As artificial intelligence rapidly transforms global industries, India’s KnowDis AI is charting a distinct path by fusing foundational research with real-world applications tailored to the country's needs. In this exclusive interview with AI Reporter Asia (AI Reporter America), Neeta Singal, Partner and Co-Founder of KnowDis AI, outlines the company’s ambitious 2025 innovation milestones — from a next-gen vernacular chatbot and Agentic AI-powered shopping assistants to cutting-edge multimodal agents for drug discovery. She also shares insights into KnowDis AI’s academic collaborations, the strategic role of Generative AI in search optimization, and how the startup is translating deep tech into impactful, scalable solutions across e-commerce and healthcare.

What are the key milestones in KnowDis AI innovation pipeline for 2025, and how do they align with the company's long-term vision?

In 2025, KnowDis AI is targeting key milestones that advance our 'Deep Tech for India' vision in three broad areas: 1. Indian languages, 2. E-Commerce and 3. Healthcare. We are planning the launch of a next-generation vernacular chatbot where we use cross-lingual techniques designed to improve performance in low-resource languages. We also plan to release an enhanced conversational shopping agent that leverages Agentic AI for proactive, multi-turn e-commerce dialogues. A third significant milestone that aligns with our healthcare focus involves validating and piloting a complex multimodal AI agent that can help scientists automate various steps of the drug discovery process.

Can you elaborate on the recent advancements in AI research at KnowDis AI and how they are shaping your product offerings?

Our recent AI research directly fuels product innovation, notably through advancements in Indic LLMs. We've developed various techniques enabling our chatbots to dynamically adjust communication style (tone, formality) based on user preferences. This helps the chatbots move beyond generic responses to enhance usability, particularly for diverse Indian languages.

How does KnowDis AI's collaboration with academic institutions, such as the Yardi School of AI at IIT Delhi, influence your research and development strategies?

Our collaboration with premier institutions like IIT Delhi's Yardi School of AI & and another one with IIT Bombay is fundamental to our R&D, creating a vital link between foundational research and practical application. Our collaborative work has resulted in publications in top-tier venues, including KDD 2025 and ICML 2024, with a focus on areas such as advanced E-commerce query analysis and novel drug design. These publications have, in turn, resulted in top-class products which have been deployed in real-world platforms. The work to be presented at KDD 2025 has been deployed in India’s largest B2B platform, and the work presented at ICML 2024 has been integrated into our proprietary Drug Discovery SaaS platform.

In what ways is KnowDis AI leveraging generative AI to enhance e-commerce search solutions, and what impact do you anticipate this will have on the market?

KnowDis AI is strategically integrating Generative AI to revolutionise e-commerce search and discovery. We have found that generative AI substantially helps improve existing systems, as well as opens up completely new possibilities that were previously impossible with older technologies. For example, in existing systems, we have found that Generative AI helps with advanced query understanding, especially in long-tail cases where traditional systems fail. Moreover, Generative AI is also helping us create truly novel experiences through the use of conversational shopping assistants, where users can ask follow-up questions and receive expert-like responses. This has the potential to change the online shopping experience significantly.

Could you share insights into KnowDis AI's approach to integrating cutting-edge AI technologies into practical applications for industries like healthcare and e-commerce?

We focus on bridging research potential with real-world value through in-depth domain understanding. We leverage state-of-the-art open-source models but differentiate through sophisticated fine-tuning, domain adaptation, and architectural modifications tailored to specific industry needs. We also perform careful optimisation for low latency and high throughput. Apart from the core modelling aspects, we also use robust MLOps practices that ensure continuous monitoring and reliability of our deployed models.