In-app writing assistance and admin console

For

Writers, support teams and operators who write inside many apps

Solves

Writing help is split across subscriptions, so text, prompts and data sit in tools the buyer does not own.

Delivers

Source-linked drafts, translations and summaries

Built in

about 4 weeks of creation time, MVP in 4 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 tool switching while keeping writing data under the buyer's control.

1. Invoke assistance from any app with a hotkey.
2. Correct grammar, spelling and formatting.
3. Translate text between languages.
4. Adjust tone for the target context.
5. Draft emails and context-aware replies.
6. Summarize websites, articles and documents.
7. Select from multiple AI models per task.
8. Use buyer-supplied API keys.
9. Save customizable prompts and workflows.
10. Show a menu bar entry and floating window.
11. Translate offline after language packs are installed.
12. Prioritize important email messages.
13. Crop and enhance screenshots.
14. Annotate screenshots with text, arrows and shapes.
15. Run on multiple operating systems.
16. Keep a privacy-focused design that does not store or reuse buyer text.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned source-linked draft set with source references and unresolved questions.

Everything these tools do, in one app

• In-app text assistance Provides AI help with writing and text tasks directly inside the applications you already use.Found in Compose, Kerlig AI, Snap AI and 3 more
• Systemwide hotkey access Lets you invoke AI actions from any app with a keyboard shortcut.Found in Compose, Kerlig AI, FetchAI and 1 more
• Grammar and spelling fixes Corrects grammar, spelling, and formatting errors in your text.Found in Compose, Kerlig AI, Rephrase
• Translation Translates text between languages.Found in Compose, Kerlig AI, Snap AI and 3 more
• Tone adjustment Changes the tone or style of your writing to match different contexts.Found in Compose, AI GPT for Gmail™, Kerlig AI and 2 more
• Email drafting and replies Generates email drafts and context-aware reply suggestions.Found in Compose, AI GPT for Gmail™
• Summarization Condenses websites, articles, and documents into shorter summaries.Found in Kerlig AI
• Multiple AI model support Lets you choose from different AI models for various tasks.Found in Kerlig AI, Linguo Translate, FetchAI and 1 more
• Bring your own API keys Allows you to use your own API keys for AI services, giving you control over data and costs.Found in Compose, Kerlig AI
• Customizable workflows Enables tailoring AI actions and prompts to your specific writing needs.Found in Compose, Kerlig AI, FetchAI and 1 more
• Menu bar integration Lives in the macOS menu bar for quick access without opening a separate window.Found in Compose, Linguo Translate
• Floating window Keeps an always-on-top window for easy access while multitasking.Found in Snap AI
• Offline translation Translates text without an internet connection after downloading language packs.Found in Linguo Translate
• Email prioritization Highlights important messages to help you focus on what matters.Found in Superhuman AI for iPhone & iPad
• Screenshot editing Automatically crops and enhances screenshots with AI assistance.Found in ScreenSnapAI
• Annotation tools Adds text, arrows, and shapes to screenshots for clear communication.Found in ScreenSnapAI
• Cross-platform availability Works on multiple operating systems, not just one.Found in ScreenSnapAI, SidekickBar
• Privacy-focused design Does not store or use your data, ensuring privacy and security.Found in Compose, Snap AI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted text, selected models and approved prompts

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted text

4. 3

   Selected models and approved prompts

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked drafts, translations and summaries

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved model set and one permitted input format; final tone, factual and publication checks remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source-linked assistant panel, Prompt and model settings, Administrator console. Use a compact overlay for in-app assistance, a side panel for drafts and sources, and an admin view for keys, models, prompts, usage and audit. Let users compare draft versions and see which source text each suggestion came from. Display draft, changes requested and approved states. Provide a client or teammate preview link with comments anchored to the relevant passage. Make the task-specific outcome source-linked drafts, translations and summaries visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, teammate 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

Buyer-owned documents, authorized email accounts and permitted research sources. Cloud storage, design-file import/export and publishing destinations. 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

   4 days

   One buyer segment, one recurring use case; first modules: invoke assistance from any app with a hotkey; correct grammar, spelling and formatting. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   10 days

   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 writers, support teams and operators who write inside many apps use it to solve "writing help is split across subscriptions, so text, prompts and data sit in tools the buyer does not own"?
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 drafts per writing hour and corrections after approval.
4. Measure, then decide. Track accepted drafts per writing hour and corrections after approval; 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 approved model set and one permitted input format; final tone, factual and publication checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: invoke assistance from any app with a hotkey; correct grammar, spelling and formatting. Support the remaining modules with operator review: translate text between languages; adjust tone for the target context; draft emails and context-aware replies; summarize websites, articles and documents. 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 source-linked drafts, translations and summaries. Retain the explicit scope boundary: One approved model set and one permitted input format; final tone, factual and publication checks remain editorial.

What the build depends on. Text upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist editorial QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model set and one permitted input format; final tone, factual and publication checks remain editorial.

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 writing package. Offer a monthly production allowance after repeat demand. Quote complex multi-team or regulated writing separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked draft set. 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 tool switching while keeping writing data under the buyer's control. Demonstrate a concrete source-linked draft set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Writers, support teams and operators who write inside many apps professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked draft set from a small authorized input set, with a transparent calculation of accepted drafts per writing hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five writers, support teams and operators who write inside many apps and inspect a recent example of writing help split across subscriptions, so text, prompts and data sit in tools the buyer does not own.
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 drafts per writing hour and corrections after approval, 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 drafts per writing hour and corrections after approval. 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 drafts per writing hour and corrections after approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked drafts, translations and summaries. 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 prompts, model settings and review examples, together with reliable delivery for a narrow writing niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for writers, support teams and operators who write inside many apps. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Compose, AI GPT for Gmail™, Kerlig AI, ScreenSnapAI, Snap AI, Linguo Translate, Superhuman AI for iPhone & iPad, FetchAI, Rephrase and SidekickBar. Compare this product with the buyer's present method on accepted drafts per writing hour and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, translation processing, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked drafts, translations and summaries. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Authors approve substantive changes and publication scope. One approved model set and one permitted input format; final tone, factual and publication checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.