How Multimodal AI can transform search in e-Commerce

We’ve been working very hard this past year on laying the foundation for AI findr, and now the results are starting to flourish. AI findr is a semantic search engine for eCommerce and other customer-facing data that allows mind-blowing search and discovery experiences. This product seeks to understand user intent beyond keyword or classic algorithms, connecting it with the company’s offerings and priorities and bringing back the best result, which both users and businesses want.

We are collaborating with leading companies that are already incorporating our solution into several parts of their tech stack. We use AI to create more dynamic and responsive search tools that significantly improve customer engagement and conversion rates. This post will look into one of our most successful applications: enhancing search for a fashion brand's e-commerce.?

In the dynamic fashion world, staying relevant and engaging with customers has always been the top priority. As fashion brands look to innovate and offer personalized experiences, artificial intelligence (AI) stands out as a game-changer, particularly in enhancing search capabilities. This blog post dives into how specific fashion brands can leverage AI to refine their customer search experiences, focusing on customizing AI systems using fine-tuning techniques.

When customers interact with AI findr, they experience intuitive personalization. For instance, if they search for queries such as "I want summer dresses" or “I would like an outfit for a rock concert,”? our system grasps the user's intrinsic meaning and intention. It can retrieve relevant products for the query without mentioning these concepts in their descriptions.

Fashion brands continuously seek innovative methods to connect with their consumers. In this quest, AI has emerged as a pivotal technology, especially in search and discovery online. By integrating AI, brands can offer more intuitive and responsive search functions that accurately understand and predict consumer desires.

At the core of AI findr's innovative approach are multimodal embedding models, crucial for interpreting and processing the complex, varied forms of data that users input into search queries. These models combine and analyze data from different modalities—such as text, images, and possibly even voice—to create a unified understanding of what users are searching for. This capability is particularly transformative in fashion e-commerce, where seamlessly integrating visual cues with textual descriptions can significantly enhance search results' relevance and precision. By employing these sophisticated AI models, AI findr captures the explicit content of a user's query and the subtler nuances and context that underlie their search intentions, delivering a truly customized shopping experience that aligns with the user's desires and the brand's unique offerings.

The Core of AI Customization: Fine-Tuning Multimodal Embedding Models

Before diving into fine-tuning, it’s crucial to understand what multimodal embeddings are. In the context of AI, embeddings are representations of items (like text or images) in a vector space – in simple words is a way to convert data like words or images into vectors (lists of numbers) that a computer can understand and use to find similarities or differences between them. Multimodal embeddings, therefore, combine these different types of data into a single, coherent model that can process and analyze them in tandem. This is particularly useful in fashion, where visual elements and descriptive text are crucial in defining and searching for products.

Multimodal embedding models and powerful fine-tuning techniques provide a robust framework for enhancing search functionalities. These systems understand text and images, creating a holistic search mechanism that can handle queries across different modalities, such as text-to-image and image-to-image searches.

Why Fine-tuning?

Fine-tuning is a method of training where a pre-trained model is further adjusted to cater to specific needs or datasets. Each fashion brand has unique styles, aesthetics, and terminologies. When you fine-tune an embedding model using the brand’s content it allows the AI to adapt to these specific characteristics. Training on specific datasets makes the model more accurate in predicting and interpreting the brand’s particular content. On top of that, It also improves the retrieval quality, thereby making the search and discovery process faster and more relevant – which, in the end, increases the conversion rates.

Fine-tuning an AI model for a fashion brand is a challenging process that usually requires several iterations and a significant investment of time and resources until you reach a positive outcome. The first step is gathering or crafting a diverse dataset with images and high-quality product descriptions. Besides, you need a model already pre-trained on a large, diverse data set to ensure a broad understanding of different styles and contexts. You can then introduce your specific dataset to the model and continue its training, which allows the AI to learn and adapt to the nuances of your brand. Since fashion trends are ever-changing, it's important to regularly update the dataset and retrain the model to remain current and effective.

Fashion brands can significantly enhance the user experience with a finely tuned AI system. One way to do this is through visual search, allowing customers to upload an image to find similar items or styles. Another method is textual search refinement, which makes searches based on descriptive text more accurate, helping customers find exactly what they're looking for. Additionally, cross-modal searches enable customers to start with a text description and receive relevant image suggestions, or vice versa, making the search process more flexible and user-friendly.

The direct impact of implementing finely-tuned multimodal embedding models can also translate to improvements in sales revenue. By reducing the friction in finding relevant products, fashion brands can ensure that more visits convert to sales. The efficiency of the AI model in handling and interpreting complex customer queries translates to a faster, more fulfilling search process, which, in turn, discourages drop-offs and abandoned carts.

Conclusion: The Fashion Forward with AI

Integrating fine-tuned multimodal embedding models into fashion brand strategies represents a significant leap forward. By adopting and customizing these technologies, brands can offer uniquely engaging, efficient, and personalized search experiences that resonate with modern consumers. As the technology evolves, it will be fascinating to see how much further the fusion of fashion and AI will go, potentially reshaping the industry in ways we have yet to imagine.

Embracing AI in fashion isn't just about keeping up with technology—it's about setting the pace in a fast-evolving marketplace. With finely tuned AI systems, brands enhance their user interfaces and deepen their understanding of customer preferences, paving the way for a future where AI and fashion grow together, seamlessly integrated. For fashion brands looking to stay competitive and innovative, embracing and customizing AI technologies is not just an option—it's an essential strategy for the future.

Brands that consistently provide satisfying search experiences build stronger customer relationships, fostering loyalty and repeat business, which is crucial for sustainable growth. Additionally, the insights gathered from the AI's analysis of customer interactions can inform other business areas, such as inventory management, marketing strategies, and product development, aligning the entire brand's operations with actual consumer needs and preferences.

In conclusion, integrating fine-tuned multimodal embedding models into fashion brands' search functionalities enhances user experiences and significantly improves conversion rates and sales revenue. As AI technologies evolve, their potential to transform the fashion industry becomes even more profound, indicating that the future of fashion will increasingly rely on intelligent, data-driven systems to meet the demands of modern consumers.