In-app reading and writing assistant console

For

Support teams, knowledge workers and language learners who read and write inside web apps all day

Solves

Getting AI help means leaving the page, copying text into another tool and losing the context of the task.

Delivers

Reviewed in-place text actions linked to their source

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 context switching while keeping the user's text and sources under their control.

1. Show a floating assistant inside the current app or website.
2. Read the visible on-screen content in real time.
3. Offer one-click write, improve, paraphrase, summarize, explain, translate and reply actions on selected text.
4. Draft replies to messages and tickets.
5. Summarize long pages, threads and documents.
6. Translate words, phrases and passages.
7. Support multiple languages in one interface.
8. Trigger custom assistants with a single click.
9. Pull relevant answers from connected knowledge sources.
10. Translate highlighted words or phrases directly on the page.
11. Save looked-up words and phrases to a personal dictionary.
12. Show example sentences for saved words.
13. Play audio pronunciations.
14. Run vocabulary quizzes from saved items.
15. Compare the reviewed result with the recorded baseline and value assumptions.
16. Capture corrections and named-owner approval before consequential use.
17. Export a versioned reviewed in-place text actions linked to their source with source references and unresolved questions.

Everything these tools do, in one app

• In-app AI assistance Lets users access AI help directly inside the app or website they are already using.Found in Flot AI, Web Companion by Super
• No context switching Avoids the need to switch tabs or copy-paste information to get AI help.Found in Flot AI, Web Companion by Super, PhraseClip
• On-screen content awareness Reads and understands the content currently visible on the screen in real time.Found in Web Companion by Super
• One-click text actions Provides single-click options to write, improve, paraphrase, summarize, explain, translate, or reply to selected text.Found in Flot AI
• Draft replies Helps users compose responses or replies to messages.Found in Flot AI, Web Companion by Super
• Summarize content Condenses text or data into a shorter summary.Found in Flot AI, Web Companion by Super
• Translate text Translates words, phrases, or text into another language.Found in Flot AI, PhraseClip
• Multilingual support Supports communication and learning across multiple languages.Found in Flot AI, PhraseClip
• Floating assistant interface Appears as a lightweight floating assistant that does not interrupt the current workflow.Found in Flot AI
• GPT-4 powered Uses GPT-4 technology to generate up-to-date AI responses.Found in Flot AI
• Web app integration Works inside multiple web applications such as CRMs, wikis, and help desks.Found in Web Companion by Super
• Knowledge base extraction Automatically pulls relevant information from connected knowledge sources to answer questions.Found in Web Companion by Super
• Custom assistant triggers Allows users to trigger custom assistants with a single click.Found in Web Companion by Super
• Instant word translation Translates highlighted words or phrases directly on the page.Found in PhraseClip
• Personal dictionary Automatically saves looked-up words and phrases for later review.Found in PhraseClip
• Example sentences Shows example sentences to help users understand words in context.Found in PhraseClip
• Audio pronunciations Provides audio pronunciations to help users learn correct pronunciation.Found in PhraseClip
• Vocabulary quizzes Offers quizzes based on saved words and phrases to reinforce learning.Found in PhraseClip

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted page content, selected text, connected knowledge sources and saved vocabulary

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted page content

4. 3

   Selected text

5. 4

   Connected knowledge sources and saved vocabulary

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed in-place text actions linked to their source

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. One host application and one connected knowledge source; final replies, translations and published text remain human-reviewed. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant settings and sources, In-page floating assistant, Admin console and review queue. Use a compact floating panel over the host page, a side panel for sources and saved items, and a full console for administrators. Let users compare draft and edited text side by side. Display draft, changes requested and approved states. Provide a review link with comments anchored to the relevant text span. Make the task-specific outcome reviewed in-place text actions linked to their source visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, source connections, assistant triggers, saved vocabulary, approval states, usage allowances, revision limits, export 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

Customer-owned help desks, CRMs, wikis and knowledge bases. Cloud document storage, browser extension surfaces and export 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: show a floating assistant inside the current app or website; read the visible on-screen content in real time. 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 support teams, knowledge workers and language learners who read and write inside web apps all day use it to solve "getting AI help means leaving the page, copying text into another tool and losing the context of the task"?
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 in-place actions per working hour and corrections after use.
4. Measure, then decide. Track accepted in-place actions per working hour and corrections after use; 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 host application and one connected knowledge source; final replies, translations and published text remain human-reviewed. Implement one approved input format, a bounded representative case set and the first two task modules: show a floating assistant inside the current app or website; read the visible on-screen content in real time. Support the third module with operator review: offer one-click write, improve, paraphrase, summarize, explain, translate and reply actions on selected text. 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 in-place text actions linked to their source. Retain the explicit scope boundary: One host application and one connected knowledge source; final replies, translations and published text remain human-reviewed.

What the build depends on. Page content capture, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity support use requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One host application and one connected knowledge source; final replies, translations and published text remain human-reviewed.

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 workspace. Offer a monthly seat allowance after repeat demand. Quote complex multi-source or specialist language work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed in-place text actions linked to their source. 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 while keeping the user's text and sources under their control. Demonstrate a concrete reviewed in-place text actions linked to their source using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support teams, knowledge workers and language learners who read and write inside web apps all day professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed in-place text actions linked to their source from a small authorized input set, with a transparent calculation of accepted in-place actions per working hour and corrections after use and no promised savings.

The first 30 days

1. Week 1: interview five support teams, knowledge workers and language learners who read and write inside web apps all day and inspect a recent example of getting AI help means leaving the page, copying text into another tool and losing the context of the task.
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 in-place actions per working hour and corrections after use, 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 in-place actions per working hour and corrections after use. 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 in-place actions per working hour and corrections after use; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed in-place text actions linked to their source. 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 assistant triggers, source mappings and review examples, together with reliable delivery for a narrow support and learning niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for support teams, knowledge workers and language learners who read and write inside web apps all day. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Flot AI, Web Companion by Super and PhraseClip, plus browser extensions, copy-paste into a chat tool and built-in app helpers. Compare this product with the buyer's present method on accepted in-place actions per working hour and corrections after use. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, page and source 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 reviewed in-place text actions linked to their source. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user voice, source attribution, quotation accuracy and usage permissions. Users approve substantive changes and publication scope. One host application and one connected knowledge source; final replies, translations and published text remain human-reviewed. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.