Service lane
Local & private models
Some data can't leave the building, and shouldn't. Local models run on infrastructure you control: your own servers or private cloud, with nothing sent to a third-party API. Capable AI where the data stays put. Private by construction, not by policy.
How it works
01
Your data stays with you
The model runs on your hardware or in your private cloud.
02
We pick and tune the model
Size and power matched to the job, without paying for excess.
03
Zero egress, full control
No request ever leaves your infrastructure.
01
What it is
A local model runs on infrastructure you own or control, on-premise hardware or a private cloud tenancy, instead of a hosted third-party API. Open-weight models have closed much of the gap with the big closed ones. For many tasks you can keep the data in-house and still get strong results. Nothing leaves your boundary: no prompts, no documents, no embeddings.
02
What it's good for
A few shapes this takes in practice:
- Sensitive records stay in-house. The model reads and reasons over them without a copy ever leaving your network.
- Compliance gets simpler when there's no third party in the data path to vet, contract, and audit.
- Costs stay predictable at volume. You run on capacity you already pay for instead of per-token metering.
- It keeps working where the internet doesn't: an isolated network, a factory floor, a site with no reliable link out.
03
What's possible
Open-weight models selected and sized to your task and hardware. Deployment on-premise or in a private cloud tenancy you control. Fine-tuning or adaptation on your own data, kept in-house. The same building blocks as the rest (retrieval, agents, integrations) running fully private. And a hybrid split where the sensitive work stays local while less critical calls can still use a hosted model, if you decide so.
04
What you gain
- Data that never leaves. The strongest privacy guarantee there is, because there's no egress to trust.
- Control over the whole stack: the model, the data, the deployment, the update schedule.
- No lock-in to one vendor's API, pricing, or roadmap. You own what you run.
- Predictable economics at scale: fixed capacity instead of a bill that grows with every call.
05
Who it's for
Organisations handling data that regulation or contracts won't let off-site: health, legal, finance, public sector, anything under strict confidentiality. Teams with hard data-sovereignty or residency requirements. High-volume workloads where per-token pricing stops making sense. Environments that have to work air-gapped or offline. If “where does the data go” has a hard answer, this lane honours it.
06
How we build it
We start from your constraints: what data is in scope, where it's allowed to live, what hardware you have or can get. Then we pick open-weight models sized to the task, deploy them inside your boundary, and adapt them on your data if it helps. Retrieval, agents, and integrations layer on top, all staying private. Where a hybrid split makes sense, you decide exactly what may ever leave. The same people build it and run it, so the private deployment stays maintained instead of stranded.
07
Examples
Illustrative, not case studies. Every deployment is scoped to your own data boundary:
- An in-house assistant over confidential records that answers staff questions without a single document leaving the network.
- A private deployment for a regulated team, so AI can touch sensitive data while staying inside the compliance boundary.
- An offline setup for a site with no reliable connection, where the model runs locally and keeps working regardless of the link.
Want this wired into your stack?
OTW builds and runs it end to end. Join the waitlist for early access, or write to us and we'll work out what to wire first.