Build cloud-native applications 3x faster with AI-driven microservices design, automated Kubernetes optimization, and intelligent service mesh configuration for enterprise-grade scalability.
AI-powered cloud-native development uses machine learning to automatically design microservices architectures, optimize Kubernetes deployments, and manage service mesh configurations. It accelerates development by 3x while ensuring scalability, resilience, and cost efficiency.
Intelligent automation from architecture design to production deployment
Automatically decompose monoliths into optimal microservices using domain-driven design patterns. AI analyzes code dependencies and business logic to identify service boundaries.
AI-powered resource allocation, auto-scaling policies, and pod placement strategies reduce infrastructure costs by 60% while maintaining 99.99% uptime.
Automatically configure Istio/Linkerd policies for traffic management, security, and observability. AI optimizes routing rules and circuit breakers based on traffic patterns.
AI-driven threat modeling and automated security controls ensure compliance with zero-trust principles. Continuous security scanning and policy enforcement.
AI recommends optimal database patterns (SQL vs NoSQL vs Graph), caching strategies, and data partitioning schemes based on access patterns and scalability requirements.
Automatically instrument applications with distributed tracing, metrics, and logging. AI-powered anomaly detection and intelligent alerting reduce MTTR by 85%.
Payment processing company migrated 800K-line monolith to 45 microservices in 4 months using AI-driven decomposition. Achieved 3x faster feature velocity with 60% lower cloud costs.
Retail giant achieved 99.99% uptime during Black Friday with AI-optimized Kubernetes auto-scaling handling 50x traffic spike. Zero manual intervention required.
Med-tech company built HIPAA-compliant cloud-native platform in 6 weeks with AI-generated security policies and automated compliance monitoring across 12 microservices.
Manufacturing company processes 10M events/sec with AI-optimized Kafka and Kubernetes, scaling from 20 to 200 pods dynamically based on ML-predicted demand patterns.
CogniX.AI analyzes your codebase dependencies, business domains, and data flows to automatically identify optimal service boundaries. It generates migration plans, API contracts, and even scaffolds microservice code, reducing migration time from years to months.
Yes. AI continuously analyzes resource utilization patterns and automatically right-sizes pods, optimizes node allocation, and implements predictive auto-scaling. Most enterprises see 50-70% cost reduction within 90 days while improving performance.
We support AWS, Azure, Google Cloud, and multi-cloud architectures. Our AI works with managed Kubernetes (EKS, AKS, GKE) and self-hosted clusters, providing platform-agnostic optimization.
Timeline depends on complexity, but AI acceleration typically reduces migration by 70%. A typical 500K-line monolith can be decomposed into microservices in 3-6 months vs 12-24 months with manual approaches.
Our AI recommends optimal patterns (saga, 2PC, eventual consistency) based on your consistency requirements. It automatically generates distributed transaction orchestration code and implements compensation logic for failures.
Join enterprises achieving 99.99% uptime with AI-powered cloud-native development