Your own models.On your own hardware.
Some work cannot leave your building, some runs a million times a day and costs too much on a frontier model, and some just needs a small model that does one thing well. We deploy open-weight models where your data lives and tune them for the job.
A practical 30 minutes: what fits, what to do first, and who does what.
When a local model is the right call
Frontier models are the right default for most work. They are the wrong one when the data is regulated, the volume is huge, or the task is a narrow decision you make the same way every time.
Most teams that ask about local AI have one of three problems. Their data cannot go to a third-party API. Their usage bill grew faster than the value. Or they need a fast, cheap answer to the same kind of question thousands of times: route this ticket, score this lead, flag this document.
Each has a different answer. Regulated data calls for an open-weight model inside your own network. Volume calls for a smaller model on the high-frequency path, with a frontier model kept for the hard cases. Narrow decisions call for a small model tuned on your own labelled examples, which often beats a general model at a fraction of the cost.
We measure before we recommend. Every deployment starts with an eval set built from your real work, so you see how the local model scores against the hosted one before you commit to running it.
What we build
Private model deployment
Open-weight models such as Llama, Qwen, Gemma and gpt-oss, running on your servers, workstations or private cloud, behind your own auth.
Task-tuned small models
A small model trained on your labelled examples for one decision: classification, routing, extraction or scoring. Fast, cheap and easy to audit.
Local and hosted routing
Routine calls go to the local model, hard ones go to Claude or GPT. One interface for your team, and a bill that tracks the value.
Eval sets and benchmarks
A test set drawn from your own work, scored across local and hosted models, so the choice rests on numbers rather than a vendor pitch.
Data boundary and governance
Where data flows, what is logged, who can call which model. Written down, so security and compliance can sign off.
Wired into your tools
Local models exposed over MCP and standard APIs, so they plug into Claude, your agents and your internal apps like any other tool.
Not sure a local model beats what you pay for now?
Leave an email and tell us the task. We will send back which model we would test, where it would run, and how we would measure it against your current setup.
Who this is for
It fits when a hosted model is blocked by data, cost or speed, not just when local sounds interesting.
How it runs
Scope
The task, the data boundary, the volume and the hardware you have. We agree what success means in numbers.
Evaluate
An eval set from your real work, scored across candidate local models and the hosted baseline.
Deploy
The winning model deployed in your environment, tuned if needed, wired into your tools, with monitoring.
Train
Your team learns to run it, update it and re-score it when a better model ships.
Who does what
- Build the eval set and run the benchmarks
- Select, tune and deploy the model
- Wire it into your tools and agents
- Document how to run and update it
- Provide the hardware or cloud account
- Share example data and label a sample with us
- Name an owner for the model after handoff
- Set the data and security rules we build to
Frequently Asked QuestionsWhat teams ask us before they start.
Is a local model as good as Claude or GPT?
What hardware do we need?
Can you fine-tune a model on our data?
Do we have to stop using Claude or ChatGPT?
What does it cost?
Talk to us about running your own models
Thirty minutes. Tell us the task and what is blocking a hosted model. We will tell you honestly whether local is worth it, or whether the model you already pay for is the better answer.