Insights / Private AI
Why your client files should never go to ChatGPT:
what to run instead.
A client file is not spare material for an AI experiment. Before you paste it into a chatbot, know where it will go and who will handle it. If the file needs to stay in the building, I can bring the AI to it.
An upload is
a handover.
A cloud AI tool processes your prompts on a provider's systems. Uploading a document means sending it outside your own equipment. The service, terms and settings determine how it is handled after that.
For a business working with sensitive records, that is a decision to make deliberately. A convenient drafting box does not tell you whether the workflow fits the way you need to handle client information.
I start with the work and the data boundary. Which documents does the task need? Who should have access? Can any of that information leave the office? Those questions come before choosing a model.
Training is only
part of the question.
A setting that restricts training use does not move cloud processing into your office. The file still has to reach the provider for the tool to work on it.
Check where the information is processed, whether it is retained, who can access it and what any connected tools receive. The answers depend on the specific service and setup. Don't treat every cloud product as identical.
If your requirement is that the material stays on your premises, a fully local workflow addresses that directly. There is no need to send the file to an AI provider in the first place.
Bring the AI
to the files.
With private, on-premises AI, the model runs on a computer or server in your office. Local document search can find relevant passages for it to work with. The prompts, source material and responses stay within that setup.
That can help with everyday work:
- Draft standard letters from your templates and selected details.
- Summarise a long document into points you can check.
- Find passages in policies, records and internal guidance.
- Transcribe recordings with a local transcription tool.
These are jobs to test against your own needs. A local model is not automatically equivalent to a cloud service, and either can make mistakes. Check names, facts and source material before using the output.
Your files stay local.
Your judgement stays involved.
AI can produce a convincing wrong answer. Keeping processing in-house changes where the work happens. It does not remove the need to review it.
Private needs
looking after.
The whole workflow matters. A local model connected to an outside search, logging or document service can still send information out. I check the connections and make the boundary explicit.
Access also matters inside the office. Staff should only be able to retrieve documents they are allowed to see. Devices, backups, updates and the local network need care alongside the AI.
I assess the hardware against the task and the people using it, set up the software and document access, and train the team. A fully local workflow can operate offline once the necessary software and models are in place.
Start with a task.
Keep the file to yourself.
Tell me the kind of document you work with and the repetitive job you want help with. Leave client names and confidential details out of the enquiry.
I have spent 20+ years working inside small businesses, and I run local AI daily. I'll help you work out whether a local setup fits the work, what it needs and where its limits are.