I'm actually a lawyer by trade, not a full-time developer.

I built Metot because generic AI summaries are often useless for academic rigour. They gloss over the logic. As a lawyer, I needed to see the skeleton of an argument, not a blurb.

So, I spent the last few months iterating on system prompts and structured output constraints to force LLMs to act as analytical engines rather than creative writers.

The "AI" difference: Instead of asking the model to "write about this," Metot uses a multi-stage prompting pipeline to:

Deconstruct Logic: It extracts the Thesis -> Premises -> Evidence chain (Argument Mapping).

Analyze Metadata: It identifies methodological contributions and citation networks (Lit Review).

Review Tone: It acts as a strict academic referee for style and consistency (Text Review).

The Output (Screenshots): I tested it on Judith Thomson’s The Trolley Problem to show it works outside of law. You can see how the tool extracted the specific "Distributive Exemption" argument structure and the "Loop Case" analysis in these screenshots: https://imgur.com/a/1EMysz1

Why I’m posting here: Since my background is strictly legal, I need feedback from researchers in STEM and Social Sciences:

Does this "Argument Mapping" structure hold up for your field's papers?

Where do the LLM hallucinations creep in for your specific domain?

Privacy Note: No user data is used for model training.

Invite Code: Use HACKERNEWS to skip the waitlist.

Link: https://metot.org