Cloud foundations built for AI and telemetry workloads.
Architecture, migration, and integration across Azure, AWS, and GCP — tuned for the streaming data, GPU workloads, and hybrid edge topologies that AI-driven operations demand.
Why this matters now
Generic cloud setups buckle under operational AI: telemetry floods storage budgets, GPU capacity gets provisioned wrong, and edge sites need architectures the reference diagrams never covered.
Service pillars
Landing zones for AI
Secure, cost-governed foundations with the data and GPU services AI workloads require.
Hybrid & edge topology
Consistent deployment across cloud, on-prem, and edge sites with unified observability.
Integration fabric
APIs, events, and identity connecting cloud workloads to your enterprise systems.
What changes for your operation
- Costs that scale sub-linearly — Tiering and lifecycle design keep telemetry storage sane.
- Deploy anywhere — One pipeline ships to cloud and edge alike.
- Security posture built in — Zero-trust patterns, private networking, compliance mapping.
Stack we deploy with
- Azure / AWS / GCP
- Kubernetes
- Terraform
- Event Grid / Kafka
- Entra ID / IAM
Where this service earns its keep
- AI landing zones
- IoT platform migration
- Hybrid edge-cloud architecture
- FinOps for telemetry workloads
Where we deploy it
Questions we hear most
A common starting point — we assess workload placement, exit costs, and target architecture, then execute the consolidation or make multi-cloud deliberate.
Ready to put cloud integration to work?
Book a demo, or start with the AI Readiness Assessment — a 30-minute working session that maps your highest-value first deployment.
- AI that sees, predicts, and acts — not just a dashboard.
- Pilot to fleet rollout in weeks, with a go/no-go you can defend.
- Enterprise-grade security, procurement, and support from day one.
Schedule a demo
See Invexal on your own cameras and data.