Not every workload needs a data center. Not every workload fits on a laptop either.

We figure out the awkward middle — three layers, run as one system: compute, intelligence and connectivity.

01 — Compute

GPU infrastructure, sized to the workload you described, not the tier we had spare.

Dedicated or burstable GPU allocations, provisioned for training, fine-tuning or inference. You tell us what you're running; we work out the shape of the hardware underneath it.

  • Dedicated and shared GPU tiers
  • Burstable capacity for spiky inference load
  • Monitoring on utilization, temperature and throughput
GPU CORESheatmap (illustrative)
02 — Intelligence

Your model can scale. Your infrastructure should probably get the memo.

Model deployment and inference, wired directly into the GPU layer underneath it — no separate vendor to reconcile when load changes shape overnight.

  • Managed inference endpoints
  • Autoscaling against real request volume
  • Works with open-weight and hosted models
NEURAL NETstructural, not decorative
03 — Connectivity

SIP integration. Because eventually your AI has to talk to somebody.

Voice trunking and routing that sits directly against the compute and model layer, so a voice agent isn't bolted on as an afterthought — it's part of the same system from the start.

  • SIP trunking and number provisioning
  • Routing built around call quality, not just cost
  • Direct path from voice traffic into inference
PACKET STREAMSIP / voice
How it fits together

One system. Three layers. No handoffs between vendors.

Compute, intelligence and connectivity, run against the same monitoring, the same account, and the same team.

See what that looks like for you →