The infrastructure layer required to run enterprise AI reliably, privately, and at production scale.
Constraints we design around, not around later.
The operational layer beneath every model and agent.
A single controlled entry point that routes requests across model providers, private and public.
Sized, monitored compute for private or hybrid model serving.
Kubernetes-based deployment for model services and agents.
Reduce redundant inference cost without sacrificing observability.
Credentials and keys managed as infrastructure, not embedded in code.
Structured storage for vectors, logs, and model artifacts.
Backup and failover planning before you need it, not after.
Metrics, logs, and traces across every model and agent call.
No — we design, deploy, and operate infrastructure across your cloud, private cloud, or on-premise hardware, including Dropp Tempo's infrastructure operations where relevant.
Yes — model gateways and inference services are designed to integrate with what you already run, not replace it wholesale.
Either your team with full documentation, or Cortex through Managed AI Operations.