Explorium, a Tel Aviv startup building an automated data and feature discovery platform, today announced it had raised $19 million total across various funding rounds. Emerge and F2 Capital both provided $3.6 million seed investments; Zeev Ventures led with an $15.5 million series A investment round.
Explorium recently saw an incredible year, earning Fortune 100 customers from industries including financial services, consumer packaged goods, retail, and ecommerce. Explorium CEO Maor Shlomo previously led large-scale data mining platforms for IronSource, Natural Intelligence and the Israel Defense Force’s 8200 intelligence unit before cofounding Explorium alongside cofounders Or Tamir and Omer Har. “We’re doing for machine learning data what search engines did for web pages,” stated Explorium cofounders Or Tamir and Omer Har were.
Explorium’s platform serves as a central repository for an organization’s information, linking disparate sources together seamlessly and dynamically. Utilizing machine learning, Explorium extracts, engineers, aggregates and integrates the most pertinent features from data in order to power sophisticated predictive algorithms – before ranking and deploying only those which show promise.
Explorium’s predictive variables can be leveraged by lenders and insurers alike to discover predictive variables from thousands of data sources, says Shlomo, while retailers use it to forecast which customers are most likely to purchase each product. “Like search engines that quickly retrieve relevant results for you on the Internet, Explorium works similarly by searching data sources within and outside your organization to produce features that lead to accurate models,” according to him.
Explorium provides data scientists with tools for adding custom code that incorporates domain knowledge and optimizing AI models, as well as tools designed to identify optimization-informed patterns from large corpora.
Explorium’s vision of enabling data scientists by gathering relevant information from every source on scale and making models more robust is creating a paradigm shift in data science,” according to Emerge founding partner Dovi Ollech. Working closely with their team from day one confirmed they possess both expertise and ability needed to implement such a revolutionary platform.
Explorium joins an emerging “auto ML” segment. Databricks recently unveiled an autoML toolkit, designed to automate hyperparameter tuning, batch prediction and model search; IBM released Watson Studio AutoAI back in June; this cloud service from Microsoft promises enterprise AI model creation as does Google AutoML suite.
IDC forecasts that global spending on cognitive and AI systems will reach $77.6 billion by 2022, up from $24 billion last year. Gartner agrees: In its survey of thousands of business executives worldwide, they discovered AI implementation increased 270% within four years – 37% year over year!
