Traditional methods of understanding consumer sentiment, such as focus groups and surveys, are often time-consuming, expensive, and limited in scope. Analyzing vast datasets of human conversations to extract actionable insights can be challenging and require significant manual effort.

Quilt.AI offers a platform, Sphere, that leverages predictive AI and multilingual natural language processing to rapidly analyze and categorize large volumes of text, video, and image data, providing instant feedback on consumer perception of brands and assets. The platform's AI models are trained on extensive datasets, enabling it to capture not only basic features but also complexities such as narrative, linguistic nuances, and cultural context. By analyzing text, images, and videos frame by frame, the platform identifies objects, emotions, and brand values, uncovering valuable insights and patterns that may otherwise go unnoticed. This allows brands to quickly understand consumer sentiment, optimize content, and refine brand strategy without relying on traditional methods.

Predictive AI for instant feedback on consumer sentiment

Multilingual natural language processing models trained on over a billion text-image pairings

Text analytics to uncover entities, sentiments, and themes from text data

Video analytics to recognize objects, emotions, and cultural nuances within videos

Image analytics to discover insights within visual data, including colors, shapes, textures, and photographic compositions

Zero-shot multimodal models for targeted commercial analysis and identification of brand values

The primary audience includes brands, marketing teams, and content creators seeking to understand consumer sentiment, optimize content, and refine brand strategy.