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Case study / Project archive
NeuralVault
A legal-research retrieval prototype exploring dense, lexical, and graph-based context.
Project archive
Historical project; AI-assisted development. Individual and team contribution has not been independently verified.
The idea
The problem
Explore legal-research context retrieval across semantic matches, lexical matches, and graph relationships; the matching historical source is still needed to verify those paths.
The implementation
What I built
This preserves the earlier retrieval idea and declared stack. Exact implementation scope and personal task attribution require the matching legal-research source.
- Python
- PostgreSQL
- Neo4j
Features
- Archived concept: retrieve legal-research context through dense, lexical, and graph-based approaches.
- The retained stack names PostgreSQL and Neo4j; working retrieval behavior has not been verified for this version.
Architecture
- The legal-research version is described as Python retrieval over relational and graph stores; its actual retrieval and ranking code is unavailable for review.
- Local NeuralVault folders instead contain database-intelligence demos. Their SQL and fraud modules are not evidence for the legal-research version.
Validation and measured results
- No matching legal-research implementation, retrieval evaluation, or citation-quality measurement was established in this review.
Limits and unfinished work
- No legal-answer accuracy, corpus coverage, or retrieval improvement is claimed. The similarly named database demo is a separate version.
- Older source can contain superseded descriptions. Numerical impact, adoption, awards, and deployment claims are not carried into this entry.
Evidence and sources
This project’s evidence remains local. Public source and downloadable receipts are not attached yet.