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Case Study

Generalist

An AI-powered personal intelligence platform delivered as a native desktop application. Generalist brings sophisticated AI capabilities to individual knowledge work while prioritising privacy and local-first operation.

Type
Desktop Application
Focus
Personal Intelligence
Architecture
Local-first, Privacy-focused
Role
Full product development
Engagement Model
Our Product
Generalist reading list

The Challenge

Most AI tools today are cloud-dependent, requiring you to send your data—your notes, your documents, your thoughts—to someone else's servers. For many knowledge workers, this is a non-starter: the information they work with is sensitive, proprietary, or simply personal.

The challenge was to build an AI tool that could provide genuine intelligence augmentation while keeping data under the user's control. Not a dumbed-down local model, but a properly capable system that respects privacy by design.

The Solution

Generalist is a native desktop application that brings AI capabilities directly to your machine. It integrates with your local files and workflows, providing intelligent assistance without requiring you to upload your data to external services.

The desktop-native approach means responsive performance—no waiting for cloud round-trips—and seamless integration with the tools you already use. It's AI that fits into your workflow rather than requiring you to fit into its web interface.

Local-First Architecture

Your data stays on your machine. Intelligence without surveillance.

Native Desktop Experience

Responsive, integrated, and available offline. No browser tabs, no web latency.

File System Integration

Works directly with your documents, notes, and projects where they live.

Personal Knowledge Base

Build and query your own intelligence layer across all your information.

Technical Approach

Generalist required solving the tension between capability and privacy. We needed AI features that could compete with cloud services while respecting the constraint that user data shouldn't leave their machine unnecessarily.

The architecture uses a combination of local processing and carefully scoped external calls, always under user control. The result is an application that feels powerful and responsive while maintaining the privacy guarantees that make it trustworthy for sensitive work.

The Outcome

Generalist proves that AI tools don't have to be cloud-dependent panopticons. Users can have sophisticated intelligence augmentation while maintaining control over their data—privacy and capability aren't mutually exclusive.

The desktop-native approach also opens possibilities that web apps can't match: deep system integration, offline availability, and performance that doesn't depend on network conditions. It's AI that works where you work.

Application Screenshots

Inside Generalist

Interested in privacy-first AI?

Let's discuss how we can build AI tools that respect your data.

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