Last updated : February 13, 2026 9:15 am
Chemical and materials R&D digitization in India is representing a US $2 billion near-term opportunity
In an exclusive interaction with Pravin Prashant, Executive Editor, Indian Chemical News, Satvik Kalra, Co-founder & COO and Trapti Kalra, Co-founder & CTO, SarthhakAI shares their insights on company's vision and mission, India and global opportunity, product innovation, customers, security aspects of software, patents and trademarks support, and monetization plan. Excerpts of the interview:
Vision and mission of SarthhakAI? How is your company helping scientific community increase speed of innovation?
Our vision is to become the foundational AI intelligence layer for scientific R&D and manufacturing, where human expertise, experimental data, and AI systems continuously work together to accelerate discovery, optimization, and industrialization of new materials, formulations, and processes. Our mission is to embed AI directly inside the workflows of scientists and engineers so that experimentation, formulation, analysis, optimization, and scale-up are continuously augmented by domain-aware AI built and guided by scientists and domain experts.
SarthhakAI’s platforms increase the speed of innovation by shifting R&D from a document-centric model to an AI-native operating model. Scientific context- projects, experiments, formulations, trials, datasets, and documents is captured in a contextual way that AI can immediately reason over. This allows AI agents and models to assist scientists during the design, execution, and interpretation of experiments. The result is fewer blind experiments, faster learning from historical data, shorter development cycles, and more confident technical decision-making, enabling brand-new discovery at a much faster pace.
India and global market opportunity of both scientific, Episteme Labs, and Continuum Labs? What percentage of the market is SarthhakAI targeting?
SarthhakAI operates at the intersection of scientific software, industrial AI platforms, and digital R&D transformation. The combined global opportunity across ELN/LIMS, formulation software, industrial AI, and scientific data platforms is estimated to be US $8–10 billion (Rs. 67,000 crore - Rs. 83,500 crore), with strong growth in specialty chemicals, polymers, materials, life sciences, and advanced manufacturing.
In India alone, chemical and materials R&D digitization is still at an early-stage but is rapidly expanding, representing a US $2 billion (Rs. 16,700 crore) near-term opportunity, driven by specialty chemicals, pharmaceuticals, polymers, and materials innovation. SarthhakAI is targeting a significant share of this global opportunity over the next 7-10 years, focused on high-value enterprise deployments.
Type of AI and deep-tech technologies SarthhakAI is utilizing for research divisions of chemical companies?
At SarthhakAI, we have implemented a suite of domain-specific auto-AI capabilities that operate directly inside scientific workflows, including agentic workflows integrated within a chemistry- and material-science-aware platform. This makes world-class AI natively accessible to chemical and materials R&D teams without any IT assistance. The platform delivers modern AI-based capabilities at a rapid pace to its global user base.
These capabilities are powered by a layered deep-tech stack comprising domain-adapted Large Language Models (LLMs) and Small Language Models (SLMs), Graphical Retrieval-Augmented Generation (G-RAG) over enterprise knowledge bases, multi-agent orchestration frameworks, machine learning models, optimization algorithms, and automated model-selection pipelines.
Together, this stack enables our platform bodh scientific to function as an AI-native R&D intelligence platform, where AI systems actively participate in formulation, experimentation, analysis, and decision-making.
What customization does SarthhakAI do for formulations, polymers, specialty chemicals, and manufacturing?
SarthhakAI’s bodh scientific platform is designed to be customizable across multiple scientific and industrial domains, including chemicals, polymers, packaging materials, and formulation-driven industries. Customization is achieved by embedding domain-specific data structures, workflows, and AI behaviors into the platform so that each industry experiences intelligence aligned with how its R&D and manufacturing teams actually operate.
One major focus area is formulation-centric R&D. The platform models formulations as structured scientific entities composed of ingredients, composition logic, processing conditions, and measured properties. Realistic composition rules such as percentage-based ranges, grouped ingredients, and alternative material sets are natively supported. This enables AI systems to reason chemically rather than treating formulations as generic tables.
Alongside formulation design, SarthhakAI places strong emphasis on process optimization and manufacturing intelligence. This allows models to learn relationships between composition, process, and properties, which is essential for scale-up and robust manufacturing.
