osint-investigation

To cross-reference public records, you need a framework that links entities across disparate datasets like SEC EDGAR, USAspending, and OpenCorporates using normalized matching tiers. This approach enables automated entity resolution and verifiable evidence chain construction for due diligence.

Yes, you can perform statistical correlation and timing analysis to identify suspicious patterns between events like lobbying filings and government contract awards. This mechanism helps investigative journalists uncover potential conflicts of interest through verifiable data patterns.

Evidence chain construction is the process of generating structured findings that trace every claim back to specific source rows. This ensures transparency and auditability in investigative work by linking entities across government contracts, corporate registries, and court filings.

No, you do not need external libraries. The multi-source entity resolution and data normalization tasks require only the Python standard library. This lowers the entry barrier for setting up the environment for public records investigation.

You resolve entities across SEC filings and federal contract data by applying normalized matching tiers to link records from SEC EDGAR and USAspending. This process identifies potential conflicts of interest by cross-referencing corporate filings with federal contract awards.