Compute

Run intelligence on compute you control.

Owned GPUs, cloud GPUs or your own API keys. We design the environment, size it to the model, and run it inside your VPC or on our fleet. The aim is simple: lower operating cost and no lock-in.

Three ways to run

Not one shared cloud.

Pick where the intelligence runs, and change your mind as the workload changes. The models and datasets move with you.

Owned GPUs

Hardware you own.

Dedicated GPU servers on-premise or colocated. The best unit economics at steady load, and the only option for some data regimes. We specify, install and operate.

Steady, sensitive workloads
Most flexible
Cloud GPUs

Capacity by the hour.

Rented GPUs on your cloud account or ours. Scale up for a training run, scale down after. Pay for hours, not hardware, and keep the option to move.

Training runs · bursty inference
BYOK

Your keys, your cloud.

Bring your own model keys and cloud. The fastest start: nothing to provision, nothing leaves your walls, and you keep every existing contract.

Fastest start · existing contracts
Sized to the model

Right-sized, not over-provisioned.

Operating cost is decided when the model is chosen. A tuned 7B adapter on one mid-range GPU often replaces a frontier API call at a fraction of the cost per token.

0.8B to 4B
SLMs on CPU or edge devices
7B
One 24 GB GPU
32B + LoRA
One 80 GB GPU
Frontier API
Only where it earns its cost
What you hold

Models and datasets are yours to keep.

Compute is where it runs. These are what it runs, and both are files you can hold, inspect and move.

Published
Models

Open-weight domain models

LoRA adapters on open base models, published on Hugging Face, proven on our own live deployments.

  • B2B Marketing v2 (Qwen3-32B) · 86% head-to-head vs base
  • CFO Brain v1 (Qwen3-32B) · 75% head-to-head vs base
  • B2B Marketing 7B for single-GPU serving
  • Apache-2.0 · zero-leak verified
Yours to run anywhere
See the models
Datasets

Curated training and evaluation sets

Designed scenario matrices, hand-reviewed exemplars, verifier gates and held-out evaluation prompts.

  • Training pairs generated against real scenarios, not scraped
  • Held-out eval sets published alongside each model
  • Golden datasets for vertical solutions, annotated by your experts
  • Scrubbed before training, verified clean after
The moat
How we build them
The platform underneath

Compute is layer one of eight.

Memory, data, governance, models, resilience, orchestration and human oversight sit on top. Adopt a layer at a time or run the whole stack in your VPC. See the platform →

  • Isolated instance per client, scoped credentials per tool
  • HTTPS only, admin access over identity-gated tunnels
  • Human approval on high-stakes actions, full audit logs
  • Client data never used to train public models
Intelligence · 05–08
08Human oversightHITL
07OrchestrationMulti-agent · DAG
06ResilienceFailover · SLA
05Models & skillsRegistry
Foundation · 01–04
04GovernancePolicy · Audit
03DataETL · Sync
02MemoryVector · Context
01ComputeOwned · Shared · BYOK

Get a compute plan, not a quote.

Bring the workload. We will size the model and the hardware together and show you the cost per task. No pitch deck. Just a technical conversation.