Reviewed research evidence and writing workspace

For

Researchers, research groups and academic writers producing literature reviews and papers

Solves

Papers, notes, extracted data and draft text sit in separate tools, so search, reading, extraction and writing are repeated by hand and citations drift out of sync.

Delivers

Reviewer-approved evidence set with linked citations

Built in

about 6 weeks of creation time, MVP in 7 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 manual search, extraction and citation work while keeping every claim traceable to a source.

1. Search licensed academic databases and the open web for relevant papers.
2. Screen results against stated inclusion and exclusion criteria.
3. Summarize each paper with source-linked quotes.
4. Extract specified data points, including from tables.
5. Answer questions against uploaded PDFs.
6. Provide a conversational interface for explanations about a paper.
7. Organize citations, notes and references in one library.
8. Take notes with backlinks and outgoing links.
9. Draft literature review sections with citations.
10. Suggest relevant citations while writing.
11. Check grammar and formal academic style.
12. Apply journal templates to drafts.
13. Export citations in reference-manager and bibliography formats.
14. Share drafts and workspaces for feedback.
15. Recommend papers from stated interests and prior activity.
16. Show supporting quotes and explanations for extracted or selected information.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewer-approved evidence set with source references and unresolved questions.

Everything these tools do, in one app

• Paper Search Finds relevant academic papers from large databases or the web.Found in Elicit, Deep Review by SciSpace, ResearchGPT and 4 more
• Paper Summarization Generates concise summaries of academic papers to speed up understanding.Found in Elicit, LitRevu, ResearchGPT and 4 more
• Literature Review Generation Automatically creates literature review paragraphs or sections with citations.Found in Elicit, ResearchGPT, OpenRead and 1 more
• Data Extraction Pulls specific data points, including from tables, out of papers.Found in Elicit
• Citation Export Exports citations in formats suitable for reference managers or bibliographies.Found in Sourcely
• Reference Management Organizes citations, notes, and references in a central place.Found in Paperguide, Enago Read
• PDF Upload and Q&A Allows users to upload PDFs and ask questions to get answers from the document.Found in ResearchGPT, OpenRead
• AI Chat Interface Provides a conversational interface to ask questions and get explanations about papers.Found in Paperguide, Enago Read
• Academic Writing Assistance Helps draft and improve academic content with suggestions and corrections.Found in Paperguide, SciSpace AI Academic Writer
• Citation Suggestions Suggests relevant citations and references while writing.Found in SciSpace AI Academic Writer
• Grammar and Style Correction Checks and corrects grammar and style for formal academic tone.Found in SciSpace AI Academic Writer
• Note-taking System Provides tools to take and organize notes, including backlinks and outgoing links.Found in OpenRead
• Collaboration Tools Enables sharing drafts, workspaces, and feedback among researchers.Found in Elicit, Enago Read, SciSpace AI Academic Writer
• Personalized Recommendations Suggests relevant papers based on user interests or previous activity.Found in Enago Read
• Transparent Sourcing Shows supporting quotes or explanations for how information was extracted or selected.Found in Elicit, Deep Review by SciSpace
• Journal Templates Offers pre-built templates for writing journal papers.Found in OpenRead
• Research Community Provides seminars and a community for researchers to connect and collaborate.Found in OpenRead

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed database results, uploaded PDFs, reading notes and draft text

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed database results

4. 3

   Uploaded PDFs

5. 4

   Reading notes and draft text

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved evidence set with linked 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 defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Search and screening, Evidence and extraction, Draft and citation review. Use a thumbnail gallery for projects, a large central reading and extraction canvas, and a right-hand panel for sources, notes and comments. Let users compare paper versions and draft versions side by side. Display screened, extracted, drafted and approved states. Provide a shared workspace link with comments anchored to the relevant paper, table or paragraph. Make the task-specific outcome reviewer-approved evidence set with linked citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, shared workspace access, comments, approval states, database 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, licensed database APIs, reference managers and journal submission destinations. Start with file exchange and validate destination specifications before promising direct submission. 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

   7 days

   One buyer segment, one recurring use case; first modules: search licensed academic databases and the open web for relevant papers; screen results against stated inclusion and exclusion criteria. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 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 academic writers producing literature reviews and papers use it to solve "papers, notes, extracted data and draft text sit in separate tools, so search, reading, extraction and writing are repeated by hand and citations drift out of sync"?
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 review sections per research hour and citation corrections after review.
4. Measure, then decide. Track accepted review sections 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 defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions. Implement one approved input format, a bounded representative case set and the first two task modules: search licensed academic databases and the open web for relevant papers; screen results against stated inclusion and exclusion criteria. Support the remaining modules with operator review: summarize each paper with source-linked quotes; extract specified data points, including from tables; draft literature review sections with citations. 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 database integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the reviewer-approved evidence set with linked citations. Retain the explicit scope boundary: One defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions.

What the build depends on. PDF upload and preview, asynchronous extraction jobs, editable version history, reviewer access and tested export formats. High-fidelity extraction requires specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions.

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 review package. Offer a monthly research allowance after repeat demand. Quote complex systematic reviews or specialist domains separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved evidence set with linked 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 manual search, extraction and citation work while keeping every claim traceable to a source. Demonstrate a concrete reviewer-approved evidence set with linked citations using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

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

The first 30 days

1. Week 1: interview five researchers, research groups and academic writers producing literature reviews and papers and inspect a recent example of papers, notes, extracted data and draft text sitting in separate tools.
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 review sections 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 review sections 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 review sections 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 evidence set with linked 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 review protocols, extraction schemas and reviewer corrections, 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 academic writers producing literature reviews and papers. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Elicit, LitRevu, Deep Review by SciSpace, ResearchGPT, Paperguide, OpenRead, Enago Read, Sourcely, SciSpace AI Academic Writer and Undermind are what buyers use today, each covering part of the job. Compare this product with the buyer's present method on accepted review sections 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

Database access, PDF 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 the reviewer-approved evidence set with linked citations. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Researchers approve substantive changes and publication scope. One defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.