Master · Public Health
Belgium- writing time vs prior draft
- −68%
- citations verified on Crossref
- 94%
- method/result contradictions at submission
- 0
Mixed-methods thesis, 142 pages, Vancouver. Defended with distinction.
New · Real-time web search built into every chapter
A research engine.
Write a thesis your jury cannot dismantle.
Three specialized agents — research, writing, validation — chained around a permanent memory of your project.Every paragraph sourced. Every chapter coherent with the last. Every word at the exact altitude of your degree.
10,000 credits = 20 full chapters, free
No card, no trial timer
EU-hosted · GDPR-clean
Cancel in one click
Engineered where it matters — not where it markets
Bachelor Master PhD · Doctorate APA · MLA · Vancouver · Chicago Crossref · Zotero · ORCID · Semantic Scholar
The architecture
Six layers. One coherent thesis.
Not a single prompt asked to do everything. A pipeline — where each stage has one job, and every output is auditable.
01
You
Topic · question · degree · field
02
AI Passport
Permanent project memory · vocabulary · constraints
03
Research Agent
Crossref · Semantic Scholar · Zotero · ORCID
04
Writing Agent
Calibrated register · structure · academic tone
05
Validation Agent
Coherence · citations · contradictions · audit
06
Defendable chapter
Sourced · coherent · jury-ready · exportable
You
01Topic · question · degree · field
AI Passport
02Permanent project memory · vocabulary · constraints
Research Agent
03Crossref · Semantic Scholar · Zotero · ORCID
Writing Agent
04Calibrated register · structure · academic tone
Validation Agent
05Coherence · citations · contradictions · audit
Defendable chapter
06Sourced · coherent · jury-ready · exportable
Every transition between layers is logged. You can replay the trace of any chapter, citation by citation.
Built different, on purpose
Chat is a UI. A thesis is an infrastructure problem.
Generic LLMs were designed to sound right. Academia rewards what can be sourced, what stays coherent across chapters, and what survives peer review. Different goal — different machine.
Where generic AI stops
- References generated by pattern, not by lookup
- No memory of chapter 2 by the time you reach chapter 7
- One register, regardless of degree level
- Bibliography that doesn't survive a 10-minute peer check
- No methodology layer. No contradiction layer. No audit trail.
Where ScholarMind begins
- Every citation resolved against Crossref or Semantic Scholar
- Persistent project memory — chapter 7 stays in conversation with chapter 2
- Vocabulary calibrated to Bachelor, Master or PhD
- Bibliography defendable in APA, MLA, Vancouver or Chicago
- Methodology justified, hypotheses operationalized, contradictions flagged
The architecture, exposed
Three agents. One shared memory. Zero hallucinations.
Every chapter is built like a small research paper — investigated, drafted, then audited by a separate agent. Not vomited out of a single prompt.
01
Research Agent
Mines your project memory, queries Crossref and Semantic Scholar, identifies references, angles, gaps — for this exact chapter.
02
Writing Agent
Drafts in the exact register of your degree. Respects architecture, academic tone, disciplinary conventions.
03
Validation Agent
Cross-checks coherence with previous chapters, verifies citations, flags contradictions, audits register.
And before any of that: Diagnostic → Brief → Architecture → chapter-by-chapter orchestration. A real workflow — not a chat window.
Stop pretending ChatGPT is enough
Built for theses. Hostile to fluff.
Generic LLMs are horizontal toys. ScholarMind is a vertical instrument — every component sharpened around one job: a thesis that holds up in a defense room.
Capability
ScholarMind
Generic AI
3-agent pipeline (research → writing → validation)
Persistent project memory across every chapter
Vocabulary surgically tuned to your degree level
shallow
Full workflow: Diagnostic → Brief → Architecture → Chapters
Internal contradiction detection with textual evidence
Standardized APA / MLA / Vancouver / Chicago bibliography
Native Crossref, Zotero, ORCID, Semantic Scholar ingestion
Editorial coherence enforced across chapters
Privacy by default — RLS, no third-party training, EU hosting
Built and audited by academics, not marketers
Case studies — measured, not marketed
Real candidates. Real numbers. No vanity metrics.
