Users demand a good experience.

Harassment, scams, and fraud in e-commerce chat messages ruin that. Plus offline transactions can be detrimental to marketplaces.

 
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Spectrum helps e-commerce companies’ Trust & Safety efforts scale through improved accuracy, international language support and an advanced library of behavioral detection.

 
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Recognize

Spectrum’s classifiers help e-commerce companies recognize toxic behavior in real time across multiple languages.

 
 

Prioritize & Respond

Results are used to prioritize and automate responses.


FEATURED E-COMMERCE BLOG POSTS


FEATURED MODELS FOR E-COMMERCE

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Offline Transactions

E-Commerce companies use our Offline Transactions model to detect when buyers and sellers are trying to move a transaction outside of the platform, jeopardizing the company’s ability to collect revenue.

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Toxic

E-Commerce companies use our toxic model to detect content that includes anything considered sexual, insulting, harassing, or hateful; this type of content would typically be removed from a platform but would not necessarily result in the poster being banned from the site.

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Fraud

E-Commerce companies use our fraud model to detect content that is deceiving or misrepresenting and has to do with payment or accounts that is typically illegal in nature.


HIGHLIGHTED CAPABILTIES

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Multi-Lingual Support

Your home-grown solution may have English covered, but you’ll need multi-lingual support to protect your community all over the globe. You choose the languages you need when you need them.

Spectrum Labs’ proprietary language features allow us to support international data at scale while maintaining strong performance across all models.

 
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Real-Time APIs

Transactions can happen fast – threats can be introduced to your community and your revenue stream in the blink of an eye, and spread before you’re even notified of them through self-reporting solutions.

Spectrum Labs provides real-time results that allow you to immediately recognize and respond to toxicity before it evolves into an even bigger problem.