Does Your AI Tool Read Your Conversations?

Sep 2

The kitchen table holds a half-finished math worksheet, a mug of coffee gone lukewarm, and a tablet propped against a stack of mail. A teenager taps the screen: "Explain photosynthesis."

The answer arrives in seconds, in a calm and familiar voice. It feels effortless. But behind that smooth interface, each question travels across the internet, is processed on a machine in a distant data center, and comes back as a reply.

We rarely think about how it works, as long as it works.

Where does your conversation actually go?

Privacy at home usually gets framed as a policy choice, but it really comes down to plumbing.

A cloud AI service keeps your conversations on its servers, not in your home. That arrangement buys convenience, speed, and free tiers, but it also means the retention period, training defaults, and access rules are set by someone else.

When those settings change, or when a new regulation changes how long chats are kept, your household has to react rather than decide.

With most cloud assistants, what you type leaves your home immediately, travels to a provider's data center, and is processed and stored there. Companies typically use that input for three quiet purposes:

Keeping the service running and secure
Improving their models, often on by default unless you opt out
Occasionally sending samples to human reviewers for quality and safety checks

None of this means someone is actively listening. It is simply how cloud services work at scale.

What changes when you run AI on your own network?

Moving AI onto your own network changes the plumbing without changing how you use it.

A local server sits under the desk, connected to the same router as the television. When you run a model there, your question never leaves the house. The hardware reads the text, keeps the context, and writes the reply right there.

There is no trip to a data center and no outside company setting the rules for your conversation. You still get capable models that understand context, but the privacy boundary is physical rather than contractual.

Does everything need to run locally?

No. Not every task belongs behind your firewall.

Live weather, map directions, and software updates still need a connection to outside services. The practical split looks like this: keep personal notes, drafts, photo libraries, and family calendars on your local machine, and send only public, non-sensitive tasks to outside providers.

You choose which apps can reach the internet and which stay entirely within your home network. Setting up that private zone once means you don't have to re-check privacy settings every time a cloud service changes its terms.

Setup guides and architecture diagrams are in the Companion documentation.

What hardware do you need to run AI models locally?

It depends on the size of the models you want to run. The deciding factor is memory: VRAM on a graphics card, or unified memory on machines that share it between processor and graphics, such as Apple Silicon Macs and AMD Ryzen AI systems.

Hardware	What it handles
8–12GB graphics card	7B–8B parameter models comfortably
16–32GB graphics card or unified memory	13B–32B parameter models without constant swapping
64–128GB unified memory	70B-class models on a single device
32GB system RAM	A practical baseline for longer conversations and supporting apps
Modern multi-core processor	Runs the apps and data processing around the model
Fast NVMe storage	Loads model files quickly; plan for 50–200GB of space

A private AI server for the whole family

Companion Core is built to be that box under the desk. It arrives ready to run, with an AMD Ryzen AI Max+ 395 and up to 128GB of unified memory, enough for 70B-class models on the device itself. Every Companion Hub app and local AI model runs at home, so homework questions, family photos, and personal notes stay on hardware you own.

Companion Core is on Kickstarter now, at the lowest price it will ever have: