Consumers are influenced by many different factors that Whisk maps through a rich food ontology.
We map properties like:
- Nutrition (via gov. published open data sets)
- Taste: E.g. flavour, type
- Purchase: E.g. price, perishability, alternatives
Novel Machine Learning
Machine Learning (Deep Learning) technology helps Whisk parse unstructured and frequently updated recipe and store data.
Through an ecosystem partners that have integrated Whisk, we collected millions of data points every week.
Data points include:
- 100,000,000 monthly recipe impressions
- 500,000 monthly shopping lists interactions
- 250,000 monthly store products
Personalisation & Recommendation
By combining our Food Genome, Machine Learning and proprietary data Whisk is able to to deliver world-leading personalisation and recommendation.
You can find some examples of our more sophisticated recommendations here:
Whisk delivers highly sophisticated solutions across the world. Our largest presence is the US, UK and Australia – but we have integrations in 10 regions and 7 languages.
We use the latest in cutting edge technologies, including:
- Databases: Google BigQuery, Mongo, Neo4J
- Code: Scala (back-end) and React (front-end)