How Daisy Understands Legal Text
Daisy uses advanced AI techniques to understand and process legal information. This page explains in simple terms how our technology works behind the scenes.
What is Vector Search?
When Daisy processes legal text, it converts words and phrases into mathematical representations called "vectors." This allows our AI to understand the meaning behind the text, not just match keywords.
Strict Data Isolation Guarantee
Daisy maintains a rigorous approach to data security in our vector search implementation. Only freely accessible websites (such as those from legislative bodies, public regulations, and open legal resources) are vectorized and stored in our shared database. This public information is available to all clients to provide a foundation of legal knowledge.
However, when it comes to your organization's data, we implement complete isolation at every level. When users upload their own assets, generate documents through our platform, or provide any other proprietary data, the vectorization happens in a database instance specifically created for your organization alone.
This isolation isn't merely a software-level security measure—it's a physical separation. Each client's vector database exists as a completely separate instance with dedicated resources, ensuring that no other organization can, under any circumstances, access your vectorized data. Even in the unlikely event of a security incident, the physical separation of database instances creates an impenetrable barrier between your data and other clients.
Your legal documents, proprietary information, and generated content remain exclusively yours, with database-level isolation that exceeds industry standards. This approach reflects our commitment to not just meeting but exceeding the strictest data privacy and security requirements in the legal technology sector.
Why This Matters For You
This technology enables Daisy to:
- Find Relevant Information: Even when your query doesn't use the exact same words as the documents
- Understand Context: Grasp the meaning behind legal terms and provisions
- Provide Better Results: Deliver more accurate and comprehensive responses
Examples in Action
Document Generation
When generating a legal document, Daisy uses vector search to:
- Find the most relevant clauses for your specific situation
- Ensure compliance with the latest regulations
- Include all necessary legal provisions
Legal Research
When researching a legal question, Daisy:
- Understands the intent behind your question
- Searches through vast legal databases using vector similarity
- Returns the most relevant information, not just keyword matches
DPO-Specific Use Cases
As a Data Protection Officer working in Europe, Daisy's vector search capabilities transform your daily workflow:
GDPR Documentation Management
Scenario: You need to quickly locate where specific technical measures are documented across multiple policies.
Without Vector Search: Manually open and search through dozens of documents using Ctrl+F with multiple keyword variations, hoping to find the relevant sections.
With Daisy: Simply ask "Where are our technical measures for data encryption documented?" and our vector search immediately identifies all relevant passages across your entire documentation landscape, even if they use different terminology.
DPIA Analysis and Comparison
Scenario: You need to verify if your latest Data Protection Impact Assessment addresses all necessary technical and organizational measures.
Without Vector Search: Create a checklist of requirements and methodically review the entire DPIA, potentially missing conceptually related sections that use different phrasing.
With Daisy: Ask "What technical organizational measures does our latest DPIA include for customer data?" and receive comprehensive results that understand the semantic relationship between your question and various document sections.
Cross-Border Data Transfer Compliance
Scenario: You're trying to recall which processor agreements mention specific Standard Contractual Clauses following Schrems II.
Without Vector Search: Spend hours reviewing each agreement individually, searching for explicit mentions of specific terms.
With Daisy: Query "Which processor agreements implement post-Schrems II safeguards?" and immediately identify all relevant documents, even those using different terminology to describe the same compliance measures.
Regulatory Update Impact Assessment
Scenario: After a new EDPB guideline is published, you need to understand how it affects your existing policies.
Without Vector Search: Manually cross-reference the new guidelines against each existing policy, a time-consuming process prone to oversight.
With Daisy: Ask "How does the new EDPB cookie guidance affect our current consent practices?" and receive an analysis that semantically links the new requirements to your existing documentation.
Audit Preparation
Scenario: You need to prepare for an upcoming data protection audit by gathering evidence of compliance from various documents.
Without Vector Search: Create a complex spreadsheet tracking where each compliance element is documented and manually locate each reference.
With Daisy: Request "Find all documentation related to our data retention procedures" and instantly receive comprehensive results from across your organization's document repository.
Benefits Over Traditional Search
| Traditional Keyword Search | Daisy's Vector Search |
|---|---|
| Matches exact words only | Understands meaning and context |
| Misses synonyms and related concepts | Recognizes related terms and concepts |
| Requires precise query wording | Works with natural language questions |
| Can't understand legal context | Specifically tuned for legal understanding |
How This Improves Your Experience
For developers integrating with our API, this means:
- More Accurate Results: Get relevant information even with imperfect queries
- Natural Interactions: No need to craft perfect keyword searches
- Contextual Understanding: The API understands the legal context of your requests
Technical Implementation
For those interested in the technical details:
- Daisy uses state-of-the-art embedding models to convert text to vectors
- We maintain a specialized vector database optimized for legal text
- Queries are processed through our proprietary similarity algorithm
- Results are ranked based on semantic relevance, not just keyword matches
Using Vector Search in Your Integration
You don't need to understand the details of vector search to use our API. It works automatically behind the scenes to power our document generation and research capabilities.
When you make an API call, our vector search technology ensures you get the most relevant and accurate results possible.
Looking Forward
We're constantly improving our vector search technology to make it even more accurate and efficient. As our models learn from more legal texts, the quality of our document generation and research capabilities will continue to improve.