Many popular horizontal AI solutions use data and content that’s collected from across the web when training AI models. These large, horizontal datasets are missing out on retail-specific information and consumer behaviors. As a result, it’s near impossible to fully solve retail’s vertical-specific problems with a horizontal AI. Their outputs are generic and marginally effective.
Highly-specific, smaller language models are retailers’ answer. And the retail-specific richness of Lily AI’s data makes it stand apart.
In a world of garbage in garbage out, quality training data is more critical than ever.
Lily AI has worked with some of the world’s largest retailers and brands for almost a decade. In that time, we’ve cultivated the largest proprietary dataset of private retail data, including a wide range of images, text, products, and consumer behaviors across fashion, home, and beauty.
With this training data, the Lily AI team of machine learning engineers and data scientists, who partner with retail merchandising domain experts, trains and fine-tunes an ever-expanding mix of generative AI, machine learning, deep learning, computer vision, and small language models. Today, these models can detect thousands of attributes in our product taxonomy with industry-leading precision and efficiency. As a result, only Lily AI’s suite of vertical AI solutions for retail can deliver enterprise-grade and consumer-approved attribution to power retail-specific use cases and technology integrations.
The human touch touches all aspects of Lily AI’s outputs.
Since new product and consumer data flows through Lily AI’s models every day, fresh, and highly relevant customer language is always being added to our human-powered product taxonomy of over 20,000 customer-oriented product attributes, synonyms, and trends.
Our extensive taxonomy reflects words and details that today’s consumers actually use and shop for, including:
- The latest macro- and micro-trends and aesthetics that pop up everywhere, from the runway to TikTok
- Subjective attributes, such as styles, fit, color families, occasions, and special occasions
- Objective attributes, such as materials, embellishments, fabrics, colors, closures, and more
- Synonyms that showcase different ways to describe the same product attribute, such as glossy vs. high shine
Only real retail data drives quality model outputs and results.
Brands and retailers who want to drive conversions and increase profitability can only trust solutions that use retail-specific data when training their vertical AI models. The strength of our training data fueling the AI models, as well as the team’s deep expertise in both AI and retail, make Lily AI the best choice for retailers who want to increase product discoverability and sales at scale.
After all, when investing in the best AI solutions to enhance online and offline customer experiences, retailers see a 90% increase in revenue. And who could refuse that?
