On-screen context assistant console

For

Mac-based developers, operators and knowledge workers who run AI actions on screen context

Solves

AI help requires switching apps, copying context and pasting results, which breaks keyboard flow and spreads work across several subscriptions.

Delivers

Reviewed, source-linked task results

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Run AI-assisted actions on a Mac using on-screen context, without switching apps or copying and pasting.

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.
4. Run processing locally on the Mac by default.
5. Switch between local and remote model backends.
6. Send calls with the user's own provider key.
7. Operate without an account or telemetry.
8. Load custom actions and community extensions.
9. Execute multi-step tasks across apps or tabs.
10. Move the cursor, click, fill forms and handle files.
11. Coordinate work across multiple browser tabs.
12. Keep per-task tab sets, history and background runs.
13. Save completed flows as reusable skills.
14. Pause for user decisions before continuing.
15. Hand off login to the user and resume afterward.
16. Record each step in an action log and trace.
17. Interrupt instantly when the mouse moves.
18. Adapt output style from a short user description.

Everything these tools do, in one app

• On-screen context awareness Reads the active app, selected text, or visible screen content so you don't have to re-explain what you're doing.Found in Cai, Friendware, Tine
• Single-keystroke invocation Triggers the assistant with one keyboard shortcut or key press instead of switching to a separate chat window.Found in Cai, Friendware
• Inline text completion Accepts AI suggestions directly in the current app without leaving your keyboard flow.Found in Friendware
• Local on-device execution Runs processing on your Mac so data stays on-device unless you choose otherwise.Found in Cai, Tine
• Multiple model backends Lets you plug in different local or remote AI models to balance speed, quality, and setup effort.Found in Cai
• Bring-your-own-key Sends LLM calls directly to your chosen provider using your own API key.Found in Nimbus
• No account or telemetry Works without signing up and without sending usage data to a cloud service.Found in Cai
• Custom actions and extensions Lets you script your own workflows or install community extensions to extend what the tool can do.Found in Cai
• Multi-step task execution Performs a sequence of actions across apps or tabs to complete a task from one instruction.Found in Nimbus, Tine
• Cursor or click automation Moves the cursor or performs clicks, form fills, and file handling on your behalf.Found in Nimbus, Tine
• Multi-tab coordination Coordinates work across multiple browser tabs within a single task.Found in Nimbus
• Session management Gives each task its own tab set and history, and can run in the background.Found in Nimbus
• Reusable skills or flows Captures a completed flow so you can reuse it for similar tasks later.Found in Nimbus
• Pauses for user decisions Stops and asks you to make judgment calls before continuing automation.Found in Nimbus
• Auth handoff Lets you log in manually and then resumes the agent afterward.Found in Nimbus
• Action log and traces Records each step taken so you can see what the tool did.Found in Nimbus, Tine
• Instant interrupt Stops the AI immediately when you move the mouse, putting control back in your hands.Found in Tine
• Personalization of style Adapts outputs to how you write based on a short description of yourself and your style.Found in Friendware

What goes in, what comes out

The workflow

1. In
   Start with

   Active app state, selected text, visible screen content, local model settings and user-defined actions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect active app state

4. 3

   Selected text

5. 4

   Visible screen content

6. 5

   Local model settings and user-defined actions

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed, source-linked task results

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, action versions, user comments, approval states, model allowances, run limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: read active app, selected text or visible screen content; trigger the assistant with one keyboard shortcut. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will mac-based developers, operators and knowledge workers who run AI actions on screen context use it to solve "AI help requires switching apps, copying context and pasting results, which breaks keyboard flow and spreads work across several subscriptions"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Completed tasks per operator hour and corrections after review.
4. Measure, then decide. Track completed tasks per operator hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, source-linked task results. Retain the explicit scope boundary: One macOS version and one approved local model backend; final actions and judgment calls remain with the user.

What the build depends on. Screen-context capture, asynchronous task jobs, editable action history, reviewer access and tested export formats. High-fidelity automation requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One macOS version and one approved local model backend; final actions and judgment calls remain with the user.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined action package. Offer a monthly production allowance after repeat demand. Quote complex multi-app automation or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked task results. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Run AI-assisted actions on a Mac using on-screen context, without switching apps or copying and pasting. Demonstrate a concrete reviewed, source-linked task results using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Mac-based developers, operators and knowledge workers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked task results from a small authorized input set, with a transparent calculation of completed tasks per operator hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five Mac-based developers, operators and knowledge workers and inspect a recent example of AI help requires switching apps, copying context and pasting results, which breaks keyboard flow and spreads work across several subscriptions.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure completed tasks per operator hour and corrections after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Completed tasks per operator hour and corrections after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Completed tasks per operator hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked task results. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved actions, screen-context rules and review examples, together with reliable delivery for a narrow Mac workflow niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for Mac-based developers, operators and knowledge workers. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Cai, Nimbus, Friendware and Tine, plus manual copy-paste between apps. Compare this product with the buyer's present method on completed tasks per operator hour and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, local compute time, storage, reviewer hours, user revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked task results. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.