Autonomous Data Tiering & Placement

We implement AI-driven tiering systems that move data between AFA, NAS, SAN, object storage, and cloud based on performance, cost, and policy constraints. Cognitive agents built with reinforcement learning make placement decisions in real time without human intervention.

Predictive Capacity & Performance Management

Time-series and deep learning models forecast storage consumption, IOPS demand, and throughput requirements. This enables proactive provisioning and workload balancing to avoid service degradation and unplanned capacity purchases.

Self-Healing Data Infrastructure

Our systems detect disk failures, corruption risks, and replication issues before they cause outages. Automated remediation routines trigger corrective actions such as rebalancing, re-replication, or failover without disrupting workloads.

Data Lifecycle Automation

We deploy policy-driven engines that automate retention, archiving, deletion, and compliance workflows. These systems integrate with ILM (Information Lifecycle Management) frameworks to ensure data governance while reducing storage costs.

Multi-Cloud Data Fabric Integration

We create unified data layers that span AWS, Azure, GCP, and on-premises systems. Using cognitive orchestration, data is migrated, replicated, or cached intelligently to optimize latency, cost, and resilience.

Cross-Domain Observability & Data Analytics

Using storage-aware observability stacks, we collect and analyze metrics on latency, throughput, error rates, and capacity trends. Cognitive analytics engines correlate these metrics with application performance for root cause identification.