Source-grounded ebook reading and study workspace

For

Researchers, students and professional readers working through long ebooks and documents

Solves

Reading and studying long ebooks is slow, and answers, narration, translation and summaries are scattered across several subscriptions.

Delivers

Reviewed reading workspace with cited answers, narration, translation, summaries and exportable notes

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce study time per book while keeping every answer traceable to its source passage.

1. Open and display common ebook and document formats.
2. Answer in-book questions from the book's content.
3. Show the passages or quotes each answer came from.
4. Read the book aloud with text-to-speech narration.
5. Translate selected passages or whole chapters.
6. Summarize dense sections or entire chapters.
7. Collect marked passages and notes into an editable draft.
8. Export highlights and notes as a Markdown file.
9. Suggest books from reading history and preferences.
10. Categorize books by genre and theme.
11. Connect to ebook platforms and retailers for purchase.
12. Create and share curated reading lists.
13. Run without accounts or external servers, keeping data on the device.
14. Use the reader's own AI provider key for AI features.
15. Use a local AI model on Mac for fully offline work.
16. Narrate with a downloaded on-device neural voice.
17. Refine searches and navigate large libraries quickly.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewed reading workspace with source references and unresolved questions.

Everything these tools do, in one app

• Ebook file reading Opens and displays common ebook and document formats for reading.Found in Readr, Reader Alive
• In-book question answering Lets you ask questions and get answers based on the book's content.Found in Readr, Reader Alive
• Source citations Shows the passages or quotes the answer came from so you can verify it.Found in Readr, Reader Alive
• Text-to-speech narration Reads the book aloud so you can listen instead of reading.Found in Readr, Reader Alive
• Translation Translates selected passages or whole chapters into another language.Found in Reader Alive
• Summarization Generates summaries of dense sections or entire chapters.Found in Reader Alive
• Highlights and notes Collects marked passages and notes into an editable draft.Found in Readr
• Markdown export Exports highlights and notes as a Markdown file.Found in Readr
• Personalized recommendations Suggests books based on your reading history and preferences.Found in Bookaroozie
• Genre categorization Uses AI to categorize books so you can explore genres and themes.Found in Bookaroozie
• Retailer integration Connects to ebook platforms and retailers so you can purchase books.Found in Bookaroozie
• Curated reading lists Lets you create and share reading lists with a community.Found in Bookaroozie
• Local-first design Runs without accounts or external servers, keeping data on your device.Found in Readr
• Bring your own AI key Uses your own API key from AI providers for AI features.Found in Readr
• Offline local model Can use a local AI model on Mac to stay completely offline.Found in Readr
• On-device neural voice Uses a downloaded neural voice for narration without sending audio data.Found in Readr
• Search refinement Supports easy navigation and refining searches to find books.Found in Bookaroozie

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed ebook files, reading history and personal notes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed ebook files

4. 3

   Reading history and personal notes

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed reading workspace with cited answers, narration, translation, summaries and exportable notes

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. Local-first operation and the reader's own AI key remain options; final citation, translation and summary checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Library and import, Reading and study workspace, Notes and export. Use a thumbnail shelf for books, a large central reading pane, and a right-hand panel for questions, citations, notes and narration controls. Let users compare a passage with its translation and summary side by side. Display draft, reviewed and exported states for notes. Provide a shared reading-list view with comments anchored to the relevant passage. Make the task-specific outcome reviewed reading workspace with cited answers, narration, translation, summaries and exportable notes visible beside its evidence, review state and value baseline.

Accounts and administration

Library ownership, file versions, shared reading lists, approval states, usage allowances, export 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

Reader-owned ebook files, personal notes and permitted research sources. Cloud file storage, ebook platform import/export and note destinations. Start with file exchange and validate destination specifications before promising direct retailer purchase. 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: open and display common ebook and document formats; answer in-book questions from the book's content. 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

   3 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 researchers, students and professional readers working through long ebooks and documents use it to solve "reading and studying long ebooks is slow, and answers, narration, translation and summaries are scattered 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: Verified answers per study hour and correction rate on cited passages.
4. Measure, then decide. Track verified answers per study hour and correction rate on cited passages; 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 fixed ebook format and one language pair; final citation, translation and summary checks remain human. Implement one approved input format, a bounded representative book set and the first two task modules: open and display common ebook and document formats; answer in-book questions from the book's content. Support the remaining modules with operator review: show the passages or quotes each answer came from; read the book aloud with text-to-speech narration; translate selected passages or whole chapters; summarize dense sections or entire chapters; collect marked passages and notes into an editable draft; export highlights and notes as a Markdown file. 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 formats, languages and book volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the reviewed reading workspace. Retain the explicit scope boundary: One fixed ebook format and one language pair; final citation, translation and summary checks remain human.

What the build depends on. File upload and preview, asynchronous AI jobs, editable version history, reviewer access and tested export formats. High-fidelity citation and translation require specialist language QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed ebook format and one language pair; final citation, translation and summary checks 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 book package. Offer a monthly reading allowance after repeat demand. Quote complex multi-language or specialist research workflows separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed reading workspace. 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 study time per book while keeping every answer traceable to its source passage. Demonstrate a concrete reviewed reading workspace using the buyer's approved book and show the baseline, corrections and actual delivery effort.

Where to find buyers

Researchers, students and professional readers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant academic or practitioner events.

Lead magnet

A reviewed sample reading workspace with cited answers, narration, translation, summaries and exportable notes from a small authorized book set, with a transparent calculation of verified answers per study hour and correction rate on cited passages and no promised savings.

The first 30 days

1. Week 1: interview five researchers, students and professional readers and inspect a recent example of slow study of long ebooks and scattered answers, narration, translation and summaries.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure verified answers per study hour and correction rate on cited passages, 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: Verified answers per study hour and correction rate on cited passages. 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

Verified answers per study hour and correction rate on cited passages; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed reading workspace. 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 formats, citation examples and reviewer corrections, together with reliable local-first delivery for a narrow reading niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for researchers, students and professional readers. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Readr, Bookaroozie and Reader Alive, plus generic reading apps and separate translation or narration tools. Compare this product with the buyer's present method on verified answers per study hour and correction rate on cited passages. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, narration processing, storage, reviewer hours, client revision rounds and licensed source books. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the reviewed reading workspace. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Readers approve substantive changes and sharing scope. One fixed ebook format and one language pair; final citation, translation and summary checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.