Research reading and source stewardship console

For

Researchers, analysts and research teams who read, summarize and cite large volumes of documents and web pages

Solves

Sources, summaries, notes and citations live in separate tools, so reading work is repeated and provenance is lost.

Delivers

Reviewer-approved source library with linked summaries, citations and annotations

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 repeated reading and citation rework while keeping sources traceable.

1. Summarize long documents and web pages.
2. Summarize the current web page from a browser extension.
3. Save and organize sources in one searchable library.
4. Extract author, date, publication and reading-time metadata.
5. Paraphrase passages in a chosen style or tone.
6. Generate references and manage citations.
7. Search across the whole document collection.
8. Answer questions from stored document content.
9. Annotate and comment on passages.
10. Share sources for team annotation and review.
11. Read PDFs, images, audio, video and handwriting.
12. Handle content in many languages.
13. Assist writing with completions and edit suggestions.
14. Generate counterarguments, headlines and draft text.
15. Transcribe audio and video into minutes.
16. Filter weak arguments and clickbait.
17. Trigger summarization by keyboard shortcut.
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 reviewer-approved source library with source references and unresolved questions.

Everything these tools do, in one app

• Text summarization Condenses long documents or web pages into short, easy-to-read summaries.Found in Genei, TLDR This, Gimme Summary AI and 2 more
• Webpage summarization Generates a summary of any web page directly from the browser.Found in Genei, TLDR This, Gimme Summary AI and 2 more
• Browser extension Adds summarization and saving tools to the web browser for quick access.Found in Genei, TLDR This, Gimme Summary AI and 1 more
• Document management Organizes, stores, and manages documents in one place.Found in Genei, Petal
• Metadata extraction Automatically pulls details like author, publication date, and reading time from content.Found in TLDR This, Petal
• Paraphrasing Rewrites text in a different style or tone.Found in Genei, TLDR This
• Citation management Generates references and helps manage citations for proper sourcing.Found in Genei, Unriddle
• Search within documents Finds specific information inside a large collection of documents.Found in Genei, Unriddle
• Question answering Answers questions based on the content of your documents.Found in Genei, Petal
• Collaboration tools Lets teams annotate, comment, and share documents for joint work.Found in Unriddle, Petal
• Annotation Allows users to add notes and comments directly on documents.Found in Genei, Petal
• Multi-format support Reads and extracts information from PDFs, images, audio, video, and handwritten documents.Found in Unriddle
• Multilingual support Works with content in over 90 languages.Found in Unriddle
• Writing assistance Helps write and edit text with auto-completion and suggestions.Found in Unriddle
• Content generation Creates counterarguments, headlines, and other written content.Found in BearlyAI
• Media transcription Transcribes audio and video and generates meeting minutes.Found in BearlyAI
• Clickbait filtering Filters out weak arguments and clickbait to focus on core information.Found in TLDR This
• Keyboard shortcut Activates the summarization tool quickly with a key combination.Found in Gimme Summary AI

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed documents, web pages, media files and reading notes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed documents

4. 3

   Web pages

5. 4

   Media files and reading notes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved source library with linked summaries, citations and annotations

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 metadata parsing, citation formatting, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved input format and one citation style per pilot; final source checks and citation accuracy remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Library intake and metadata, Reading and annotation workspace, Review and export. Use a searchable library list for sources, a large central reading canvas, and a right-hand panel for summaries, citations, annotations and questions. Let users compare source text against generated summaries side by side. Display draft, changes requested and approved states. Provide a shared team view with comments anchored to the relevant passage. Make the task-specific outcome reviewer-approved source library with linked summaries, citations and annotations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, team 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

Author-owned documents, authorized web pages and permitted research sources. Cloud file 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

   6 days

   One buyer segment, one recurring use case; first modules: summarize long documents and web pages; extract author, date, publication and reading-time metadata. 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, analysts and research teams who read, summarize and cite large volumes of documents and web pages use it to solve "sources, summaries, notes and citations live in separate tools, so reading work is repeated and provenance is lost"?
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 summaries per research hour and citation corrections after review.
4. Measure, then decide. Track accepted summaries per research hour and citation corrections 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 approved input format and one citation style; final source checks and citation accuracy remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: summarize long documents and web pages; extract author, date, publication and reading-time metadata. Support the remaining modules with operator review: save and organize sources in one searchable library; generate references and manage citations; annotate and comment on passages. 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 a reviewer-approved source library with linked summaries, citations and annotations. Retain the explicit scope boundary: One approved input format and one citation style; final source checks and citation accuracy remain editorial.

What the build depends on. Source upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity citation work requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved input format and one citation style; final source checks and citation accuracy 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 source package. Offer a monthly research allowance after repeat demand. Quote complex media, multilingual or specialist review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved source library with linked summaries, citations and annotations. 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 repeated reading and citation rework while keeping sources traceable. Demonstrate a concrete reviewer-approved source library with linked summaries, citations and annotations using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Researchers, analysts and research teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved source library with linked summaries, citations and annotations from a small authorized input set, with a transparent calculation of accepted summaries per research hour and citation corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five researchers, analysts and research teams who read, summarize and cite large volumes of documents and web pages and inspect a recent example of sources, summaries, notes and citations living in separate tools, so reading work is repeated and provenance is lost.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted summaries per research hour and citation corrections 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 summaries per research hour and citation corrections 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 summaries per research hour and citation corrections 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 a reviewer-approved source library with linked summaries, citations and annotations. 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 citation styles, source formats 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, analysts and research teams who read, summarize and cite large volumes of documents and web pages. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Genei, TLDR This, Gimme Summary AI, Unriddle, BearlyAI and Petal, plus manual reading and reference managers. Compare this product with the buyer's present method on accepted summaries per research hour and citation corrections 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, media transcription and 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 a reviewer-approved source library with linked summaries, citations and annotations. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Researchers approve substantive changes and publication scope. One approved input format and one citation style; final source checks and citation accuracy remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.