At an age when most engineers are still figuring out their first career moves, Ajaz Khan is already questioning, and rebuilding the foundations of how materials are discovered. The 23-year-old chemical engineer isn’t chasing incremental improvements or flashy demos. Instead, he is taking on one of the most deeply entrenched problems in science: a research and discovery process that is slow, expensive, and astonishingly inefficient.

In a recent conversation with Indian Startup Times, Ajaz Khan, the founder of deep-tech startup Novyte, spoke at length about why traditional material R&D no longer works at the pace modern industries demand, how artificial intelligence can compress years of discovery into months, and why India must invest in its own scientific capabilities instead of depending on fragile global supply chains.

At the core of his thinking is a simple but uncomfortable truth, despite decades of progress, material discovery today still relies heavily on trial and error.

From Chemical Engineering to Rethinking Discovery Itself

Ajaz’s journey began at the Institute of Chemical Technology (ICT), Mumbai, one of India’s most demanding science institutions. While the coursework was rigorous, it wasn’t the difficulty that bothered him, it was the inefficiency.

He didn’t lose interest in chemistry; he lost patience with a system where researchers spend years running experiments, burning capital, and still end up with a painfully low success rate. That frustration became more pronounced during his master’s thesis, where he began exploring whether AI could move beyond being a supporting tool and instead become the driver of discovery.

By combining machine learning with quantum chemistry and material science, Ajaz started building models that could systematically narrow down viable compounds before they ever reached the lab. The results were striking. Novyte’s early AI models identified 39 entirely new materials, 24 of which were validated as stable, delivering a 68% hit rate compared to an industry average of roughly 5%.

That moment marked a shift. What started as academic research no longer made sense as just a paper or a project. It had the potential to become infrastructure.

Solving Problems Industries Can’t Afford to Ignore

From the start, Novyte was designed with real-world constraints in mind. Ajaz is acutely aware that material shortages don’t remain confined to laboratories, they disrupt entire industries.

He points to antimony trioxide, a critical component used in flame retardants, whose prices surged following global supply chain disruptions. When shortages like these hit, traditional R&D timelines collapse under their own weight.

Manufacturers don’t have the luxury of waiting years for alternatives. They need viable materials fast. This urgency shaped Novyte’s core philosophy, what Ajaz calls anticipatory innovation. By removing human bias and drastically reducing the search space, Novyte’s AI-led approach aims to shorten R&D cycles from years to months, while significantly lowering cost and risk.

Early Belief, Early Capital

Building a deep-tech startup in India comes with its own challenges, particularly when long R&D cycles collide with the demand for quick returns. Convincing MSMEs to invest in research was one of the toughest early hurdles.

What helped was proof. Ajaz’s master’s research became Novyte’s first validation layer, opening doors to pilot projects, enterprise discussions, and early adoption. Recognition followed, including a Student Startup of the Year award.

Investor confidence soon followed as well. Novyte raised $500K in Pre seed funding from Theia Ventures, giving the company the runway to strengthen its models, expand enterprise pilots, and move closer to production-ready deployments. For Ajaz, this kind of patient capital is essential in deep tech, where value is built through data, models, and scientific rigor—not overnight growth.

NovyteQ and the Road Ahead

Novyte is now gearing up to launch NovyteQ, its AI-powered R&D platform for specialty chemicals and advanced materials. Designed as a domain-agnostic, agentic platform, NovyteQ allows enterprises to accelerate discovery and optimization without dismantling existing R&D systems.

The platform continuously learns from experiments while pulling context from academic literature, offering validated synthesis pathways, live citations, and next-experiment recommendations. It optimizes multiple competing properties simultaneously and predicts failures before they consume time and resources. Built-in scale-up analysis bridges the gap between lab formulations and production volumes, providing physics-validated thermal and economic projections to de-risk commercialization.

Alongside this, the team is developing Novyte ψ (Psi), a quantum chemistry and inverse design engine that will sit on top of NovyteQ, enabling deeper physics-based screening for next-generation materials. With early enterprise pilots already underway, the full platform is expected to go live within the next two months. Ajaz also shared plans to explore a larger funding round around May–June, once the end-to-end discovery pipeline is fully operational.

Building India’s Material Backbone

For Ajaz, Novyte’s ambition extends far beyond commercial success. He envisions the company as a foundational layer for R&D teams—one that frees scientists from broken processes and allows them to focus on actual innovation.

There is also a broader national lens to his work. Reducing India’s dependence on imported critical materials isn’t just an economic goal; it’s a strategic necessity. Without indigenous discovery capabilities, supply chains remain vulnerable and innovation remains constrained.

Advice for the Next Wave of Deep-Tech Founders

Ajaz’s advice to young founders is grounded and pragmatic. Rejection, he believes, is unavoidable—and essential. Talking to people early, learning how to clearly articulate complex ideas, and understanding the realities of venture capital are just as important as technical brilliance.

He also cautions founders to be mindful of the mismatch that often exists between deep-tech timelines and traditional VC expectations—a gap that can only be bridged with clarity, patience, and strong proof points.

Closing Thought

As Novyte prepares to launch NovyteQ, Ajaz Khan’s journey offers a compelling glimpse into a different kind of Indian startup story—one rooted not in rapid consumer adoption, but in rebuilding the scientific systems that power entire industries.

In an ecosystem often dominated by apps and marketplaces, Novyte stands out by going deeper—into physics, chemistry, and computation. If Ajaz’s vision plays out, the next wave of Indian innovation may not just serve users, but quietly reshape the materials that make modern life possible, one discovery at a time.

Interview Conducted By : Arushi Agarwal