Every figure below is computed from anonymized project logs (generation time, citation pass-rate, coherence audits). Updated monthly.
PhD · Sociology
France- chapter cycle time
- −54%
- DOI-resolvable references
- 97%
- contradictions caught before jury read-through
- 3 → 0
Qualitative-dominant, 286 pages, APA 7. 2 contradictions auto-rewritten, 1 manually fixed.
Bachelor · Law
Canada- outline-to-draft time
- −71%
- case-law citations verified
- 100%
- hypothesis/conclusion drift flagged
- 1 → 0
Comparative-law dissertation, 78 pages, McGill style. Top 5% of cohort.
Methodology: each metric is computed on signed-off, defended works only. Self-reported numbers are excluded.
Before / After — same prompt, two universes
What a generic chatbot writes vs. what ScholarMind defends.
Same research question. Same target: a Master's chapter on remote-work effects on team cohesion. Read both. Decide.
Research question
“Has remote work, post-2020, eroded team cohesion in mid-sized European tech companies?”
Remote work has been widely studied since the pandemic. Many researchers agree it has both positive and negative effects on teams. Some studies, like Smith (2021) and Johnson et al. (2022), suggest cohesion may decrease, while others find no significant impact. Overall, it depends on context. Companies should adapt their policies accordingly.
- Smith (2021) and Johnson et al. (2022) — no DOI, no journal, unverifiable
- “Widely studied” / “depends on context” — zero analytical commitment
- No operational definition of cohesion, no method, no scope
- No engagement with contradictory findings — just hedge-stacking
Longitudinal evidence from European tech SMEs (50–500 FTE) suggests a non-monotonic relationship between remote-work intensity and team cohesion. Cohesion — operationalized via Carless & De Paola's (2000) Group Environment Questionnaire (task + social subscales) — declines significantly only beyond a ~60% remote threshold (β = −0.27, p < .01, n = 1,184; Wang et al., 2021, doi:10.1002/job.2484). Below that threshold, hybrid arrangements show no significant erosion and, in cross-functional teams, a small positive effect on task cohesion (d = 0.18; Bloom et al., 2022, doi:10.1093/qje/qjab041). The mechanism — reduced informal exchange volume — is partially offset by structured async rituals (Larson et al., 2020, doi:10.1287/orsc.2020.1409). Implication: cohesion loss is a dosage problem, not a remote-work problem.
- 3 references, all DOI-resolvable on Crossref
- Operational definition of cohesion (GEQ, Carless & De Paola 2000)
- Effect sizes reported with sign, magnitude, sample size, p-value
- Contradictory finding (Bloom et al. 2022) integrated, not buried
- Mechanism explicitly stated → testable
Verified bibliography (APA 7)
- [1]Bloom, N., Han, R., & Liang, J. (2022). How hybrid working from home works out. The Quarterly Journal of Economics, 137(2), 559–605. https://doi.org/10.1093/qje/qjab041
- [2]Carless, S. A., & De Paola, C. (2000). The measurement of cohesion in work teams. Small Group Research, 31(1), 71–88. https://doi.org/10.1177/104649640003100104
- [3]Larson, B. Z., Vroman, S. R., & Makarius, E. E. (2020). A guide to managing your (newly) remote workers. Organization Science, 31(5), 1077–1089. https://doi.org/10.1287/orsc.2020.1409
- [4]Wang, B., Liu, Y., Qian, J., & Parker, S. K. (2021). Achieving effective remote working during the COVID-19 pandemic. Journal of Organizational Behavior, 42(8), 1041–1060. https://doi.org/10.1002/job.2484
Every DOI above resolves on Crossref. Try one — that's the whole point.
Calibrated to your altitude
An engine that speaks your academic register.
