AI-powered code optimization and performance analysis
// Current implementation - 847ms average for (Order order : orders) { PaymentResult result = paymentAPI.charge(order); database.save(result); emailService.sendConfirmation(order); }
// AI-optimized - 124ms average (85% improvement) CompletableFuture>[] futures = orders.stream() .map(order -> CompletableFuture .supplyAsync(() -> paymentAPI.charge(order)) .thenCompose(result -> CompletableFuture.allOf( CompletableFuture.runAsync(() -> database.save(result)), CompletableFuture.runAsync(() -> emailService.sendConfirmation(order)) ))) .toArray(CompletableFuture[]::new); CompletableFuture.allOf(futures).join();
AI reviews every PR for performance issues before merge
Creates realistic traffic patterns from production data
Suggests and implements performance improvements
Optimizes JVM, database, and container settings
"Reduced P99 latency by 73% across 200+ microservices using AI-driven optimization"