Personalization is an important feature for brands wishing to attract customers – in fact, a study by McKinsey1 found that 71% of shoppers expect businesses to deliver personalized interactions.
Amidst this, Jarvis ML2, a “machine-learning-as-a-service” platform leverages brands’ data to help them personalize every customer journey. Its algorithms can identify sales and inventory patterns and then turn them into “actionable brand engagements, like marketing campaigns or personalized website experiences,” explains CEO Rakesh Yadav3.
A former senior staff engineer at Google, Rakesh founded Jarvis ML in 2021 after seeing a shift in consumer purchasing trends during the pandemic. “Online recommendation strategies are [now] mission critical for enterprises to adapt to this changing consumer paradigm,” he explains. “Giant tech companies like Amazon, Airbnb, Google and Facebook use machine learning to delight consumers and restrict the independence of the growth-stage and mid-market companies who end up being relegated to supplier or fulfilment roles in the tech giant ecosystems. Jarvis ML enables these companies to leverage data they already have to reduce the dependence on tech giants while scaling sustainably.”
The solution, which integrates with leading e-commerce platforms like Shopify and WooCommerce, helps brands predict the precise prices and products each individual customer is most likely to convert on, then deliver them customized recommendations accordingly. It also allows brands to create personalized marketing campaigns to drive more repeat sales.
The company has just completed a successful seed round of fundraising which it will use to grow its R&D and sales and marketing teams.