandboxed workspace where AI agents continuously learn organisational preferences, approval processes and compliance requirements. Those learnings remain customer-specific and are never shared across accounts.
“The performance of the platform on day one versus day ninety is very different,” Sapre said.
Today, the company estimates that roughly 90% of its intelligence is derived from public data and 10% from proprietary learning layers, but expects that ratio to reverse over time as customer-specific intelligence compounds.
Enterprise AI adoption in pharmaceuticals comes with an additional challenge: trust. Unlike consumer AI products, mistakes in healthcare and life sciences can have significant regulatory and commercial consequences.
To minimise hallucinations and accuracy issues, SwishX restricts many of its agents to verified data sources such as regulatory databases, government repositories and approved industry documents rather than allowing unrestricted internet searches.
The startup claims its systems currently operate with an error rate of less than 2%, down significantly from early versions that relied more heavily on general-purpose LLMs. Human oversight also remains embedded throughout critical workflows
In addition, SwishX also claims to have invested heavily in external regulatory expertise across multiple geographies and is building compliance frameworks covering pharma marketing and communications across key international markets.
Nevertheless, challenges remain as the startup expands globally and navigates complex regulatory environments. For starters, pharmaceutical companies remain apprehensive about whether a young startup can be trusted to deploy it responsibly.
Another key issue for SwishX is the growing tide of competition from two directions. On one side are enterprise software giants such as Salesforce, Oracle, IQVIA and Veeva Systems. However, SwishX claims that it positions itself as an orchestration layer that sits on top of existing systems.
“We don’t compete with Salesforce by building another CRM. The goal is to become an intelligence layer that works alongside existing software,” Sapre added.
With this, SwishX is betting that specialised intelligence and accumulated workflow knowledge will ultimately prove more valuable than generic AI capabilities.
The second category includes AI-native startups focused on areas such as drug discovery, manufacturing compliance and R&D automation. Here, SwishX believes its India roots provide a unique advantage.
Many of the world’s largest pharmaceutical companies now operate substantial global capability centres in India. As a result, SwishX works closely with teams responsible for sales, marketing and operational execution across international markets.
This proximity, Sapre argues, gives the startup a deep understanding of generic drug markets, which is a segment that often receives less attention than patented drug development but serves billions of patients globally.
The company currently operates with a team of roughly 35 people, more than 60% of whom work across engineering, product and design. Its sales team, by contrast, remains deliberately lean, as much of its customer acquisition has been driven through referrals and existing industry relationships
While the founder did not disclose client names, he said that the startup is already working with one of India’s top three pharmaceutical companies and several mid-sized drugmakers.
It is also betting on these relationships to fuel its global expansion ambitions. Instead of following the conventional India-to-US SaaS playbook, SwishX is targeting emerging markets. Through existing customer relationships, the startup has already begun operations in Brazil, Mexico and Colombia and plans to expand further across Latin America, Southeast Asia, Africa and Eastern Europe.
The Middle East was initially part of that roadmap, although the company has temporarily paused expansion efforts amid ongoing geopolitical uncertainty and supply-chain disruptions affecting customers.
Looking ahead, the founders envision something much larger than the four products they offer today. Their long-term goal is to build a full-stack vertical AI platform spanning the entire pharma lifecycle, including generic drug development, regulatory intelligence and market-entry planning. Much of the underlying data infrastructure required for those ambitions, Sapre argues, is already being assembled through its current products.
Its long-term goal is not simply to become an AI company, a software company or a data company.
Instead, Sapre describes SwishX as an outcomes company – one that helps pharmaceutical executives answer questions ranging from which products to launch and where to expand, to how commercial teams should allocate resources across markets.
In five years, the founder says success will not be measured by revenue alone but by the ability to serve thousands of pharmaceutical workspaces globally.
Source: Inc42 - Startups
