Reviewed research evidence and writing workspace

For

Researchers, research groups and academic writers producing papers, reports and presentations

Solves

Literature search, citation management, writing, analysis and figure creation sit in separate subscriptions, so sources, data and drafts drift apart and reviewer feedback is hard to trace.

Delivers

Researcher-approved manuscripts, figures and reference lists linked to their sources

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 tool switching and reference errors while keeping the researcher's judgment in control.

1. Search permitted academic sources for relevant papers.
2. Summarize selected papers with source links.
3. Extract themes and search scopes from uploaded drafts.
4. Let users choose specific sources, papers or documents for analysis.
5. Show an editable outline before analysis starts.
6. Set research depth and speed before a run.
7. Support iterative report building step by step.
8. Run statistical or code-based analysis on supplied research data.
9. Create figures and tables from analyzed data.
10. Draft and edit academic text with tracked suggestions.
11. Insert and format citations and verify metadata.
12. Check that references are accurate and complete.
13. Detect and fix LaTeX errors and format equations.
14. Generate presentation slides from paper content.
15. Revise papers against reviewer comments.
16. Support real-time multi-user editing and sharing.
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 researcher-approved manuscript, figure set and reference list with source references and unresolved questions.

Everything these tools do, in one app

• Literature search Searches academic sources to find relevant papers.Found in SciSpace Agent, Ressearch AI, SciFocus and 3 more
• Paper summarization Generates concise summaries of papers or documents.Found in SciSpace Agent, SciFocus, Chirpz Agent
• Citation management Inserts and formats citations, and verifies metadata.Found in SciSpace Agent, Chirpz Agent, Bibby AI and 2 more
• Writing assistance Helps draft and edit academic text with suggestions.Found in SciSpace Agent, Ressearch AI, SciFocus and 2 more
• Data analysis Runs statistical or code-based analysis on research data.Found in SciSpace Agent, Ressearch AI, Ontosight.ai
• Visualization creation Creates figures, tables, or other visual outputs from data.Found in SciSpace Agent, Ressearch AI, Ontosight.ai
• Real-time collaboration Allows multiple users to edit and share work simultaneously.Found in SciFocus, Bibby AI, Murfy AI and 1 more
• LaTeX support Detects and fixes LaTeX errors and formats equations.Found in Bibby AI, Murfy AI
• Reference verification Checks that references are accurate and complete.Found in Chirpz Agent, Murfy AI
• Semantic search Finds information based on meaning rather than exact keywords.Found in Chirpz Agent, Chirpz, Ontosight.ai
• Draft upload analysis Analyzes uploaded drafts to extract themes and search scopes.Found in Chirpz Agent
• Source selection Lets users choose specific websites, papers, or documents for analysis.Found in Spine Research
• Iterative research Supports building and refining reports step-by-step.Found in Spine Research
• Outline preview Shows an editable outline before the AI starts analysis.Found in Spine Research
• Adjustable depth Controls how thorough or fast the research process is.Found in Spine Research
• Integrated editor Combines AI-generated content with human input in one interface.Found in Spine Research, Chirpz
• Slide generation Creates presentation slides from paper content.Found in Murfy AI
• Reviewer comment handling Revises papers based on reviewer feedback.Found in Murfy AI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted literature sources, uploaded drafts, research data and reviewer comments

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted literature sources

4. 3

   Uploaded drafts

5. 4

   Research data and reviewer comments

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Researcher-approved manuscripts, figures and reference lists linked to their sources

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. Final statistical interpretation, authorship decisions and submission remain with the researcher. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Research brief and source selection, Editable evidence workspace, Reviewed deliverable and export. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, data, constraints and comments. Let users compare outline, draft and revised versions side by side. Display draft, changes requested and approved states. Provide a shared review link with comments anchored to the relevant passage, figure or reference. Make the task-specific outcome researcher-approved manuscripts, figures and reference lists linked to their sources visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, shared 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

Researcher-owned drafts, authorized data sets and permitted literature sources. Reference managers, cloud storage, LaTeX editors and publishing 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

   6 days

   One buyer segment, one recurring use case; first modules: search permitted academic sources for relevant papers; summarize selected papers with source links; extract themes and search scopes from uploaded drafts. 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 academic writers producing papers, reports and presentations use it to solve "literature search, citation management, writing, analysis and figure creation sit in separate subscriptions, so sources, data and drafts drift apart and reviewer feedback is hard to trace"?
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 manuscript sections per research hour and reference corrections after submission.
4. Measure, then decide. Track accepted manuscript sections per research hour and reference corrections after submission; 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 research domain and one manuscript format; final statistical interpretation, authorship decisions and submission remain with the researcher. Implement one approved input format, a bounded representative case set and the first three task modules: search permitted academic sources for relevant papers; summarize selected papers with source links; extract themes and search scopes from uploaded drafts. Support the remaining modules with operator review. 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 researcher-approved manuscripts, figures and reference lists linked to their sources. Retain the explicit scope boundary: One research domain and one manuscript format; final statistical interpretation, authorship decisions and submission remain with the researcher.

What the build depends on. Source upload and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity statistical work requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One research domain and one manuscript format; final statistical interpretation, authorship decisions and submission remain with the researcher.

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 research package. Offer a monthly production allowance after repeat demand. Quote complex data analysis or specialist figure work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded researcher-approved manuscript, figure set and reference list. 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 tool switching and reference errors while keeping the researcher's judgment in control. Demonstrate a concrete researcher-approved manuscript, figure set and reference list 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 producing papers, reports and presentations professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant academic or practitioner events.

Lead magnet

A reviewed sample researcher-approved manuscript section, figure and reference list from a small authorized input set, with a transparent calculation of accepted manuscript sections per research hour and reference corrections after submission and no promised savings.

The first 30 days

1. Week 1: interview five researchers, research groups and academic writers producing papers, reports and presentations and inspect a recent example of literature search, citation management, writing, analysis and figure creation sitting in separate subscriptions.
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 manuscript sections per research hour and reference corrections after submission, 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 manuscript sections per research hour and reference corrections after submission. 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 manuscript sections per research hour and reference corrections after submission; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs researcher-approved manuscripts, figures and reference lists linked to their sources. 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 source sets, analysis 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 academic writers producing papers, reports and presentations. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

SciSpace Agent, Ressearch AI, SciFocus, Chirpz Agent, Bibby AI, Spine Research, Chirpz, Murfy AI and Ontosight.ai, plus manual reference managers and general writing tools. Compare this product with the buyer's present method on accepted manuscript sections per research hour and reference corrections after submission. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, data processing, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of researcher-approved manuscripts, figures and reference lists linked to their sources. 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 submission scope. One research domain and one manuscript format; final statistical interpretation, authorship decisions and submission remain with the researcher. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.