At The Deep End: For India’s deep-tech industry this is a moment of reckoning

India’s deep-tech ecosystem is maturing fast. As artificial intelligence (AI), robotics, industrial internet of things (IIoT) and data-driven systems move out of labs into a real-world business environment, deep-tech companies have little option but to make a business case for their solutions. Ambition is being tested by execution.

Funding, once the centre of action, no longer defines success. What separates scalable ventures from stalled experiments is clarity about customer problem, product–market fit, and ability to convert technical proof into commercial adoption. The shift is visible in lower appetite for open-ended pilots, closer scrutiny of return on investment (RoI), tighter capital deployment, rising regulatory and data compliance requirements, and longer and more demanding enterprise procurement cycles.

Sunil K. Goyal, Managing Director, YourNest Venture Capital, an early-stage venture fund, gives an example. “Datoms, an IIoT player, initially had a vision of connecting every possible machine to the Cloud. Instead of going broad from day one, the founders chose to focus on a single category, diesel generator sets,” he says. Once customers saw consistent results, it became easier for the company to position itself as a full-scale platform capable of connecting other machines such as chillers, warehouses, even medical devices.

According to tech research firm Tracxn, India’s deep-tech sector secured $1.6 billion funding in 2025, a significant rise from $1.2 billion in 2024 and $1.1 billion in 2023. Deal volume fell to 274, compared to 393 in 2024 and 350 in 2023 India had more than 3,600 deep-tech registered startups as of October 2024.

The segment is buzzing but the ground beneath is changing rapidly.

Cracking Product–Market Fit

In deep-tech start-ups, especially in areas such as manufacturing and AI-led systems, operational challenges such as long development cycles, complex integrations, regulatory compliance, talent shortages, and aligning products with real customer needs are often outweighing funding issues.

“If founders can clearly understand the customer problem and build a strong product road map, capital usually follows. Before we invest, we look closely at whether the founding team has clarity on who the customer is and how the product will evolve,” says Goyal.

Post-investment, the biggest difference between start-ups that break out and those that struggle is ability to find the product–market fit. This means knowing exactly which customer segment, geography, industry vertical and price point will lead to repeat sales and long-term customer retention.

Many start-ups get early pilots, but unless they can convert those into sustained, repeat businesses, growth is difficult.

Subtl.ai, a Hyderabad-based GenAI start-up, for instance, built enterprise knowledge automation tools but failed to achieve product–market fit; its solution didn’t deliver compelling value over existing workflows, leading to low adoption.

“The real risk is when founders fail to crack either product–market fit or go-to-market—at that point, the outcome is often an acquisition or a slow shutdown rather than meaningful scale,” says Goyal. Common go-to-market mistakes include targeting the wrong customer segment, overpromising capabilities, neglecting enterprise adoption hurdles, underestimating sales cycles, and failing to align pilots with measurable business impact.

In fact, being resource-constrained is not necessarily a bad thing. It forces founders to think about how they should allocate capital and resources.

“Once you prove real product-market fit (with paying customers), the path becomes much clearer. At this point, it is much easier to make allocation and prioritisation decisions based on likely revenue projections rather than some unproven valuation metric,” says Bruce Keith, co-founder and CEO of InvestorAi, a personal AI investment analyst.

According to Venture Catalysts, start-ups with at least 24 months of runway see nearly 2x pilot-to-revenue conversion. But making pilots work is not an easy task either.

Challenges to Pilots

Pilots often prove technical feasibility but not commercial readiness. Enterprises care about integration, reliability, RoI, and long-term support, and not just whether the technology works. “Start-ups that succeed here design pilots with scale in mind from day one. Importantly, they treat pilots as sales processes, not experiments, using them to lock in budgets, timelines, and expansion pathways upfront,” says Ashish Taneja, Founder & CEO, growX Ventures.

Another big challenge in AI pilots is enabling real-world testing without exposing sensitive data or intellectual property. The AI data privacy challenge is acute in India’s healthcare (patient data), fintech (compliance), and manufacturing (IoT risks). Open experimentation creates risk, while overly restricted pilots produce meaningless results.

The most effective solution is secure, sandboxed environments using masked or synthetic datasets that replicate statistical behaviour. Funding can come from government AI missions, venture capital firms, big tech investments, and bootstrapped start-ups.

“Time-bound pilots tied to measurable outcomes, and not open-ended experimentation, will allow firms to move quickly while remaining compliant. This approach turns AI testing into an enterprise-ready process rather than a research exercise,” says Gaurav Gupta, Managing Partner at Decimal Point Analytics. Typically, such time-bound pilots run for 8–12 weeks, with clearly defined performance indicators around accuracy, cost savings, or productivity. This time frame balances speed with compliance.

