xForCloBot- Wrongful Foreclosure Bot is a legal AI human assistant tool...for Homeowners side Plaintiffs Attorney
Multi-Criteria Decision Making (MCDM) has long been used in fields like supplier evaluation and risk assessment to translate human expert knowledge into reproducible, weighted decision systems. With the rise of large language models (LLMs), these structured evaluations can now be simulated, scored, and refined at scale.
xForCloBot bridges this paradigm in the legal domain - when a homeowner loses their property to the lender in a foreclosure proceeding. It replaces traditional high stakes legal intake subjectivity with a formal MCDM structure based on over 90 legally significant features ( legal signals), drawn from real trial and appeallet court cases and attorney workflows. These features form the foundation for both surrogate scoring and LLM prompt generation, allowing GPT-style agents to simulate junior associate case assessments and align with ground-truth judicial reasoning.
This hybrid model — combining human-expert legal reasoning, law logic ( legal basis to assess viable claim against facts) with LLM prompting techniques — offers a faster, explainable, and more consistent intake evaluation system for wrongful foreclosure triage.
Track all wrongful foreclosure cases in our dataset:
View the Case Tracker CSV
Learn more about xForCloBot's scientific framework, case intake architecture, and decision support system by visiting the xForCloBot Wiki.
This project is licensed for non-commercial academic research only.
All other rights are reserved. For colloboration, contact: Pradeep Kumar
If this project helps your research, legal workflows, or inspires your own legal-AI builds — drop a ⭐️!