Source-linked document explanation and study console

For

Researchers, students and analysts working through difficult papers and reports

Solves

Difficult documents take hours to read, and explanations from separate tools are not linked back to the source text.

Delivers

Source-linked explanations, answers and citations

Built in

about 4 weeks of creation time, MVP in 5 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 reading time while keeping every explanation traceable to the source.

1. Upload documents for processing.
2. Generate plain-language explanations of complex text.
3. Explain highlighted passages on demand.
4. Use surrounding context to improve relevance.
5. Answer reader questions from the document.
6. Highlight sections relevant to a question.
7. Provide citations that jump to the source page or section.
8. Convert text to natural-sounding audio with voice and language options.
9. Produce audio versions of documents for listening.
10. Search across papers and merge insights from multiple sources.
11. Organize documents into collections with secure cross-device access.
12. Support shared notes and collaboration.
13. Trigger explanations from a menu-bar hotkey.
14. Show explanations in a floating panel over the current app.
15. Read text only when explicitly triggered, with no background monitoring.
16. Read only selected text and a small surrounding window, with no local storage after the response.
17. Customize how text is highlighted.

Everything these tools do, in one app

• Document upload Lets users upload documents so the tool can work with their content.Found in Explainpaper, Myreader AI, Jotlify
• AI explanations Provides AI-generated explanations that simplify complex text or concepts.Found in Explainpaper, MiniAi, Jotlify
• Highlight to explain Users highlight difficult sections and receive explanations for the selected text.Found in Explainpaper
• Context-aware responses Uses surrounding text to improve the relevance and accuracy of explanations or answers.Found in MiniAi, Myreader AI
• Question answering Allows users to ask specific questions about the document and get answers drawn from the material.Found in Myreader AI
• Relevant section highlighting Highlights parts of the text that relate to the user's question or topic for further reading.Found in Myreader AI
• Smart citations Provides citations that let users jump directly to the pages or sections containing the information.Found in Myreader AI
• Text to speech Converts text content into natural-sounding audio with multiple voices and languages.Found in Myreader AI, Jotlify
• Audio versions Transforms documents or papers into audio formats for listening on the go.Found in Jotlify
• Advanced search and merge Searches across papers and combines insights from multiple sources for a comprehensive view.Found in Jotlify
• Organized library Manages documents by organizing them into collections with secure access across devices.Found in Myreader AI
• Collaboration and notes Supports sharing insights and saving notes for later reference.Found in Jotlify
• Menu-bar hotkey Lets users trigger explanations with a keyboard shortcut from the menu bar without switching apps.Found in MiniAi
• Floating panel Displays explanations in a small floating panel over the current app.Found in MiniAi
• On-demand operation Only reads text when explicitly triggered, with no background monitoring or continuous recording.Found in MiniAi
• Privacy-minded defaults Reads only selected text and a small surrounding window, with no local storage of processed content after the response.Found in MiniAi
• Customizable highlighting Offers options to customize how text is highlighted.Found in Explainpaper

What goes in, what comes out

The workflow

1. In
   Start with

   Uploaded documents, highlighted passages and reader questions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect uploaded documents

4. 3

   Highlighted passages and reader questions

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked explanations, answers and citations

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 document format set and one language pair; final interpretation and citation checks remain with the reader. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Document library and collections, Reader with highlight and question panel, Admin console for access and usage. Use a thumbnail list for documents, a large central reading canvas, and a right-hand panel for explanations, questions, citations and notes. Let users compare an explanation against the highlighted passage side by side. Display draft, reviewed and approved states for shared notes. Provide a floating panel and menu-bar hotkey for explanations over other apps. Make the task-specific outcome source-linked explanations, answers and citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, document versions, reader comments, approval states, usage allowances, question 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 documents, authorized papers and permitted research sources. Cloud document storage, reference-manager 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

   5 days

   One buyer segment, one recurring use case; first modules: upload documents for processing; generate plain-language explanations of complex text. 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 researchers, students and analysts working through difficult papers and reports use it to solve "difficult documents take hours to read, and explanations from separate tools are not linked back to the source text"?
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: Time to answer a defined question and share of explanations accepted without correction.
4. Measure, then decide. Track time to answer a defined question and share of explanations accepted without correction; 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 document format set and one language pair; final interpretation and citation checks remain with the reader. Implement one approved input format, a bounded representative case set and the first two task modules: upload documents for processing; generate plain-language explanations of complex text. Support the third module with operator review: explain highlighted passages on demand. 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 explanations, answers and citations. Retain the explicit scope boundary: One document format set and one language pair; final interpretation and citation checks remain with the reader.

What the build depends on. Document upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One document format set and one language pair; final interpretation and citation checks remain with the reader.

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 document package. Offer a monthly reading allowance after repeat demand. Quote complex multi-source or specialist review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked explanations, answers and citations. 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 reading time while keeping every explanation traceable to the source. Demonstrate a concrete source-linked explanations, answers and citations using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Researchers, students and analysts working through difficult papers and reports 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 explanations, answers and citations from a small authorized input set, with a transparent calculation of time to answer a defined question and share of explanations accepted without correction and no promised savings.

The first 30 days

1. Week 1: interview five researchers, students and analysts working through difficult papers and reports and inspect a recent example of difficult documents take hours to read, and explanations from separate tools are not linked back to the source text.
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 time to answer a defined question and share of explanations accepted without correction, 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: Time to answer a defined question and share of explanations accepted without correction. 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

Time to answer a defined question and share of explanations accepted without correction; 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 explanations, answers and citations. 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 explanations, citation patterns and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for researchers, students and analysts working through difficult papers and reports. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Explainpaper, MiniAi, Myreader AI and Jotlify. Compare this product with the buyer's present method on time to answer a defined question and share of explanations accepted without correction. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, audio processing, storage, reviewer hours, client revision rounds and licensed source documents. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked explanations, answers and citations. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Readers approve substantive changes and publication scope. One document format set and one language pair; final interpretation and citation checks remain with the reader. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.