Our platform leverages machine learning to automatically optimize cloud infrastructure, reducing costs by up to 65% while improving performance and reliability.
AI algorithms continuously analyze usage patterns to rightsize resources and eliminate waste.
Self-healing infrastructure that detects and mitigates threats in real-time without human intervention.
Anticipate traffic spikes before they happen and scale resources proactively to maintain performance.
Modern infrastructure and deployment pipelines
Zero-downtime deployments with GitOps workflow
Reproducible, version-controlled environments
resource "aws_eks_cluster" "ai_optimizer" { name = "intellicloud-prod" role_arn = aws_iam_role.eks.arn version = "1.24" vpc_config { subnet_ids = [ aws_subnet.private_1a.id, aws_subnet.private_1b.id ] } scaling_config { desired_size = 3 max_size = 10 min_size = 3 } }
Led migration of legacy systems to Kubernetes, reducing infrastructure costs by 40% and improving deployment frequency by 300%.
Designed and implemented cloud-native solutions for enterprise clients, managing multi-cloud environments with infrastructure-as-code.
Managed on-premise infrastructure and began transition to cloud services, implementing automation for system provisioning.
Specialized in distributed systems and cloud architecture. Thesis on "Optimizing Resource Allocation in Elastic Cloud Environments".
Let's discuss how IntelliCloud can optimize your cloud spend while improving performance and reliability.