Source-linked research writing and review workbench

For

Researchers, research groups and science editors producing source-based papers, reports and review documents

Solves

Summaries, outlines, citations and section drafts are scattered across several tools, so sources, claims and reviewer corrections are hard to trace and reuse.

Delivers

Editor-approved source-linked document sections with citations

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce document revision cycles while preserving source accuracy and author meaning.

1. Import PDFs, Word files and notes.
2. Summarize long texts into concise summaries.
3. Highlight key points and extract relevant data.
4. Navigate summaries interactively.
5. Generate AI-assisted outlines.
6. Improve flow and organization of ideas.
7. Proofread for errors and clarity.
8. Manage complex writing tasks in one workspace.
9. Provide free access to basic features.
10. Offer stage-specific writing assistants.
11. Guide crafting of introductions, methods and conclusions.
12. Support outlining, drafting and revising.
13. Apply academic conventions guidance.
14. Automate routine editing and formatting.
15. Provide prompts and techniques for writer's block.
16. Generate reports and essays with professional layout.
17. Source and integrate citations for claims.
18. Build charts and visualizations from supplied data.
19. Provide templates and workflows for document types.
20. Support planned multimedia additions.
21. Give personalized advice on request.
22. Keep a clean, fast interface.
23. Integrate with productivity tools.
24. Improve accuracy through reviewed corrections.

Everything these tools do, in one app

• Document summarization Condenses long texts into concise summaries.Found in Yomu AI
• Multi-format support Handles various document types like PDFs and Word files.Found in Yomu AI
• Key point highlighting Allows users to highlight important points and extract relevant data.Found in Yomu AI
• Interactive summary navigation Provides an interactive interface to easily navigate through summaries.Found in Yomu AI
• AI-powered outlining Helps structure documents logically with AI-generated outlines.Found in SciPub+, SciPubPlus
• Flow and organization improvement Improves the flow and organization of ideas within texts.Found in SciPub+
• Proofreading assistance Catches errors and enhances clarity before final submission.Found in SciPub+
• Complex writing task management Supports managing complex scientific writing tasks with intuitive interfaces.Found in SciPub+
• Free usage options Provides free access to basic features.Found in SciPub+
• Specialized writing assistants Offers multiple assistants each dedicated to a different stage of writing.Found in SciPubPlus
• Section crafting guidance Automated guidance for crafting essential sections like introductions, methodologies, and conclusions.Found in SciPubPlus
• Structured assistance Guided support through outlining, drafting, and revising.Found in SciPubPlus
• Academic conventions guidance Tailors guidance to conform with established scholarly standards.Found in SciPubPlus
• Repetitive task automation Automates routine editing and formatting tasks.Found in SciPubPlus
• Writer's block support Offers techniques and prompts to help users initiate and sustain writing.Found in SciPubPlus
• One-click content generation Generates slides, research reports, and academic essays with professional layout in one click.Found in Oreate
• Automatic sourcing and citation Automatically sources and integrates citations to support claims.Found in Oreate
• Built-in charting and visualizations Creates charts and visualizations for data-driven slides and reports.Found in Oreate
• Templates and workflows Provides templates and workflows for various document types.Found in Oreate
• Planned multimedia features Planned additions such as AI image, podcast, and video features.Found in Oreate
• Personalized advice Provides personalized AI-driven advice on diverse subjects.Found in WeGuru
• Clean user interface Offers an intuitive and clean user interface for seamless interaction.Found in WeGuru
• Fast response times Ensures efficient information delivery with fast response times.Found in WeGuru
• Productivity tool integration Integrates with other productivity tools to boost workflow.Found in Yomu AI, WeGuru
• Continuous learning Improves accuracy and relevance over time through continuous learning.Found in WeGuru

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed papers, datasets, notes and journal constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed papers

4. 3

   Datasets

5. 4

   Notes and journal constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Editor-approved source-linked document sections with 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 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 fixed document type and citation style; final accuracy and meaning checks remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source intake and references, Editable document workspace, Review and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant claim or section. Make the task-specific outcome editor-approved source-linked document sections with citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, client 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 manuscripts, authorized interviews 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

   6 days

   One buyer segment, one recurring use case; first modules: import PDFs, Word files and notes; summarize long texts into concise summaries. 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, research groups and science editors producing source-based papers, reports and review documents use it to solve "summaries, outlines, citations and section drafts are scattered across several tools, so sources, claims and reviewer corrections are hard to trace and reuse"?
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 sections per editorial hour and corrections after review.
4. Measure, then decide. Track accepted sections per editorial hour and 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 fixed document type and citation style; final accuracy and meaning checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: import PDFs, Word files and notes; summarize long texts into concise summaries. Support the third module with operator review: highlight key points and extract relevant data. 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 editor-approved source-linked document sections with citations. Retain the explicit scope boundary: One fixed document type and citation style; final accuracy and meaning checks remain editorial.

What the build depends on. Source 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 fixed document type and citation style; final accuracy and meaning 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 document package. Offer a monthly production allowance after repeat demand. Quote complex multimedia or specialist visualization separately. These are test prices, not market benchmarks. Package the initial sale as one bounded editor-approved source-linked document sections with 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 document revision cycles while preserving source accuracy and author meaning. Demonstrate a concrete editor-approved source-linked document sections with citations using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample editor-approved source-linked document sections with citations from a small authorized input set, with a transparent calculation of accepted sections per editorial hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five researchers, research groups and science editors producing source-based papers, reports and review documents and inspect a recent example of summaries, outlines, citations and section drafts scattered across several tools.
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 sections per editorial hour and 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 sections per editorial hour and 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 sections per editorial hour and 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 editor-approved source-linked document sections with 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 citation styles, journal constraints 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, research groups and science editors producing source-based papers, reports and review documents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Yomu AI, SciPub+, SciPubPlus, Oreate and WeGuru, plus manual reference managers and word processors. Compare this product with the buyer's present method on accepted sections per editorial hour and 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

Generation attempts, document 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 editor-approved source-linked document sections with citations. 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 fixed document type and citation style; final accuracy and meaning checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.