AI agents and models are tuned to reason in terms of industrial trade-offs—performance versus cost, stability, sustainability, and manufacturability—ensuring recommendations are practical and actionable.
How do these solutions help industrial R&D and new product innovations (and also research institutes)?
SarthhakAI’s platforms are designed primarily for R&D teams and organizations whose mandate is to create new chemicals, new materials, and differentiated products faster. For industrial teams, the platform enables faster formulation and product development, early property prediction before physical testing, reduced experimental waste, strong reuse of historical internal data, and data-backed technical decision-making.
Beyond incremental improvements, our platform supports breakthrough innovation by uncovering previously undiscovered correlations across past experiments and proposing promising formulation spaces that may not be intuitive.
An equally important outcome is organizational intelligence. Experimental knowledge no longer lives only in individual scientists’ notebooks; instead, it becomes a persistent, searchable, AI-accessible asset that compounds in value over time. Research institutes and universities similarly benefit through structured digital lab records, AI-assisted analysis, improved reproducibility, and better alignment with industrial research workflows.
Who are your customers and how do you plan to add prospective customers?
SarthhakAI serves R&D-intensive organizations in specialty chemicals, polymers, materials, coatings, inks, packaging materials, and advanced manufacturing. Customer acquisition is driven by domain-specific enterprise pilots, demonstrations on real formulation problems, industry conferences and technical workshops, partnerships with institutes and labs, and direct enterprise sales through word-of-mouth. In addition, the bodh scientific platform will soon be launched as a B2C offering. SarthhakAI follows a land-and-expand model, starting with one team or product line, proving value, and then expanding across additional groups, sites, and use cases within organizations.
What problem does Episteme Labs solve and how does it help R&D community?
Episteme Labs addresses a fundamental limitation in today’s industrial AI adoption: most organizations use AI models, but very few own and control their scientific intelligence. It provides an environment where organizations can build and train predictive models on their data, fine-tune domain-specific language models, evaluate, version, and govern models, and deploy models directly into R&D workflows.
This enables property-prediction models, performance classifiers, process-optimization models, and domain-tuned language models that reflect the organization’s unique chemistry and know-how. Over time, companies build a portfolio of proprietary models that become strategic assets, embedded directly inside everyday scientific work rather than living as standalone data-science projects.
What problem does Continuum Labs solve and how does it help R&D fraternity?
Continuum Labs focuses on connecting the physical world of laboratories and manufacturing plants with AI systems. It enables integration of analytical instruments, sensors and data acquisition systems, embedded systems, and robotics and automation, creating continuous data pipelines from experiments and processes into analytics and models.
The impact is a shift from manual, episodic data capture toward continuous, AI-driven experimentation and optimization. Scientists and engineers can monitor experiments in real time, detect deviations early, and progressively move toward semi-autonomous experimentation and closed-loop process optimization. Continuum Labs therefore forms the bridge between digital intelligence and physical execution.
Does your software help in filing patents and trademarks and how secure is your software?
SarthhakAI supports the technical preparation layer of IP generation by enabling structured technical documentation, invention disclosure drafting, and audit-ready experiment trails with full data lineage in all our platforms. This significantly reduces the effort required to prepare high-quality patent documentation, while final legal filing remains with patent and trademark professionals. From a security perspective, the platform is built for enterprise environments with role-based access control, organization-level data isolation, encryption in transit and at rest, and options for private or customer-owned cloud deployments. The bodh scientific platform is GDPR compliant and ISO 27001 certified by an accredited body.
What is your short-term and long-term business and monetization plan?
In the short term, revenue is generated from SaaS (Software-as-a-service) enterprise deployments, B2C SaaS subscriptions, and custom agent and model development on bodh scientific, with a strong focus on demonstrating RoI within customer R&D teams. In the medium term, SarthhakAI will expand through domain-specific solution bundles across formulations, polymers, coatings, and related industries, alongside large-model deployments via Episteme Labs.
In the long term, the company will move toward usage-based AI services and deep industry consortium solutions, with SarthhakAI evolving to conduct net-new research across scientific disciplines, supported by dedicated and automated laboratories powering large portions of industrial R&D through Continuum Labs.