Case Studies
Real-Time Fraud Detection
Company: FinTech Solutions Inc.
WebAssembly enabled real-time fraud detection at scale, processing 100,000+ transactions/second with sub-10ms latency.
Edge ML for Retail
Company: SmartRetail Inc.
Deployed WebAssembly-optimized models across 500+ stores for real-time inventory and customer analytics.
Healthcare Diagnostics
Company: MedAI Systems
Integrated WebAssembly-accelerated diagnostic models into EHR systems with 100% FHIR compatibility.
WebAssembly + Fraud Detection
Financial institutions using Rust-on-Wasm achieved 300k+ predictions per second on standard servers.
// Wasm optimized inference
pub fn detect_fraud(input: &Tensor) -> bool {
#[wasm_optimize(level = 3)]
unsafe {
// SIMD-optimized fraud patterns
let sig = unsafe { compute_signature(input) };
FRAUD_DB.contains(&sig)
}
}
Edge ML in Retail
Using WebAssembly for edge inference reduced cloud dependency while enabling real-time analytics:
55% reduction in store-server latency | 82% lower cloud costs | 99.99% uptime
Healthcare Diagnostics
WebAssembly enabled deployment of complex diagnostic models to edge devices with 0.0001% drift compared to Python baselines