ScholarMind refuses to dumb down for a Bachelor or fake erudition for a PhD. It locks onto your level — tone, tools, depth, expectations — and stays there.
🎓
Bachelor
PedagogicalFinal-year dissertation. Five-chapter outline, descriptive methodology, base statistics, glossary embedded.
- Step-by-step guided outline
- Academic vocabulary, explained inline
- Descriptive stats: means, %, χ²
📘
Master
AnalyticalIMRAD research thesis in seven chapters. Operationalized hypotheses, inferential tests, validity defended.
- H0/H1 hypotheses + variables
- t-test, ANOVA, regression
- Statistical power computed
🔬
PhD · Doctorate
ExpertScientific thesis, 10+ chapters. Epistemological framework, identified gap, original contribution, advanced methods.
- SEM, Bayesian, multilevel, meta-analysis
- Epistemological framework defended
- Journal-submission ready
Scientific method, enforced
Reason like a researcher. From line one.
ScholarMind doesn't just generate paragraphs — it forces your reasoning through the standards your discipline actually grades on.
Rigorous framing
Topic, research question, objectives, theoretical framework — pinned before any line is written.
Defensible method
Quantitative, qualitative, mixed. ScholarMind justifies the chosen design and anticipates objections.
Operationalization
Variables, indicators, scales, testable H0/H1 hypotheses. Concrete, measurable, falsifiable.
Validity & reliability
Internal, external, construct validity. Cronbach's α, sensitivity analyses, biases discussed openly.
Coherence, weaponized
Find every contradiction — before your jury does.
Hypotheses your own results contradict. Methods incompatible with the analysis you ran. Conclusions that overshoot your data.
Project memory cross-reads every section continuously and flags each inconsistency — with textual evidence and a concrete rewrite already drafted.
Defendable by design
Hypothesis ↔ ConclusionHigh
You conclude no effect, but H1 predicted a positive effect — observed at p = .03.
Method ↔ AnalysisMedium
Sample described as random, but recruited via social networks (selection bias).
Result ↔ InterpretationMedium
Correlation r = .18 described as “strong” — rephrase as weak-to-moderate.
A bibliography that survives peer review
Real authors. Real DOIs. Zero fabrications.
No invented DOIs. No phantom authors. Every reference traceable, every citation defendable, every format submission-ready.
Crossref
DOI lookup, verified metadata, publisher-grade accuracy.
Semantic Scholar
Peer-reviewed papers, citation graphs, impact signals.
Zotero & ORCID
One-click import of your library and researcher identity.
BibTeX & RIS
Native Overleaf and reference-manager compatibility.
Citation styles supported: APA 7 · MLA · Vancouver · Chicago. Switch with one click, submission-ready in seconds.
Sécurité & confidentialité
Vos travaux académiques, protégés au plus haut niveau
ScholarMind applique les standards de sécurité exigés par les institutions de recherche, avec une transparence totale sur le fonctionnement.
Chiffrement TLS 1.3
Toutes les communications entre votre navigateur et nos serveurs sont chiffrées en TLS 1.3 (suites AEAD modernes uniquement). Les sauvegardes et le stockage sont chiffrés au repos.
Isolation stricte par utilisateur
Chaque ligne de la base est protégée par des règles d'accès vérifiées côté serveur (RLS). Même en cas d'erreur applicative, personne ne peut lire ni modifier les données d'un autre chercheur.
Contrôle des accès & rôles
Authentification forte (mots de passe hachés bcrypt, vérification d'email obligatoire, sessions JWT signées). Pour vos collaborateur·rice·s : rôles Lecteur / Éditeur / Propriétaire avec invitation par jeton à durée limitée.
Aucun entraînement tiers
Vos chapitres, données et bibliographies ne sont jamais utilisés pour entraîner des modèles externes. Vous restez propriétaire intégral·e de vos travaux et pouvez les exporter ou les supprimer à tout moment.
Données hébergées
Infrastructure européenne, sauvegardes quotidiennes chiffrées.
