Custom AI Deployment

Some data should never leave your building. I deploy private and local models, build the tooling around them, and keep you off a single vendor's roadmap.

Preston Vawdrey, AI deployment consultant, standing in front of streams of code with a server rack, padlock and neural node cluster behind him

Custom AI Deployment Services I Offer

Private & Local Models

Models running on hardware you control, for customer records, health information and anything else you cannot paste into a public chat window. I run this setup myself for exactly that reason.

Custom Tooling

Connectors that give a model safe, scoped access to your own systems, so it works with your data instead of guessing at it.

Data Handling

Clear rules about what goes where, what gets retained and what never leaves. Written down, so your team can follow it without asking every time.

Cost & Vendor Control

An architecture that lets you move between providers, and a realistic view of what each option costs once you are past the trial tier.

How a Deployment Project Works

01

Requirements & Constraints

We establish what the model needs to do and, more importantly, what your data is not allowed to do.

02

Choose the Architecture

Local, private cloud or hosted API, chosen against your actual sensitivity and budget rather than a default.

03

Build & Secure

The deployment goes up with scoped access, logging and a tested fallback for when something is unavailable.

04

Document & Hand Over

Your team gets the runbook and the training, so this is infrastructure you own rather than a dependency on me.

Custom AI Deployment FAQs

Why run a model locally instead of using ChatGPT or Claude?
Mostly because of data. If you handle customer records, health information or anything under a confidentiality agreement, a local model keeps that on hardware you control. Cost predictability at volume is the second reason.
Is a local model as good as a frontier model?
No, and I will not pretend otherwise. Local models are meaningfully behind the best hosted models on hard reasoning. For classification, extraction, summarising and drafting against your own data, they are often more than good enough.
What hardware does this need?
Less than most people expect. A well-specified Mac or a single GPU workstation handles a lot of real business work. I will size it against your actual use rather than the largest model available.
What happens if I want to move to a different provider later?
That is what the architecture is for. Keeping the prompt layer and the data layer separate from any one vendor's API means switching is a configuration change rather than a rebuild.

Ready to Run AI on Your Own Terms?

Let's talk about what your data allows, what you are trying to automate, and what that actually costs.

Interested in: Custom AI Deployment

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