Our Technology
How AI & Vector Databases Empower Evidence
Nora's Law uses cutting-edge AI technology and vector databases to help individuals navigate constitutional rights with precision, accuracy, and accountability.
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Retrieval-Augmented Generation (RAG)
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI technique that combines information retrieval with generative language models. It works by first retrieving relevant documents or evidence from a knowledge base, then using that information to generate accurate, grounded responses. In Nora's Law, RAG ensures that our AI legal navigator always bases its analysis on your actual case documents and constitutional law, not just general training data.
History & Development
RAG was developed by researchers at Facebook AI Research (FAIR) in 2020 as a way to improve the accuracy and groundedness of AI language models. The technique gained significant attention because it solves a critical problem: traditional AI models can "hallucinate" or generate plausible-sounding but incorrect information. RAG prevents this by anchoring AI responses to actual documents.
How RAG Works in Nora's Law
- Document Storage: Your evidence (documents, recordings, statements) is securely stored with metadata
- Retrieval: When you ask a question, RAG searches for relevant documents matching your query
- Augmentation: The AI legal navigator uses these retrieved documents plus constitutional law precedents to generate responses
- Generation: The AI produces analysis grounded in YOUR actual evidence, not hypotheticals
Why This Matters for Civil Rights: In family court proceedings, evidence accuracy is everything. RAG ensures that Nora's Law never makes recommendations based on fabricated data or hallucinations. Every analysis is traceable back to your documents and established law.
Vector Databases
What is a Vector Database?
A vector database stores data as vectors—multi-dimensional numerical representations of text, images, or other information. Instead of searching by keywords, vector databases search by meaning and similarity. This is revolutionary for legal analysis because it allows the system to understand the intent and context of your case documents, not just the literal words.
Google's Contribution to Vector Technology
Google pioneered much of the foundational research in vector embeddings through projects like Word2Vec and more recently BERT (Bidirectional Encoder Representations from Transformers). These innovations allow computers to understand semantic relationships in text—meaning the AI can recognize that "constitutional violation" and "denial of due process" are related concepts, even if they use different words.
Open Source Evolution
Recently, vector database technology has become increasingly open source. Nora's Law uses Qdrant, an open-source vector database, to ensure:
- Transparency: You can verify how your data is stored and searched
- Independence: We're not locked into proprietary systems that could change terms or pricing
- Community: Continuous improvements from a global developer community
- Privacy: We control the infrastructure entirely—no third-party access
For Evidence Integrity: Vector databases allow us to detect when similar documents or patterns appear across multiple cases, exposing institutional patterns of abuse that might otherwise remain hidden.
đź“Š Pattern Detection
Vector databases excel at identifying patterns across large datasets. In Nora's Law, this means detecting when the same judge, social worker, or agency appears in multiple cases with similar patterns of constitutional violations.
How Pattern Detection Works:
- Vector database encodes all case documents as multi-dimensional vectors
- AI analyzes similarities between cases (allegations, procedures, evidence handling)
- When patterns emerge across 3+ cases, the system flags them
- Users can see which institutions/individuals show repeated violations
- This evidence becomes powerful in court: no longer "isolated incidents"
Why This Changes Everything: Institutions can claim that one case involved innocent mistakes. But when the same procedural errors, evidentiary failures, or rights violations appear repeatedly across multiple families, the evidence becomes undeniable.
đź“‹ Evidence Repository
The Evidence Repository stores your documents with cryptographic timestamps and integrity verification. This ensures that courts can verify your evidence hasn't been altered since it was uploaded.
- Immutable timestamps: Cryptographic proof of when evidence was created
- Chain of custody: Complete audit trail of who accessed what and when
- Integrity verification: Proof that documents haven't been modified
- Legal admissibility: Properly formatted for court proceedings
đź”’ Secure & Private
End-to-end encryption ensures that your case documents remain confidential. We use military-grade encryption, and we never access or analyze your evidence—only you and those you authorize can see your case.
Your Privacy is Our Foundation
In CHIPS proceedings and family court cases, confidentiality is essential. Nora's Law treats your data with the highest level of security. We don't sell data, we don't share with third parties, and we don't use your evidence for purposes other than supporting your case.
Technology for Constitutional Rights
RAG, vector databases, and AI don't replace lawyers or courts. Instead, they level the playing field. Families that can't afford expensive legal teams can now access the same evidence analysis, pattern recognition, and constitutional guidance that was once only available to the wealthy.
When every violation is documented, timestamped, and legally actionable—when patterns of abuse are exposed across multiple cases—corruption becomes too costly to commit.