Your thesis,
defended on real data.
From protocol to submission. Every citation verified. Your patient data is processed by Python, never by AI.
Made by a resident of Community Medicine.
- PubMed
- Europe PMC
- OpenAlex
- PubMed Central
- Unpaywall
Watch eight agents draft your thesis, live.
A dashboard, not a chat. Every agent's progress is visible, and every output waits for your approval before the next step begins.
Start with the tools. They cost nothing.
The methodology questions your ethics committee will ask first, worked out properly with the calculation shown, and a paragraph you can paste straight into your protocol.
Most tools stop at the protocol.
We start where your data begins.
Synopsis generators write a literature review and call it done. Your thesis lives or dies on the chapter they can't touch: Results.
- Protocol and synopsisPythesis
- Data collectionYou
- Results from real dataOnly Pythesis
- Discussion vs literatureOnly Pythesis
You approve every step.
Nothing is generated behind your back. Eight specialised agents do the work, but every output waits for your sign-off.
Start from your protocol
Upload your approved synopsis. The engine reads every field, and you review and correct it before anything runs.
Approve every decision
Outline, cleaning, variable mapping, chart style. Nothing is written until you sign off.
It writes, then verifies
Eight agents draft chapter by chapter. Every citation is checked against the source record.
Download, submit, defend
A formatted DOCX with the highlighted source PDFs and an originality report.
Built like a research engine.
Three guarantees that separate a verifiable thesis from a fluent hallucination.
| Patient | Age | Hb (g/dL) | Parity |
|---|---|---|---|
| PT-001 | 24 | 9.2 | Primi |
| PT-002 | 29 | 10.8 | Multi |
| PT-003 | 31 | 7.6 | Multi |
| PT-004 | 22 | 11.1 | Primi |
| Parity | Mild | Moderate | Severe |
|---|---|---|---|
| Primigravida | 30 | 17 | 5 |
| Multigravida | 24 | 32 | 12 |
The association between parity and severity of anaemia was statistically significant (χ² = 6.12, p = 0.047).
The questions your guide will ask.
No fine print. You stay the author of your work, and Pythesis is the research assistant that earns the citation.
Are the references actually real?
Yes. The engine searches PubMed, Europe PMC and OpenAlex live, then verifies every citation against the source record. Anything it can't confirm is flagged in your originality report. We never paper over a fabricated reference.
Is this plagiarism? Do I stay the author?
You remain the author of your work. Pythesis drafts from real, cited literature and your own data, runs an originality and AI-likeness check, and gives you full control to edit every line. It is a research and writing assistant, not a way to pass off someone else's work.
Does it work with my own hospital data?
That's the whole point. Upload your Excel sheet and the engine de-identifies, cleans and analyses it with Python, not AI, then writes a Results chapter with real tables, charts and statistics. Most tools stop at the protocol. We start where your data begins.
Is my patient data safe?
Yes. Identifiers are removed before anything else runs, and cleaning and statistics are done by Python on servers in Mumbai. Where an AI step needs to understand your columns, it sees only column names and summary statistics (averages, percentiles and category counts), never an individual patient's values. The server will not run any AI step on your data until de-identification has been applied.
What does the multi-agent task force do?
Eight specialised agents handle different parts of your thesis: a research harvester, data cleaner, statistics engine, humanizer, plagiarism guard, writer, formatter and verification agent. Each does one thing well, and you approve their output at every stage.