Luviner Ward learns what human traffic looks like and blocks everything else. Zero rules, zero signatures, zero configuration. One Docker command to deploy.
Automated scrapers steal your content, pricing, and competitive intelligence every day. By the time you notice, the damage is done.
Bot traffic inflates your analytics, wastes your ad budget, and skews your business decisions. You're optimizing for ghosts.
Traditional WAFs need constant rule updates. Every new bot pattern means a new rule. You're always one step behind.
Ward uses a liquid neural network to learn the temporal fingerprint of human browsing. No rules to write, no signatures to update. It observes your real traffic, builds a behavioral model, and flags deviations in real time.
* Results from Ward's internal synthetic test suite (5 bot profiles against simulated human traffic). These are sanity-check numbers, not a reproducible benchmark on public third-party datasets — always validate Ward on your own traffic before relying on these figures. A public reproducible benchmark is on the Q3 2026 roadmap.
Bots with regular timing, missing cookies, no referrer. Ward catches them from the first request sequence — their timing is too perfect to be human.
Rapid login attempts with rotating IPs. Ward detects the behavioral pattern, not the IP — rotating proxies don't help against neural detection.
Automated API calls that mimic legitimate usage but deviate in timing, path sequences, or header patterns. Ward learns your API's normal rhythm.
Run docker run -p 9741:9741 luviner/ward. Ward starts in learning mode automatically.
Ward observes your first 500 requests and builds a behavioral model of normal human traffic. No labels, no training data needed.
Once trained, every request is scored in 0.23ms. Bots get flagged with an action: allow or block. Your app decides what to do.
Traffic patterns change? Reset and re-learn, or upload a CSV of known-good traffic. Ward adapts to your evolving audience.
Deploy Ward today. Zero config, zero cloud dependency, zero compromises on privacy.