The use of Large Language Models (LLMs) in eCommerce is still in its early days. Many companies are already experimenting with chatbots, automated product recommendations, or smarter search functions – yet in practice, the limitations quickly become clear. New products appear that a model has never seen before. Customers ask questions in countless different ways, and the system isn’t prepared for them. And for nearly every task – from recommendation to sentiment analysis – separate, specialized models are required. The result: complexity, high costs, and little flexibility.

This is where a new research project comes in: eCeLLM by Bo Peng and colleagues.

“We aim to build a generalist model that can effectively generalize across diverse e-commerce tasks by leveraging large-scale, high-quality instruction data.”
— Bo Peng et al., 2024

The goal of the research is to train LLMs to better reflect the real-world requirements of digital commerce. Instead of deploying one model for product recognition, another for attribute extraction, and yet another for customer inquiries, eCeLLM follows the approach of a generalist model. This is made possible by a dedicated dataset – ECInstruct – containing tens of thousands of high-quality instructions covering ten core eCommerce tasks.

The results are impressive: in tests, eCeLLM significantly outperformed established models such as GPT-4 or other state-of-the-art approaches – by more than ten percent on average. Even more interesting, the models remained robust when confronted with entirely new products or unseen question formulations. For companies with dynamic assortments and diverse customers, that’s a decisive advantage.

For eCommerce companies, this means two things. First, the fragmentation into many small, isolated AI components could soon be a thing of the past. A well-trained, task-spanning eCommerce LLM can handle multiple functions at once – saving resources in development and operations. Second, time-to-market decreases: new products no longer need to be laboriously “learned” by the model before becoming part of recommendations or search results. The impact for customers is tangible – more flexible answers, more precise recommendations, and an overall better experience.

Of course, eCeLLM remains a research project for now. But it clearly shows where the open challenges lie: user profiles and context data are still limited, explainability (“Why this product?”) is missing, and transferring lab results into productive retail environments is not yet solved.

Our Perspective at foobar Agency

For us, eCeLLM is primarily a signal: AI research in eCommerce is becoming more relevant and practical. It’s no longer about impressive demos, but about models that can truly make a difference in dynamic, complex commerce environments.

In many companies today, specialized (AI) systems work side by side – one for recommendations, one for search, one for support. This is precisely what the Composable Commerce approach we follow at foobar Agency is designed for: independent yet interconnected components that together create a strong, coherent ecosystem.

What eCeLLM illustrates in this context is the next level of evolution. AI models no longer need to be trained in isolation for each task; they can share and transfer knowledge across domains. In a modular, open architecture, this creates new opportunities: specialized tools remain, but are complemented by cross-functional eCommerce LLMs that understand relationships and make data flows smarter.

To unlock this potential, however, companies need more than just powerful models. They need a clean data foundation, clear interfaces, and a flexible architecture. That’s exactly where we at foobar Agency come in: we create the technical and organizational prerequisites that allow new technologies to be integrated seamlessly. Our experience shows that Composable architectures are key – they give businesses the freedom to adopt models like eCeLLM as soon as they become market-ready, without being tied to monolithic systems.

In short: eCeLLM offers a glimpse into the future, but the groundwork for it is being laid today. Companies that align their data, systems, and processes now will benefit faster and more confidently from these developments in the years ahead – and gain a clear competitive edge.

Dominik Thalmeier, AI Architect at foobar Agency concludes:‍

"eCeLLM is not a finished product but a research milestone. Yet it clearly shows that the future of eCommerce lies in robust, generalist AI systems – and that businesses investing in composability and data quality today will be the winners tomorrow."
— Dominik Thalmeier

Do you want to learn how your company can start building the foundation to use AI models like eCeLLM successfully in the future?

👉 Let’s talk – we support you from strategy to implementation: Contact us

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FAQ: AI & eCeLLM in eCommerce

How do traditional LLMs differ from cross-functional eCommerce models like eCeLLM?
Traditional LLMs are trained on general knowledge and quickly reach their limits in commerce. eCeLLM, on the other hand, is trained with real eCommerce data and can handle tasks such as product recognition, recommendations, or question answering with much greater precision.

Why are clean product data and composability crucial for successfully implementing AI in eCommerce?
Models like eCeLLM can only perform reliably when they are fed with consistent, high-quality data. Composability additionally ensures that new technologies can be integrated flexibly – without relying on monolithic systems.

Can a single model really handle multiple tasks such as recommendations, search, and customer service at the same time?
Research results suggest that cross-functional eCommerce models can share knowledge across different domains – and often achieve better results than specialized single-purpose models. We’re excited to see further findings and practical applications emerge.

What advantages does eCeLLM offer for retailers with large, dynamic product assortments?
The model is more robust when dealing with new products and varying language patterns. This allows retailers to react faster, deliver more precise recommendations, and ultimately improve the overall customer experience.

When is the right time for companies to invest in AI-driven eCommerce solutions like eCeLLM?
eCeLLM is still a research project, but companies should start preparing their data foundations and system architecture today. Those who invest now in data quality and flexible architectures will be ready to adopt such models as soon as they become market-ready.

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