ShieldLabs

ShieldLabs is a platform for visitor identification and the detection of anonymous traffic, designed for web and SaaS applications. The problem it solves has been around for as long as free accounts have existed: the same person creates ten accounts from the same laptop, shares their subscription with half the neighbourhood, or logs in using stolen credentials, and the marketing figures look good until someone takes a closer look.

How does it work? A JavaScript snippet is added to the website, and with every visit, ShieldLabs reads over a hundred device, browser and network signals and builds a persistent identifier, which remains the same even after cookies are deleted, the IP address is changed or the user browses in incognito mode. In addition to the identifier, each request is assigned a risk score from 0 to 100 and a list of the signals that generated it: VPN, proxy, Tor, data centre IP, anti-detection browser, modified operating system, browser automation, spoofed location, suspicious paid click.

In my opinion, the most useful detail on the site is that the score isn't a black box. You see, for example, 'proxy +10, mismatched time zone +10, anti-detection browser +60', and you draw your own conclusions. The company clearly states in the FAQ that it doesn't block anything: it delivers the signals, and your code decides whether to allow, send for verification or reject. It's like a doorman who doesn't turn anyone away, but whispers in your ear who was there last night wearing a different hat.

On top of the signal layer are the 'patterns': pre-defined correlations, run across the entire traffic history, which uncover multi-accounting, shared accounts, account takeovers, account farms and ban evasion, each flagged as suspicious or dangerous, along with the accounts involved. There's no need to write your own rules. The analytics module completes the picture with a traffic quality score by channel, referrer and UTM, so you can see which partner or campaign is bringing in fake visits rather than real people.

The development side is straightforward: a single snippet, an API and webhooks across all plans, including the starter plan, and compatibility with WordPress, Shopify, Tilda, Next.js, React, Vue, Angular and Svelte. The plans are self-serve, scale according to the number of monthly identifications and do not involve sales calls or annual contracts. The company claims an identification accuracy of up to 99 per cent, a figure it presents as the result of signal correlation, rather than a single cookie.

The website features dedicated pages for different industries (iGaming, crypto, fintech, e-commerce, ticketing, streaming, AI products) and use cases, ranging from bonus and trial period abuse to paywall circumvention. As a reviewer, I would also highlight the 'AI Info' page, written for language models, as well as the public documentation and the open demo. These are signs that the product is designed to be understood before it is purchased, which, in a niche full of vague promises, matters.