Artificial intelligence has now become a driving force in all aspects associated with the rapidly evolving digital landscape. In this regard, one of the most profound transformations of all time is currently taking place in online markets.
In an exclusive interview with Amarinder Dhaliwal, Chief Product Officer at IndiaMART InterMesh Limited, we look at how AI is reshaping the company’s operations. He oversees both product and technology at IndiaMART. From improving product classification and catalog management to optimizing buyer-supplier matching, AI is playing a pivotal role in IndiaMART’s strategy. Excerpts from an interview:
“AI is the future of B2B”
AI plays an important role nowadays. Can you share a successful AI project you have conducted?
Data is truly the backbone of everything we do at IndiaMART. We’ve been using machine learning for seven or eight years, and more recently, AI, through various projects.
The company’s innovative AI-powered solutions have significantly improved user experience and operational efficiency. One such example is Lead Manager, a CRM platform that uses AI-suggested responses to streamline communication between buyers and sellers.
Over 60% of messages on the platform are now sent using these suggested responses, demonstrating the platform’s effectiveness in reducing response times and improving overall communication.
With a massive user base of 198 million registered buyers and 8 million suppliers offering 110 million products across 56 industries, our AI innovations are poised to have a massive impact on the industry. B2B landscape. This article looks at the company’s AI-powered solutions and explores how they are reshaping the future of online marketplaces.
It’s hard to choose just one, but here is a general overview:
Research and user experience: AI helps us understand the intent of users behind their searches, even if they are in different languages, misspelled, or combine terms. This allows for precise categorization of user needs and better matching.
Matchmaking: AI helps us identify the most suitable suppliers that can meet a buyer’s specific needs.
Communication platform: We have a platform called Lead Manager where suppliers and buyers interact. AI suggests actions to both parties, facilitating faster communication.
Improving the quality of the catalog: AI helps in cropping images, deep cleaning, and even extract information from product images and text descriptions. This ensures accurate product categorization and improves search results.
Content quality: Although we do not use AI for content generation (as ours is a user-generated platform), AI helps improve the quality of uploaded content by identifying specifications and ensuring proper categorization.
Can you explain how AI is being used to automate and improve catalog management, especially around product classification, image tagging, and content generation?
Our main goal is to improve data quality, no automation. Here’s how AI can help:
Image processing: AI helps clean and enhance uploaded product images.
Product labeling: Unlike a platform with a limited range of products, IndiaMART deals in hundreds of categories. AI helps label products based on crucial specifications and brand information.
Content categorization: AI analyzes product descriptions to identify the most relevant category for each product, ensure accurate search and matching results.
How to leverage AI for personalized user experiences?
We do not use specific AI techniques for personalization. Instead, We rely on different models within Lead Manager.
For example, when a buyer inquires about a mattress, AI suggests relevant follow-up questions a vendor might ask, such as size or material preference. These suggestions are tailored based on the specific product category, make communication between buyers and suppliers more effective.
Can you describe the AI algorithms used to match buyers with sellers?
We use a combination of open source and API-based models, including tools such as LLaMA and ChatGPTThese models are used to analyze and match buyer requirements with supplier offerings, continuously optimizing the matching process based on real-time data.
What challenges did IndiaMART face in terms of data quality and quantity while developing AI-based features?
The accuracy of any AI result depends on the quality of the data fed into the system. Since IndiaMART is a user-generated platform with millions of suppliers, Cleaning and ensuring data consistency is essential.
For example, when categorizing products, we encountered inconsistencies where commercial machines were classified as regular machines, or the mattress sizes were mixed up. Cleaning this data before using it to train models was a significant challenge.
How to optimize AI models to handle large volumes of data and real-time interactions?
With the progress made in Generative AIModel optimization has become more manageable. The cost of token processing has decreased and model performance has improved. We focus on improving prompts and leveraging efficient algorithms to efficiently handle large datasets and real-time interactions.
Can you share IndiaMART’s product roadmap for the next few years? How does AI play a role in these plans?
As mentioned earlier, AI is at the heart of our business model. We believe that by continually improving the quality and quantity of data feeding our AI models, we can significantly improve our matchmaking capabilities.
Our roadmap includes improving our AI Capabilities to improve content quality, search accuracy, and search functionality. We seek to better understand user needs in different languages and dialects, thereby improving the overall user experience and operational efficiency.
How do you ensure the privacy and security of user data, and what measures are taken to avoid algorithmic bias?
We prioritize data privacy by using aggregated data over personalized information. To avoid algorithmic bias, we focus on data cleaning and normalization to ensure AI models are trained on accurate and unbiased data.
