Local AI Deployment
AI that runs on the hardware you already have — sized correctly, installed properly, and kept running.
The problem
Most local AI deployments stall on the same thing: nobody sized the hardware correctly, so the install either falls over or the model quietly underperforms.
The outcome
A local AI deployment sized to your hardware, installed and verified in your environment, with a clear path if you outgrow it.
- Built a confidence-gated OCR pipeline with a human review queue and a full audit trail for an insurance document workflow, running on-prem.
- The same hardware guidance behind our own products, not vague minimums.
What's included.
- Hardware sizing: laptop → workstation → on-prem GPU.
- Model and quantisation selection for the hardware you actually have.
- Installation and verification in your environment.
- Guidance on scaling up if you outgrow the initial configuration.
How this actually runs.
Assess
Review your hardware, data sensitivity, and where AI needs to run.
Size & select
Match a model and quantisation to the hardware you have — or recommend what to add.
Install & verify
Get it running in your environment and confirm it performs as expected on your own tasks.
Handover & advise
Hand over a working deployment, with ongoing advisory available if you need it.
Built on.
- GGUF / llama.cpp
- Quantisation (Q4_K_M and others)
- Local GPU & Apple Silicon inference
Proof this isn't theoretical.
Orchestrator Studio
Early accessConfiguration-driven workflow platform for document-heavy and operations-heavy teams.
Questions worth asking.
No — hardware sizing is the first step of this engagement, not a prerequisite. We recommend running a 27B LLM, which runs comfortably on a machine with 48 GB of VRAM; smaller models also work, with some compromise on speed and accuracy. Bring what you have and we’ll work it out with you.
It includes picking a model and quantisation that fits your hardware. Fine-tuning a model to your own data and building the evaluation harness that proves it works is LLM Engineering & Evaluation — a separate, deeper engagement.
We size for headroom where we can, and the same engagement can help you scale up — from a laptop to a workstation to an on-prem GPU — when you do.
Ready to talk about local ai deployment?
AI that runs on the hardware you already have — sized correctly, installed properly, and kept running.