About YY
An AI forensic data analysis and research laboratory.
YY examines datasets, operational records, and AI-system behavior. The work preserves source context, separates observation from inference, and leaves a reviewable artifact.
- Source-linked findings
- Explicit uncertainty
- Bounded conclusions
What we examine
Three evidence surfaces, one discipline
Services, software products, open-source tools, and research are delivery forms. Evidence-oriented analysis is the common work.
- Practice area
Datasets and corpora
Inspect structure, duplicates, provenance declarations, secret-risk signals, and drift without turning a narrow observation into a broad quality verdict.
- Practice area
Operational records
Convert authorized source material into source-linked registers, drafts, and review queues while keeping ambiguous or unsupported fields visible.
- Practice area
AI-system behavior
Test observable system properties, preserve receipts, and distinguish what the evidence demonstrates from what it cannot establish.
How the laboratory works
The evidence boundary is part of the result
- 01
Preserve the source
A finding should remain connected to the file, recording, event, or declared input that supports it.
- 02
Separate observation from inference
Copied facts, deterministic checks, model suggestions, and human decisions should not collapse into one unlabeled answer.
- 03
Keep uncertainty visible
Missing, conflicting, and unsupported evidence belongs in the output rather than being silently repaired.
- 04
Make review possible
The useful result is a readable artifact another authorized person can inspect, correct, export, or refuse.
A precise use of forensic
Traceable analysis, not an implied legal credential
At YY, forensic means that observations retain their source, assumptions are stated, uncertainty stays visible, and another reviewer can inspect the path to the result. It does not mean YY is an accredited forensic laboratory, a calibration laboratory, a law firm, or an expert-witness service.