On-screen context assistant console

Run AI-assisted actions on a Mac using on-screen context, without switching apps or copying and pasting. For Mac-based developers, operators and knowledge workers, convert active app state, selected text, visible screen content, local model settings and user-defined actions into reviewed, source-linked task results. The benefit is a testable hypothesis, measured through completed tasks per operator hour and corrections after review; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect active app state, selected text, visible screen content, local model settings and user-defined actions, then follow this sequence: 1. Read active app, selected text or visible screen content. 2. Trigger the assistant with one keyboard shortcut. 3. Accept inline AI suggestions in the current app. Resolve uncertain cases with qualified reviewers, approve reviewed, source-linked task results, and measure completed tasks per operator hour and corrections after review against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Local execution by default; remote models only with the user's own key and explicit choice. Final actions and judgment calls remain with the user. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve user intent, source attribution, action accuracy and usage permissions. Users approve substantive changes and external actions. One macOS version and one approved local model backend; final actions and judgment calls remain with the user. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One macOS version and one approved local model backend; final actions and judgment calls remain with the user. Implement one approved input format, a bounded representative case set and the first two task modules: read active app, selected text or visible screen content; trigger the assistant with one keyboard shortcut. Support the third module with operator review: accept inline AI suggestions in the current app. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

User-owned Mac apps, browser tabs and local files. Cloud model providers via the user's own key, file import/export and destination apps. Start with file exchange and validate destination specifications before promising direct automation. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Context and action setup, Live task console, Review and trace log. Use a thumbnail gallery for saved actions, a large central console for the active task, and a right-hand panel for context, model and permissions. Let users compare runs side by side. Display draft, awaiting decision and completed states. Provide a trace view with each step linked to its source. Make the task-specific outcome reviewed, source-linked task results visible beside its evidence, review state and value baseline.