Private AI on my own hardware
My own on-premise AI server runs local language models so client data stays in-house. It powers AI in dashboards, writing assistants, automation and image work.
My own on-premise AI server runs local language models so client data stays in-house. It powers AI in dashboards, writing assistants, automation and image work.
AI is genuinely useful in logistics. It can draft, summarise, check, and spot what doesn't look right. But most AI tools send your data to someone else's servers to do it.
Customer lists, rates, manifests and emails are not things to hand to a public service and hope for the best. For a lot of businesses, that's where the AI conversation stops.
The usual choice is between ignoring AI or taking that risk. I didn't want either, for myself or for my clients.
My own on-premise AI server.
On top of it I run:
I use AI in how I deliver, too. On a warehouse KPI dashboard I run, requests are made on the dashboard itself, with files attached, and become builds. It's AI-assisted delivery, with me deciding what goes live.
The server runs in-house. The models are local. Work done for clients stays inside, from the first question to the finished answer.
For a logistics business, that changes the conversation. AI can be put to work on real data, in the dashboards and inboxes people already use, without that data being handed to anyone else.
When a client asks where their data goes, the answer is short: to my own hardware, and no further. It stays on hardware I own and look after.
And because the models run locally, I choose which ones to use and when to change them. The AI fits the job, not the other way round.
The same hardware makes long-form AI film from start to finish: 20-minute lip-synced AI episodes, AI music and AI video.
If you're living with a problem like this one, tell me about it.
Answers are written by AI and may be wrong - for anything important, use the contact page.