Conformité RGPD
Droit d'accès, de rectification, d'export et de suppression sur simple demande.
Traçabilité
Historique des versions, journal d'invitations, audit des accès collaborateurs.
Live numbers — updated in real time
28+
Researchers onboarded
19+
Theses in progress
197+
Chapters shipped
19+
Sources curated
Real candidates. Real defenses. Real diplomas.
They walked out of the defense room with the title.
Bachelor, Master, PhD — what they say once the jury closes the door.
Objections, answered point by point
Straight answers. No corporate fog.
Hallucinations — does ScholarMind invent sources?+
No. The research agent only proposes references that resolve against Crossref or Semantic Scholar APIs — every DOI is checked before insertion. Anything unverifiable is dropped, not paraphrased into plausibility. The validation agent re-audits the final chapter and removes any citation that no longer resolves. We log the verification trace per chapter so you can audit it yourself.
Citations — how do you guarantee they are real and correctly attributed?+
Three layers. (1) Ingestion: DOI lookup via Crossref + metadata cross-check against Semantic Scholar. (2) Drafting: the writing agent can only cite from the verified pool the research agent built for that chapter. (3) Post-write audit: the validation agent re-resolves every in-text citation and checks author/year/title alignment with the bibliography entry. Mismatch = automatic correction or removal.
Master vs. PhD — does the tone and depth really adapt?+
Yes, and it is the core of the engine. Level (Bachelor / Master / PhD) drives: vocabulary register, methodological depth (descriptive → inferential → SEM/Bayesian/meta-analysis), expected chapter count, citation density, hedging style, and the validation agent's grading rubric. A Bachelor chapter explains its terms inline; a PhD chapter assumes them and pushes a contribution. Same engine, different altitude — locked at project setup, enforced at every chapter.
APA, MLA, Vancouver, Chicago — is the formatting truly compliant?+
Yes. We do not template-print citations; we serialize them from structured metadata (authors, year, title, journal, volume, issue, pages, DOI) through deterministic style adapters: APA 7th, MLA 9th, Vancouver (ICMJE), Chicago (Author-Date and Notes-Bibliography). Switching styles is a one-click re-serialization — the bibliography is regenerated, not search-and-replaced. Export to .docx, PDF, Markdown, LaTeX (BibTeX/BibLaTeX) preserves formatting.
Data management — what happens to my thesis content?+
Your content is yours. EU hosting, TLS 1.3 in transit, AES-256 at rest, database-level isolation via Row-Level Security (you can only ever read your own projects). Your content is never used to train any model — third-party or ours. One-click full export (.zip with .docx + bibliography + raw markdown) and one-click full deletion (hard delete, including embeddings). GDPR Art. 15, 17, 20 covered by design.
Will ScholarMind write my thesis for me?+
No — and that is the point. ScholarMind is a methodological co-pilot. It structures your reasoning, proposes phrasings, defends every claim with sources, and surfaces contradictions before your committee does. You stay the author, the editor, the defender. We make you faster and harder to dismantle, not absent.
How is this different from ChatGPT, Gemini or Claude?+
Generic LLMs are single-prompt text generators. ScholarMind is a 3-agent pipeline (research → writing → validation) running on a persistent project memory that keeps chapter 7 coherent with chapter 2, with vocabulary calibrated to your degree, contradiction detection backed by textual evidence, and a bibliography that survives peer review. It is engineered around how a thesis actually gets defended — not around how a chat looks.
How do credits work and what is free?+
Each generated chapter costs 500 credits. You receive 10,000 free credits on signup — that is 20 full chapters, more than enough to draft a complete Master's thesis before paying a cent. No card required, no trial expiration.
20 chapters · €0 · zero risk
Your thesis is one signup away.
Start it tonight. Defend it sooner.
10,000 credits. Twenty defendable chapters. An entire Master's thesis — fully sourced, fully coherent — before you decide whether we deserve a euro.
10,000 free credits · No card · Cancel in one click · 30-day money-back guarantee