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A suite of transformers-based classifiers trained on multiple languages. They attribute companies to fixed taxonomies (sectors, industries, categories, insurance codes etc).
A combination of Named Entity Recognisers and heuristics that find and understand entities from company websites.
A collection of extractive-based models trained to find specific text passages. They extract what a company specializes in in an open-ended fashion for new potential niches.
A selection of extractive and semantic techniques working in unison to associate tags to a company.
An ensemble of abstractive and extractive models working on top of our website sectioning algorithms. Professionally trained to sniff out marketing gibberish (like this one) from descriptions.
A carefully fine-tuned classifier model on short texts. Using only the name of a company, Gary takes rough guesses of industry placement.
Returns the headquarters and all secondary physical locations and addresses for the company. Each address can be identified by its Country, Region, City, Postcode, and Geo-coordinates.
The short description provides a brief overview of the company’s activities, while the longer description delves into more detail about the company’s business operations.
Companies can be categorized into three types based on their ownership structure. “Public” refers to companies that are listed on the stock exchange, “Non-profit” is reserved for organizations that do not operate for the purpose of making a profit, and “Private” applies to companies that are privately held.
The year when the company was founded.
Veridion’s proprietary Industry classification scheme uses a machine learning model to classify the business based on its business activities.
The web address (URL) of the company’s website.
These will usually be front office phone numbers or department level phone numbers.
General business emails for the company.
A machine learning model identifies key attributes, related to the company’s activities. For example, a tech company may contain tags such as “startup”, “SaaS”, “Artificial Intelligence”. Or a Landscaper may contain risk tags such as “Tree Trimming”.
Number of people employed by the company.
Company revenue, based on disclosed information on the web.
The company’s LinkedIn URL
The company’s YouTube URL
The company’s Facebook URL
The company’s Tik-Tok URL
The primary and secondary six-digit NAICS codes & labels based on machine learning predications
The four-digit numerical Standard Industrial Classification (SIC) Codes & labels
The four-digit numerical code ISIC codes & labels
The four-digit NACE codes & labels
Up to five Insurance Bureau of Canada (IBC) codes & labels based on machine learning predictions
The four-digit NCCI code that insurers use to classify employees exposure to risk
Types of technologies that are used by the company website and public assets such as: Content Management System, E-Commerce Platform and other web technologies.
The product name as provided by the company that is selling or producing the product for over 4 million manufacturers.
The primary section of information detailing the product’s specifications that are being offered by a company.
The (two,six and eight-digit) UNSPSC numerical code and label for the product. Classified based on machine learning models
The SASB Sustainability Industry Classification Scheme (SICS) Industry code & label for a company. Based on machine learning predicitions
ESG Commitments disclosed by the company such as Net Zero Pledges, Human Rights Commitments, Diversity Commitments etc. Classified into standard industry taxonomies.
ESG News about the companies behaviours such as its impact on the environment, sustainability initiatives, governance. Classified into standard industry taxonomies.
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