ShieldLabs is fraud detection and prevention with traffic quality scoring. Stops multi-accounting, account sharing, account takeover, fake signups and ad fraud.

Key Capabilities

• Visitor Identification: Recognises returning visitors across separate sessions, after cleared cookies, in incognito mode and through IP address changes, over long periods of time.
• User Identification: Automatically detects linked users across accounts, devices and IP addresses
• Network and Device Intelligence: More than 100 signals are collected together and cross-checked against each other, which exposes deep masking and delivers up to 99% detection accuracy.
• Anonymity Signal Detection: VPN detection, proxy detection, Tor detection, Apple Private Relay detection, datacenter IP detection, IP reputation, anti-detect browser detection, browser automation detection, operating system tampering, incognito mode and geolocation spoofing. Each one is returned as an individual named signal, not a single binary flag.
• Bot and AI Traffic Detection: Identifies bots, automated traffic and AI agents, and separates bad bots from good ones.
• Risk Scoring: Scores the visitor, the user, the device and the IP address. Detected anonymity signals, behavioural history, deterministic rules and AI are aggregated into a single normalised risk score.
• Ready-Made Patterns: Pre-computed detections of multi-accounting, account sharing, account takeover and impossible travel, correlated across users, devices, IP addresses and their behaviour. No custom rules to write and no fraud model to train.
• Risk Analytics: A single traffic quality score plus a score for every source, channel, referrer and UTM campaign, with analytics across visitors, users and devices.
• AI Traffic and Fraud Copilot: A layer that turns collected data into summaries, investigations, direct answers and ready-to-run actions, adapted to the customer’s type of business.

Use Cases: Protect

• New-account fraud: fraudulent signups at registration.
• Multi-accounting: one person running many accounts to farm a benefit.
• Account takeover: someone else logging into a real user’s account.
• Payment fraud: high-risk and fraudulent transactions at checkout.
• Ad fraud: paid traffic that does not match what was paid for.
• Affiliate fraud: partners driving fake or incentivised traffic and signups to earn commission.
• Bonus abuse: signup bonuses claimed repeatedly by the same person.
• Promo abuse: discount codes redeemed far beyond intent.
• Referral fraud: self-referrals and fake invitees.
• Free-trial abuse: endless new trials from the same person.
• Loyalty fraud: points and rewards farmed illegitimately.
• Ban evasion: banned users returning under new identities.
• Sybil attacks: many fake identities used to sway a system, common in crypto and Web3.
• Impossible travel: the same account or device appearing in places too far apart for the time between the visits.
• Location spoofing: faked geography to reach restricted content or pricing.

Use Cases: Grow

• Returning visitor recognition: the same capability pointed the other way, identifying a good returning customer without asking them to prove who they are again.
• Account sharing: one subscription used by many people, turned into an upgrade conversation instead of a loss.
• Paywall bypass: readers consuming paid content without paying, converted into subscribers.
  Traffic quality: seeing which acquisition channels deliver real people, so budget moves to the sources that convert.

Who It Is For

Built for AI, iGaming, cryptocurrency and Web3, SaaS, fintech, e-commerce, travel, marketplaces, media and streaming, ticketing, and gaming.

Aimed at SMBs and teams on a small budget that want a ready-made solution: five minutes to integrate and detection at the level of enterprise platforms, without enterprise pricing and without a call with a sales team.

It fits developers, analysts, marketers, growth, product, risk and support teams, and founders running all of it themselves.

Integrations

Integration takes five minutes.

• Client SDKs: JavaScript, React, Next.js, Vue, Angular, Svelte, Preact, WordPress, Shopify, Tilda.
• Server SDKs: Node.js, Python, Go, PHP, Java, .NET.

A public API and signed webhooks sit underneath, so ShieldLabs connects from any language. An OpenAPI specification is published for everything else.

Pricing

Self-serve and flat-rate, with a price that drops as traffic grows, no sales call and no annual contract. The free plan gives 5,000 one-time identifications. Paid plans are Starter at $99 per month for 25,000 identifications, Growth at $399 per month for 150,000, and Scale at $999 per month for 500,000, with up to 20% off on annual billing. Effective cost falls from $0.00396 to $0.00199 per identification as volume grows. The API, webhooks and support are included on every plan, including the free one.