Harmonya's language for products incorporates the perspectives of consumers, brands and retailers and can be leveraged by any application and domain that uses product data.
Harmonya does not limit product characteristics to a predefined set of attributes. The absence of predefined hierarchies and characteristics reveals the ‘unknown unknowns’ about products.
Harmonya captures the ways consumers express their product perceptions and experiences, while simultaneously maintaining fundamental product attributes commonly used by the industry.
At the core of Harmonya’s language: a complex mapping of products and their related concepts. Our platform is powered by a constantly expanding set of inputs.
Harmonya’s data infrastructure is constantly refreshing, ingesting new inputs, and continuously connecting products with new concepts, ensuring that its language accurately reflects the consumer and industry point of view.
Harmonya provides an API-based data enrichment and augmentation layer, alongside a set of distinct applications that enhance the value of its underlying product data language.
Harmonya’s ability to marry consumer, shopper, and industry perspectives with product performance data unlocks an unprecedented depth of understanding for category managers to act on.
Granular and rich product understanding enables more dynamic product listing optimizations, and helps shoppers explore and find products that meet their evolving needs and preferences.
Harmonya’s machine learning models can help automatically manage product catalog and item master data needs such as dynamic product classification and categorization capabilities.
Harmonya’s evergreen insights engine helps innovation and R&D teams understand what’s driving success in the market with more precision, and significantly reduce the time it takes to bring products to life and on to shelves.
The backbone of
Data acquisition engines continuously run and refresh to ingest product related data from publicly available and customer data sources
Data components such as nutrition classifiers and attribute normalizers unify unstructured data originating from multiple sources
Harmonya's product knowledge graph applies advanced relation discovery techniques to identify how clusters of products are related to one another
ML and NLP models, including topic modeling and sentiment analysis, form the complex mapping of products and their related concepts