Before “generative AI” became a household phrase, the foundation for intelligent digital commerce was already being built by a small group of engineers and researchers determined to make machines understand intent. They weren’t just designing e-commerce systems—they were building the infrastructure for what would become conversational commerce.
Among these pioneers is Ridhima Mahajan, a Senior IEEE Member and senior engineer specializing in marketplace architecture and algorithms for retail commerce. Over the past decade, she has helped design and scale data-driven systems that power some of today’s most complex digital marketplaces, from pricing engines to logistics optimizers. But her most influential contribution came long before modern large language models existed—when she helped architect eXpert Personal Shopper (XPS), one of the earliest conversational shopping engines powered by IBM Watson’s natural language processing capabilities.
When AI Learned to Shop
Nearly a decade ago, when natural language understanding (NLU) was still in its infancy, Ridhima worked on the team that built XPS, a system designed to help users find products through conversation. It was, at the time, a radical idea: that AI could parse unstructured language—“I need something warm but lightweight for hiking in the Alps”—and map it to structured product data, ranking and recommending the most relevant items from retailers like The North Face and Flowers.com.
Using early neural and symbolic AI techniques, the system combined user intent recognition, product graph mapping, and ranking logic to deliver accurate, human-like suggestions. The underlying framework was later patented for its innovation in integrating symbolic reasoning with statistical learning—an early form of hybrid intelligence that remains central to AI development today.
“The goal was never just to make machines respond—it was to make them understand,” Ridhima recalls. “We wanted to teach AI how to interpret not only what users say, but what they mean.”
This work laid the groundwork for what would later become standard in recommendation systems and retail AI—techniques for dynamic product ranking, intent inference, and multi-turn conversational guidance.
Architecture That Stood the Test of Time
What made the XPS system remarkable wasn’t just its novelty but its architectural foresight. Built for scale and reliability long before AI infrastructure became mainstream, it demonstrated how a well-designed data pipeline could bridge human ambiguity and machine precision.
The system’s success hinged on a principle that remains true today: hybrid intelligence—the fusion of symbolic rules and neural reasoning to achieve interpretability and accuracy. The architecture supported high recall for diverse product categories, fault tolerance for live sessions, and adaptive ranking models that improved through feedback loops. These are the same principles now found in modern AI-driven platforms—from recommendation engines to conversational agents to autonomous decision systems.
Ridhima’s experience with this early hybrid system directly informs her current work architecting large-scale backend systems for pricing, payments, and logistics. The same qualities that made XPS ahead of its time—resilience, fairness, and scalability—are now essential to every data-intensive product she helps build.
Designing for Fairness and Accuracy in the Algorithmic Age
As AI takes a larger role in commerce, from dynamic pricing to predictive fulfillment, the challenges Ridhima confronted years ago have only grown in importance. “The technical question is no longer just ‘can AI make the right recommendation?’” she says. “It’s ‘can it do so fairly, transparently, and in a way that users can trust?’”
Algorithmic fairness and accuracy are now central to how marketplaces operate. Biased models can distort recommendations, skew prices, or disadvantage certain sellers or buyers. Ridhima’s early work anticipated this reality by emphasizing structured reasoning and explainability—two elements critical for responsible AI adoption.
Her approach combines data integrity, transparency, and contextual learning, ensuring that AI systems not only optimize outcomes but do so ethically. It’s a perspective informed by both her engineering experience and her contributions as an Editorial Board Member at the SARC Journal of Innovative Science, where she helps review research shaping the future of intelligent systems.
Conversational Commerce Comes Full Circle
Today, conversational AI is once again transforming retail—this time powered by advanced LLMs capable of maintaining context and memory across long interactions. From personalized shopping assistants to automated customer support, AI systems are revisiting many of the same challenges XPS addressed years ago: understanding intent, balancing precision with personalization, and maintaining trust at scale.
Ridhima sees this as a full-circle moment for the industry. “The tools have changed—models are larger, inference is faster—but the core challenge remains the same,” she notes. “It’s still about bridging human language and machine understanding in a way that feels natural and useful.”
Her perspective aligns with broader trends shaping global AI competitiveness. As the U.S. continues to lead in AI innovation, the lessons from early projects like XPS—architectural robustness, fairness, and interpretability—remain essential for ensuring responsible growth in recommendation-driven markets.
Architecting the Future of AI Marketplaces
Today, conversational and agentic AI are once again reshaping the commerce landscape. Voice-based shopping assistants, automated customer support, and AI-driven logistics systems all trace their roots back to the same challenges Ridhima helped address: how to interpret human intent, balance personalization with accuracy, and maintain trust at scale.
As she explains, “The resurgence of conversational and agentic AI in commerce isn’t a new chapter—it’s a continuation. The same architectural questions persist: How do we design systems that are fast, fair, and context-aware? How do we protect user privacy while delivering personalization?”
These questions are not just technical—they are strategic. In a recommendations-driven global market, the ability to ensure accuracy, fairness, and contextual understanding in AI systems is a matter of national competitiveness. The U.S., in particular, stands at a pivotal moment in shaping the standards for responsible, high-performance AI.
Ridhima’s early contributions foreshadowed this reality. By architecting one of the first systems where AI could reason about consumer intent and product relevance, she helped lay the foundation for the intelligent marketplaces that now define digital commerce.
As the industry moves toward ever more autonomous systems, her work offers both a blueprint and a warning: the success of future AI platforms will depend not just on their power to predict, but on their capacity to understand—and to do so fairly, transparently, and at scale.
