Česky

OPENAI BUILD WEEK · DEMONSTRATION PROTOTYPE

AI Advocate for the Poor

Structure, memory, and the ability to act for people who would otherwise face the system alone.

PROJECT MISSION

From thousands of fragmented items to an auditable case

A person with limited money or health may have a legitimate claim and enough evidence, yet still fail because they lack the resources, time, or professional capacity to organize their case.

AI Advocate is designed to compensate for this disadvantage. It turns fragmented legal and life materials into a structure of people, institutions, proceedings, events, claims, and evidence. It can then gather the relevant sources within a selected branch and prepare a proposed next step for human review.

fragmented materialsauditable structurerelevant branchproposed action

This does not replace a lawyer. It helps a person reach the point where a lawyer, legal-aid organization, ombudsperson, or public authority can actually help.

A CONCRETE USE CASE TODAY

A 2004–2026 European case as a network of institutions, proceedings, and remedies

The prototype displays an anonymized connection from the research program to the President, Czech courts and public prosecutors, ministries, police and forensic bodies, the CJEU, European Commission, and ECtHR. It distinguishes indexed material from creator-stated claims that still require an anonymized primary source.

Privacy in this demo: selected files are processed only in your browser. They are not uploaded and disappear when the page is refreshed.

EVERYDAY POTENTIAL

A person brings a box of records — the system returns an understandable path

A prisoner’s family, a disabled person, a senior, a debtor, or a parent dealing with an authority may fail not because evidence is absent, but because they cannot find it in time, connect it to the right proceeding, and explain it to a professional.

documents or speech in the user’s languagesafe anonymizationtimeline and evidence traffic lightreview by a lawyer or advice service

Outcome: deadlines, institutions, unanswered arguments, and cited passages, with the proposed next step remaining under human review. Voice input, OCR, automatic anonymization, and universal analysis are product vision; the current public prototype safely demonstrates the principle on one supported sample.

CONTROLLED PRE-SUBMISSION TEST

Citizen XY’s archive already exists. One new document is being added.

2004–2026 archivenew referral noticedeadline and relevance checkupdated decision graph

This button runs the first internal test. A separate outside-PDF test and safe text mapping appear below.

REAL-WORLD PROJECT OUTPUT

Documents sent on 19 July 2026

Created within AI Advocate for the Poor through collaboration between Mgr. Dušan Dvořák and Codex. At the author’s request, each document is published in its sent form, including his identifying details. The analytical layer identifies other people only by public role; the linked PDFs preserve the form in which the documents were actually sent.

BEFORE THE TEST
Drafts dated 18 July 2026

The new referral notice had not yet been safely propagated through all three documents, and some wording treated a prepared draft as already filed or evidenced.

AFTER THE TEST
Outputs sent on 19 July 2026

The system created a new procedural node, assigned document-specific relevance, limited the meaning of the referral, and corrected document status. The three sent outputs appear below.

SECOND TEST — PUBLIC INPUT

A safe outside-input test — the PDF stays on your computer

Files are processed only in the browser and are never uploaded. The exactly supported court PDF is recognized by its digital fingerprint and produces an anonymized supervisory-board change analysis. An unknown PDF is safely rejected. Other text receives only a technical metadata map without inheriting prepared legal conclusions.

Open the anonymized evidentiary extract PDF