How to Use AI for Personal Notes Without Sending Them to the Cloud

You paste a meeting reflection, a health-related thought or a rough plan for a difficult conversation into your notes app. Then you ask AI to summarise it, find related ideas or turn it into tasks. The answer is useful, but an important question remains: which parts of that process left your computer?

Using AI for personal notes without sending them to the cloud is possible, but it is not a single switch labelled “private”. Your notes, search index, AI prompts, model downloads, backups and diagnostic data can each follow different routes. Understanding those routes helps you make sensible choices without giving up useful tools.

Start by mapping your note-taking workflow

An AI note workflow usually has five separate parts:

1. Note storage: the text, attachments and metadata saved by your notes app.
2. Search index: data created to make ordinary keyword search fast.
3. AI index or embeddings: numerical representations of note content used for semantic search and retrieval.
4. The model request: the prompt, relevant note excerpts and instructions sent to an AI model.
5. Backups and synchronisation: copies of notes or app data stored elsewhere.

A workflow is only fully local if all five remain on your device or within systems you control.

For example, an app may store your notes in local files but use a cloud AI provider whenever you ask a question. Another may run the model locally but save its database to a synced folder. Neither option is necessarily wrong, but they offer different privacy boundaries.

What “local” means for notes and AI

Local-first generally means that your primary data is stored on your own computer rather than on a company server. It does not automatically mean that every feature works offline.

Check the following separately:

Part of the workflow	Local option	What to verify
Notes	Files or a local database on your computer	Where the app stores its data
Attachments	Local folders or app storage	Whether files are uploaded for processing
Keyword search	Local search index	Whether search requests leave the device
Semantic search	Local embeddings database	Whether embeddings are generated remotely
AI answers	Local language model	Whether prompts are sent to an API
Backups	Encrypted drive or your own backup system	Whether cloud backup is enabled

The distinction between a note and an embedding is especially important. An embedding is not normally readable like the original note, but it is still derived from your content. Treat it as sensitive data. If an app sends embeddings to a remote service, it is still sharing information about your notes.

Choose between local models and cloud APIs

A local language model runs on your computer. You download the model files, and the app sends prompts to a model server running locally. Tools such as Ollama are commonly used to run compatible models on a desktop machine.

With a local model, your note excerpts and questions do not need to be sent to an AI provider. This is the strongest option when you write about personal, professional or confidential subjects.

The trade-off is practical rather than ideological. Local models can require significant disk space and memory. Their quality and speed depend on your hardware and the model you choose. They may be less capable than leading cloud models for complex writing, nuanced reasoning or long documents.

Cloud APIs work differently. When you ask a question, the app sends a request to a provider such as OpenAI, Anthropic, Google or a service that routes requests to multiple models. That request may include your question, system instructions and selected excerpts from your notes.

Cloud models can be convenient and capable, but you should assume that any text included in the request leaves your computer. Read the provider’s current data handling and retention terms before using it for sensitive notes. Terms can differ by account type, region and product.

A useful approach is to use both deliberately:

• Use a local model for daily note search, planning and private reflections.
• Use a cloud model only for material you are comfortable sharing, or after removing identifying details.
• Keep cloud AI as an explicit choice rather than the default for every question.

Check what is actually included in a prompt

The prompt is often larger than the question you type.

If you ask, “What have I said about changing jobs?”, an AI notes app may send:

• Your question
• Instructions telling the model how to answer
• Several relevant note excerpts
• Note titles, dates or tags
• Conversation history from earlier questions

This is useful because the model needs context. It also means that a short question can expose more information than expected.

Before enabling a cloud provider, look for settings or documentation covering:

• Whether the app sends full notes or only selected excerpts
• How many notes can be included as context
• Whether titles, tags and dates are sent
• Whether chat history is retained locally or remotely
• Whether you can review or clear AI conversations
• Whether a question can trigger web search or other external tools

If the app does not make this clear, treat the workflow as uncertain. Privacy depends on what is transmitted in practice, not on a broad claim that an app is “secure”.

Keep semantic search local too

Semantic search finds notes by meaning rather than exact words. A search for “feeling stuck at work” may find a note titled “Monday thoughts”, even if it does not contain the word “stuck”.

To do this, software creates embeddings for your notes and often stores them in a vector database. There are two privacy decisions here:

1. Where embeddings are created
2. Where embeddings are stored

For a local workflow, both should happen on your computer. A local embedding model processes the note text locally, and the resulting index remains in local app data.

If embedding generation uses a cloud API, your note content must be sent to that API. If the embeddings are then stored in a hosted database, another copy of derived data exists outside your computer.

The same principle applies to Fundus or any other AI notes app: choosing a local model matters, but you should also check how its search and retrieval features handle note content.

Review the settings that matter most

Privacy settings can be buried behind friendly labels. Focus on settings that affect data movement rather than cosmetic preferences.

A practical privacy checklist

• Confirm where notes and attachments are stored.
• Disable cloud synchronisation if you do not want copies outside your computer.
• Choose a local model endpoint for AI features.
• Choose local embeddings if semantic search is available.
• Turn off automatic web search, if the app offers it.
• Review whether usage analytics or crash reports include note content, prompts or file paths.
• Check whether AI chat history is saved and where it is saved.
• Delete test conversations after checking what context the app retains.
• Encrypt your computer’s disk and use a strong login password.
• Protect backups with encryption and review where they are stored.

Disk encryption is worth highlighting. A local-only app cannot protect your notes if someone can access an unlocked computer, copy its files or read an unencrypted drive. Local privacy includes physical access and account security.

Be careful with synchronised folders and backups

You may keep notes in a folder that is automatically synchronised by a cloud storage service. This can be a good backup strategy, but it means your notes are no longer only on your computer.

The same can happen without obvious synchronisation. Operating system backups, phone backups, shared desktop folders and browser-based clipboard tools can create copies. If privacy is your priority, identify every backup destination.

You do not need to avoid backups. Losing a private notebook because there was no backup is not a privacy win. Instead, use encrypted backups that you understand and control. Test that you can restore them.

Use a simple rule for sensitive content

Before asking AI about a note, ask one question: would you be comfortable with this exact context being processed by an external provider?

If the answer is no, use a local model or do not include the material. Remove names, addresses, employer details, medical information and unique events if you need cloud assistance with a general writing task.

Local AI is not automatically safer in every respect. You still need to trust the app installed on your computer, keep it updated and download models from reputable sources. But when note storage, indexes and model prompts remain local, you substantially reduce the number of organisations that can receive your personal thoughts.

The goal is not perfect secrecy through complicated settings. It is a workflow you can explain clearly: your notes live here, your search index lives here, your model runs here, and your backups go there.