SKILL / Relationships
Plans: AgencyCross-Entity Correlation
Research and assess relationships across people, organizations and digital identifiers, distinguishing direct evidence from inference.
Read the step-by-step user guideCross-Entity Correlation answers the question at the center of complex cases: how are these people, organizations and digital identifiers connected, and what does the evidence show? Give Nexus the entities under investigation — including digital identifiers such as usernames or domains — and the Skill researches the public record around them and assesses their relationships. Each relationship carries its supporting evidence and is labeled as directly supported or inferred, so a shared address is never silently promoted to a business partnership.
What you get
Evidence-backed relationship findings between entities, ready to add to the case graph.
Using Cross-Entity Correlation in an investigation
The output is built for the case relationship graph. Supported relationships can be added to the graph directly from their evidence records, so the graph grows from inspectable findings and every edge can be traced to its source. Inferred relationships are kept visibly distinct, and connections the evidence does not support are recorded as information gaps rather than presented as findings, because knowing what could not be established is part of the assessment. Any entity in the graph can become the subject of a follow-up Skill in the same case, and the new findings join the same picture.
Cross-Entity Correlation is an Agency-plan agentic OSINT Skill intended for multi-subject investigations: corporate networks in due diligence, fraud rings that reuse identifiers, threat actors who leave traces across domains and wallets, or a person whose several public identities need to be tied together carefully. It works best after the entity-specific Skills have populated the case with evidence. The result is an assessment of relationships, not a finding of ownership, control or wrongdoing; those conclusions remain with the investigator and the evidence.
Example objectives
Run the Skill directly, or describe the objective in your own words — these are the kinds of questions it is built to answer.
Ask Nexus
“Which of these five companies and three directors are publicly connected, and which connections rest on direct evidence rather than inference?”
Ask Nexus
“Assess whether this username, this domain and this wallet address are related, and show me the evidence for each link.”
Ask Nexus
“Map the public relationships between the people and organizations already in this case and flag the ones that remain unresolved.”
How Cross-Entity Correlation fits the investigation
A Skill is one move inside a case. The findings stay in the case file, where the evidence behind them can be checked, graphed and reported.
- 01
Choose or describe
Pick the Skill from the Skill Library, or describe the objective in your own words and let Nexus work out which Skills fit the question.
- 02
Review the evidence
Open the evidence record behind any finding and read the original public source before you rely on what it says.
- 03
Add to the case graph
Promote the people, organizations and identifiers you accept into the case relationship graph, straight from their evidence records.
- 04
Brief and report
Turn the case into a BLUF situation brief and, on Pro and Agency, into a structured PDF report with chain of custody.
Evidence before conclusions
What Cross-Entity Correlation cannot establish
Nexus keeps supported findings, possible matches, contradictions and information gaps apart instead of blending them into one narrative. These are the boundaries Cross-Entity Correlation states up front.
- Cross-Entity Correlation identifies relationships the public evidence supports. A supported relationship is not proof of legal ownership, control, coordination or wrongdoing.
- Inferred relationships rest on indirect signals such as shared identifiers or timing and require corroboration before they are treated as established.
- The absence of a relationship in the result means no supporting evidence was found within the scope of the investigation, not that no relationship exists.
Best for
Teams that reach for Cross-Entity Correlation most often.
Related OSINT concepts
The vocabulary this Skill works in, defined in the glossary.
Related Skills
Skills investigators commonly run next, in the same case.
Frequently asked questions
What does Cross-Entity Correlation return?
Cross-Entity Correlation returns an evidence-backed assessment of the relationships between the people, organizations and digital identifiers you specify. Each relationship carries the evidence that supports it and a label showing whether it is directly supported or inferred. Supported relationships can be added to the case relationship graph from their evidence records, inferred relationships stay visibly separate, and connections the evidence could not establish are recorded as information gaps.
Which Nexus plan includes Cross-Entity Correlation?
Cross-Entity Correlation is one of four Skills available only on the Nexus Agency plan, alongside Breach Pattern Analysis, IP Abuse History and IP Threat Signals. Agency includes all 47 Skills in the Skill Library, end-to-end encrypted investigation notes and 300 credits per month. Pro includes 43 of the 47; Agency suits teams running multi-subject investigations that need the correlation and pattern-analysis Skills.
What is the difference between a supported and an inferred relationship?
In Cross-Entity Correlation, a supported relationship is one that a source states directly — a filing that names a director, a profile that links to a domain, a page that lists an address. An inferred relationship rests on indirect signals, such as two entities sharing an identifier, a phone number or a registration date. Nexus labels the two separately so that inference is never mistaken for direct evidence, and both remain linked to their sources.
Does a relationship found by Cross-Entity Correlation prove ownership or control?
A relationship found by Cross-Entity Correlation shows that public evidence connects two entities in a specific way, with the source attached. It does not establish legal ownership, control, coordination or wrongdoing. Two companies sharing an address may be tenants of the same building; two accounts sharing a username may belong to different people. The Skill preserves that uncertainty, and the investigator decides what the relationship means in the context of the case.