What We Build
AI-Driven Autoscaling Engine
A predictive autoscaling platform that uses ML models trained on your workload patterns to scale resources before demand arrives. Supports Kubernetes HPA/VPA, serverless functions, and bare-metal pools. Reduces over-provisioning by up to 40 percent while maintaining P99 latency targets.
Intelligent Workload Orchestration
A scheduling layer that places containers, VMs, and GPU jobs across multi-cloud and hybrid environments based on cost, latency, compliance, and carbon footprint. Integrates with AWS, GCP, Azure, and on-prem clusters through a unified control plane.
Self-Healing Infrastructure
Automated incident detection and remediation powered by anomaly-detection models that learn your system's normal behavior. Detects drift, remediates misconfigurations, and rolls back failed deployments without human intervention — with full audit trails.
Infrastructure-as-Code AI Copilot
An AI assistant that generates, reviews, and refactors Terraform, Pulumi, and CloudFormation templates. Catches security misconfigurations, suggests cost optimizations, and enforces organizational policies at the pull-request level.
Cloud Cost Intelligence
Real-time cost observability dashboards with ML-powered anomaly detection and forecasting. Attributes spend to teams, services, and features with granular tagging. Surfaces actionable recommendations that have saved clients an average of $1.2M annually.
Multi-Cloud Networking Fabric
A software-defined networking layer that connects services across clouds, regions, and on-prem data centers with zero-trust security. Supports service mesh integration, automatic mTLS, and intelligent traffic routing with latency-aware load balancing.