“Pilots that succeed usually show clear economic value within 90 days of inception,” says Apoorva Ranjan Sharma, Co-Founder & MD of Venture Catalysts. According to him, pilots that improve operations by 15% or more—such as reducing costs, cutting downtime, or increasing efficiency—often lead customers to sign paid contracts, moving from testing to officially buying or using the solution. Over 60% of such pilots convert, while the weaker ones rarely scale up.

Rather than chasing headline pilots, Qure.AI, a health tech start-up, employs AI for medical imaging diagnostics, prioritising practical, repeatable outcomes that matter to hospitals, such as faster report turnaround times, improved diagnostic consistency, and smoother workflows.

Sustainable Teams

Another big challange is hiring. According to a report by Teamlease Digital, for every 10 Generative AI job openings in India, only one qualified engineer is available. The report says the AI skill gap could widen to 53% by 2026.

“Only a tiny fraction (of India’s engineers) is trained in areas like advanced materials, chip design, and optics, and many of the best migrate to the US or Europe where funding, peers, and research timelines are more attractive,” says Sunil Gupta, Co-founder, CEO & MD, Yotta Data Services.

Deep-tech start-ups struggle the most with retaining talent like senior engineers after product validation. “Across our portfolio, companies offering employee stock ownership and clear technical road maps had about 30% lower attrition than those relying only on cash. Teams with stable and great core engineers tend to release products nearly twice as fast,” says Sharma.

According to Piyush Goel, the CEO and Founder of Beyond Key, the company has committed dedicated resources to advanced certifications and employee innovation labs to retain the best minds. Flexibility at work, professional instructor-led programmes, and interaction with clients from across the world add to employee satisfaction.

Securing IP for Sovereign Data

Another core challenge is enabling collaboration across teams, partners, and institutions without compromising data sovereignty, intellectual property (IP) protection or regulatory compliance. This risk is amplified where datasets and models are highly sensitive and often subject to strict governance requirements.

The Digital Personal Data Protection (DPDP) framework requires organisations to use personal data only for specific, approved purposes and remain fully accountable for how that data is handled. This makes traditional approaches like centralised data sharing or federated access less effective. While these models make collaboration easier, they struggle to properly manage user consent, enforce purpose limits, control cross-border data access, maintain clear audit trails, and protect sensitive personal data under the DPDP rules.

“Founders mitigate this by patent-first engineering and defensive publications before pilots. They file Indian patents through TIFAC-KIRAN or incubator support, followed by PCT (patent cooperation treaty) if needed. For data, companies use anonymisation, DPDP-aligned consent layers, and on-device processing to reduce compliance overhead,” says Bhaskar Majumdar, Managing Partner, Unicorn India Ventures.

To further grow the product idea via experimentation, non-sensitive synthetic datasets are isolated, stored using encryption, and accessed through gated controls.

Capital Allocation

The most resilient founders plan for sustainability early. Capital is allocated with a clear view of what reduces existential risk: technical, regulatory, or commercial, at each stage. They avoid premature scaling up and are comfortable saying no to opportunities that don’t strengthen their long-term moat.

“We also see a strong emphasis on optionality: building platforms that can serve multiple use cases, protecting IP that compounds over time, and maintaining financial discipline to survive longer cycles. Deep-tech is more about endurance than speed. Founders who understand that, from day one, tend to win,” says Taneja.

The strongest companies separate capital for research, customer acquisition, and regulatory work instead of spending opportunistically. “In our data, start-ups that kept at least 40% capital for product and IP development had longer survival and higher acquisition interest. This discipline helped them outlast peers when funding cycles tightened,” says Sharma.

Gox.ai, an AI-powered automated business reporting firm, maintained strict discipline. The company was initially funded through the founder’s personal savings accumulated from years in corporate roles, and costs were kept minimal. It took 12 months for them to get their first customer. Early growth at Tagbin, an AI-powered immersive museum experiences company, was almost entirely bootstrapped, driven by execution rather than funding rounds.

India’s deep-tech sector is moving from ideas to real impact. Success now comes from careful execution, not just funding or hype. Founders who plan for scale, protect data and IP, and use capital wisely are the ones building lasting companies. Pilots that show real value, teams that stay strong, and solutions that work in the real world turn innovation into success. In deep-tech, steady, smart progress beats speed every time.