Screen-aware assistant and admin console

For

Software teams and IT administrators who support users working across many desktop applications

Solves

Support and development work is split across tools that each see only part of the screen, so context is lost and follow-ups are missed.

Delivers

Reviewed, source-linked assistance and tracked follow-ups

Built in

about 5 weeks of creation time, MVP in 6 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

Reduce context switching and missed follow-ups while keeping screen data under the buyer's control.

1. Read the current screen to establish context without manual screenshots.
2. Activate the assistant by hotkey or hold-to-talk.
3. Point to the exact UI element to click for multi-step tasks.
4. Accept spoken questions and return spoken answers.
5. Transcribe meeting audio locally into text.
6. Summarize, translate or analyze selected on-screen text.
7. Automate tasks through GitHub, Notion, Slack and Google Calendar.
8. Personalize answers from user preferences and context.
9. Provide inspectable source code for modification.
10. Support local or self-hosted models for offline use.
11. Run on both Mac and Windows.
12. Show answers in a desktop overlay without switching apps.
13. Meter usage by credits with tiered daily limits.
14. Share the screen with the assistant in one click.
15. Keep session chat history for multi-step workflows.
16. Capture to-dos and commitments from the workflow.
17. Log interactions and surface promises made in conversations.
18. Surface upcoming actions from accumulated context.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed assistance record with source references and unresolved questions.

Everything these tools do, in one app

• Screen-aware assistance Reads what is on your screen to understand context without manual screenshots or copying.Found in PIP, Everywhere, Crade AI and 4 more
• Hotkey activation Activates the assistant quickly with a keyboard shortcut or hold-to-talk key.Found in PIP, Everywhere, Clicky
• Visual click-pointing Shows exactly where to click or highlights UI elements for multi-step tasks.Found in PIP, Clicky
• Voice interaction Allows spoken questions and provides spoken responses for hands-free use.Found in Highlight AI, Clicky, Talk To Your Computer
• Local audio transcription Captures and transcribes meeting audio locally into text.Found in Highlight AI
• Instant content analysis Summarizes, translates, or analyzes selected on-screen text quickly.Found in Highlight AI
• Workflow automation Integrates with tools like GitHub, Notion, Slack, and Google Calendar to automate tasks.Found in Highlight AI
• Customizable responses Personalizes answers based on user preferences and context.Found in Highlight AI
• Open-source Provides source code for inspection, modification, and community contributions.Found in Everywhere, Clicky
• Local model support Allows use of local or self-hosted AI models for offline or privacy-focused operation.Found in Everywhere
• Cross-platform Works on both Mac and Windows operating systems.Found in Crade AI, Highlight AI
• Desktop overlay Displays answers in a small window above other applications without switching.Found in Crade AI
• Credit-based usage Limits usage by credits, with higher tiers offering more daily credits and smarter AI.Found in Crade AI
• Screen sharing Shares your screen with the AI via a one-click action for real-time visibility.Found in Talk To Your Computer
• Session chat history Keeps track of the conversation within a session for multi-step workflows.Found in Talk To Your Computer
• Intent capture Identifies to-dos and commitments from your workflow without manual entry.Found in AirJelly
• Follow-up tracking Logs interactions and surfaces promises made in conversations to avoid missed follow-ups.Found in AirJelly
• Proactive task surfacing Foresees upcoming actions based on accumulated context and brings them to your attention.Found in AirJelly

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted screen context, local audio, session history and connected tool events

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted screen context

4. 3

   Local audio

5. 4

   Session history and connected tool events

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked assistance and tracked follow-ups

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. Screen capture requires explicit user consent per session; final actions and follow-up commitments remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Screen context and consent, Editable assistance preview, Admin console and audit. Use a thumbnail gallery for sessions, a large central overlay canvas, and a right-hand panel for sources, permissions and comments. Let users compare suggested actions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant screen region. Make the task-specific outcome reviewed, source-linked assistance and tracked follow-ups visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision 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 screen sessions, authorized meeting audio and permitted tool events. GitHub, Notion, Slack and Google Calendar; local model runtimes; Mac and Windows desktop APIs. Start with file exchange and validate destination specifications before promising direct publishing. 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

   6 days

   One buyer segment, one recurring use case; first modules: read the current screen to establish context without manual screenshots; activate the assistant by hotkey or hold-to-talk. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 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 software teams and IT administrators who support users working across many desktop applications use it to solve "support and development work is split across tools that each see only part of the screen, so context is lost and follow-ups are missed"?
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: Accepted assisted tasks per support hour and missed follow-ups after review.
4. Measure, then decide. Track accepted assisted tasks per support hour and missed follow-ups 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 operating system and one connected tool; screen capture requires explicit user consent per session; final actions and follow-up commitments remain human. Implement one approved input format, a bounded representative case set and the first two task modules: read the current screen to establish context without manual screenshots; activate the assistant by hotkey or hold-to-talk. Support the third module with operator review: point to the exact UI element to click for multi-step tasks. 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 assistance and tracked follow-ups. Retain the explicit scope boundary: One operating system and one connected tool; screen capture requires explicit user consent per session; final actions and follow-up commitments remain human.

What the build depends on. Screen capture and overlay rendering, asynchronous model jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist IT QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One operating system and one connected tool; screen capture requires explicit user consent per session; final actions and follow-up commitments remain human.

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 screen-assistance package. Offer a monthly production allowance after repeat demand. Quote complex multi-tool or on-premise deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked assistance and tracked follow-ups. 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

Reduce context switching and missed follow-ups while keeping screen data under the buyer's control. Demonstrate a concrete reviewed, source-linked assistance and tracked follow-ups using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams and IT administrators who support users working across many desktop applications 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 assistance and tracked follow-ups from a small authorized input set, with a transparent calculation of accepted assisted tasks per support hour and missed follow-ups after review and no promised savings.

The first 30 days

1. Week 1: interview five software teams and IT administrators who support users working across many desktop applications and inspect a recent example of support and development work is split across tools that each see only part of the screen, so context is lost and follow-ups are missed.
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 accepted assisted tasks per support hour and missed follow-ups 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: Accepted assisted tasks per support hour and missed follow-ups 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

Accepted assisted tasks per support hour and missed follow-ups 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 assistance and tracked follow-ups. 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 screen patterns, connected-tool mappings and review examples, together with reliable delivery for a narrow IT niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software teams and IT administrators who support users working across many desktop applications. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

PIP, Everywhere, Crade AI, Highlight AI, Clicky, Talk To Your Computer and AirJelly, plus manual screen sharing and generic chat tools. Compare this product with the buyer's present method on accepted assisted tasks per support hour and missed follow-ups 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, screen and audio processing, storage, reviewer hours, client 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 assistance and tracked follow-ups. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user consent, source attribution, screen-data accuracy and usage permissions. Users approve substantive actions and follow-up scope. One operating system and one connected tool; screen capture requires explicit user consent per session; final actions and follow-up commitments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.