Investigations
Digital forensic examination, incident reconstruction, litigation support, and independent expert analysis. We determine what happened and show how we know.
Explore investigationsWhen the decision depends on the evidence.
Zemi North examines digital events and AI systems to determine what happened, what the evidence supports, and where certainty ends.
Two practices. One discipline.
Evidence that holds under challenge.
Whether the artifact is a device, an incident timeline, an AI output, or an automated decision, the central question is the same: can the claim be defended?
Evidentiary sourceDigital evidence
Evidentiary sourceAI-system records
Zemi MethodHuman-led examination
Reported resultBounded finding
AI Assurance
Independent examination of AI systems, AI-assisted decisions, synthetic media, and the controls around them. Examination, not certification.
Explore AI assuranceFindings built for review
Clear source trails, calibrated conclusions, explicit limitations, and reports that an opposing expert, regulator, or court can follow.
Discuss litigation supportDecisions you can defend
Translate technical evidence into a bounded, actionable finding, without overstating what the available evidence can prove.
Start a conversationWhat we examine
Different artifacts. One evidentiary discipline.
Select an evidence environment to see the question it helps answer.
Device evidence
What happened on the system?
Examine artifacts, provenance, timelines, user activity, and the limits of what the available device record can establish.
Digital forensic investigationsEvent reconstruction
How did the incident unfold?
Correlate records across sources, test competing explanations, and reconstruct the sequence without extending beyond the evidence.
Incident reconstructionAI evidence
What did the AI system contribute?
Inspect prompts, outputs, logs, configuration, generated media, and surrounding records to identify influence and evidentiary gaps.
AI assuranceDecision record
Can the resulting decision be defended?
Separate observed facts from inference, state uncertainty, and connect the conclusion to sources that another reviewer can inspect.
The Zemi MethodPublished methodology
The Zemi Method An evidentiary methodology for investigation and assurance Version 1.2 · 2026
Method in practice
“The method exists before the engagement begins, and constrains the finding at the end.”
The Zemi Method is a dated, versioned evidentiary methodology for investigation and assurance. Read it before you engage us. Hold us to it afterward.
How the method worksIndependent Conclusions owned by a human examiner
Traceable Findings linked to inspectable sources
Calibrated Confidence and limitations stated plainly
Defensible Alternative explanations tested
Experience behind the method
Investigative judgment for a changing evidence landscape.
20+
Years across investigation, forensics, and technology leadership
Zemi North is led by Kevin V. Watson, an examiner with more than twenty years across law enforcement, corporate investigations, digital forensics, and technology leadership. The practice joins established forensic discipline with current, hands-on AI deployment and governance analysis.
Human accountability
AI-assisted, never AI-decided. Automation may support retrieval, correlation, and pattern proposal. A person owns every finding, can explain it under challenge, and answers for it.
- Lead
- Kevin V. Watson
- Practice
- Investigations / AI assurance / Training
Initial enquiry · Non-confidential
Bring us the question the evidence needs to answer.
Tell us whether you need an investigation, AI assurance, or private team training. Begin with the general nature of the matter. Evidence transfer follows conflict, authority, and scope checks.