Use Case | Semantic Searches in Public Records

Use Case: Semantic Searches in Public Records

General Description:
Managing public records can be challenging due to the vast amount of information they contain and the need to quickly locate specific data. A model that uses semantic searches allows for identifying critical information based on the meaning of the text, rather than relying solely on exact keywords. This optimizes processes like review, auditing, and inquiry in sectors such as government, legal, auditing, and journalism, enhancing efficiency and accuracy in managing records.

How It Works:

1. Uploading Public Records in PDF:
   Users upload documents such as licenses, permits, contracts, judicial resolutions, and government reports.
2. Automatic Content Segmentation:
   ○ The model organizes the information into key sections such as:
   ■ Relevant Dates: Issuance, expiration, important milestones.
   ■ Parties Involved: Names, positions, roles in the record.
   ■ Resolutions: Final decisions, penalties, agreements.
   ■ Evidence: Attached documents, references to proofs or testimonies.
3. Semantic Searches:
   ○ Users make specific queries such as:
   ■ “What resolutions are linked to construction permits in 2023?”
   ■ “Identify contracts with exclusivity clauses.”
   ■ “Find records related to labor claims.”
4. Summary Generation:
   ○ The model generates summaries including:
   ■ Relevant data according to the query.
   ■ A brief description of each related record.
   ■ Location of key information within the document.
5. Storage in Vector Database:
   ○ The records are stored in an organized manner, enabling fast searches and efficient access for future audits or reviews.

Practical Example:
Scenario:
A government office needs to review hundreds of records related to construction permits to ensure they comply with local regulations.
Process with the Model:

1. Uploading Documents:
   All construction permit records from the past year are uploaded to the system.
2. Segmentation by the Model:
   ○ The system organizes the records into:
   ■ Issuance and expiration dates.
   ■ Responsible parties: Builders, owners, inspectors.
   ■ Resolutions: Approvals, rejections, imposed sanctions.
3. Semantic Search:
   ○ Reviewers query:
   ■ “Records with penalties for non-compliance with zoning regulations.”
   ■ The system responds with:
   ■ Record 1: Penalty for exceeding the allowed height limits.
   ■ Record 2: Fine for starting construction without prior inspection.
4. Summary Generation:
   ○ The model generates a report that includes:
   ■ Summary of each record with detailed violation.
   ■ Relevant dates for follow-up.
   ■ Current status of the permit (approved, rejected, under review).
5. Report Output:
   The team receives a consolidated analysis that helps identify patterns of non-compliance and prioritize corrective actions.

Benefits of the Model in Public Records Information Search:

1. Fast and Accurate Location:
   ● Finds critical information in large volumes of documents in seconds.
2. Context-Based Searches:
   ● Performs searches based on meaning, allowing the identification of relevant data even if the exact wording is not present.
3. Intelligent Organization:
   ● Automatically segments records into key sections, facilitating access to the most important information.
4. Summary Generation:
   ● Provides a summary of relevant points, eliminating the need to review each full record.
5. Efficient Storage and Retrieval:
   ● Allows future searches in previously processed records, improving traceability and auditing.

Additional Applications:

1. Government Audits:
   ○ Verifies the validity of permits, contracts, and judicial resolutions to ensure regulatory compliance.
2. Legal Consultations:
   ○ Identifies legal precedents or specific resolutions related to a case under review.
3. Claims and Complaints Management:
   ○ Locates records linked to specific claims such as labor, environmental, or social issues.
4. Journalistic Research:
   ○ Finds critical information in public documents for reports and investigations.
5. Contract and Tender Management:
   ○ Analyzes contracts and associated records to identify relevant clauses or non-compliance.

Practical Example:
Additional Scenario:
A journalist investigates environmental regulations compliance in construction projects.
Without the model:
● The journalist manually reviews hundreds of documents, facing a slow and tedious process.
With the model:
● The system automatically segments and organizes the records related to construction projects, generating a report that highlights:
○ Record 1: Project approved but with deficiencies in waste management.
○ Record 2: Permit revoked for failing to comply with environmental impact studies.
○ Recommendation: Review projects in protected areas to identify patterns of non-compliance.

Conclusion:
Semantic search in public records through segmentation, summary generation, and vector databases optimizes the identification of critical information in lengthy and complex documents. This model improves efficiency, accuracy, and transparency in auditing, inquiry, and research processes, making it ideal for governments, businesses, lawyers, and journalists who manage large volumes of records.