AI ScanLab conducts and publishes applied research on semantic behavior in AI-mediated environments.
Our work examines how meaning, intent, and narrative structures are interpreted, transformed, and propagated by AI systems — and how these processes influence real-world outcomes before they become visible through traditional metrics.
This section presents selected research outputs and case studies that demonstrate the practical application of semantic field analysis to real-world scenarios.
Research
AI ScanLab’s analytical frameworks are grounded in original research and empirical validation.
Our work builds on Semantic Relativity Theory (TRS), a formal framework for modeling meaning as a field subject to stability, drift, and collapse under interpretive transformation.
Published research includes:
- Theoretical formulations of semantic fields and interpretive stability
- Empirical validation across multiple AI systems and contexts
- Application to high-stakes environments such as commerce, search, and automated decision-making
All research outputs are publicly documented through registered DOIs:
- Published research: DOI 10.5281/zenodo.17611607 [Link to Zenodo]
Operational methodologies, calibration processes, and computational implementations remain proprietary.
Case studies
The following case studies apply semantic field analysis to real-world products and launches.
They are not retrospective explanations.
They are forward-looking semantic diagnostics.
Each case demonstrates how analysis of pre-launch discourse, documentation, and early narrative formation allows prediction of adoption, persistence, or failure — before market data confirms the outcome.
Case 1
Semantic Field Stability and Long-Tail Adoption in Gaming Releases
Tomba! 2 Special Edition vs Avatar: Frontiers of Pandora
This case study analyzes how two very different gaming launches generated contrasting long-term outcomes, despite radically different budget sizes and initial visibility.
The analysis focuses on:
- Narrative stability versus launch amplitude
- Community-driven semantic persistence
- Long-tail adoption patterns
- Predictive signals present before release
The study demonstrates how semantic field stability can outweigh hype, marketing scale, and short-term visibility in determining long-term success.
Case 2
Pre-Launch Semantic Diagnostics in Consumer Technology
Intel Panther Lake vs Samsung Galaxy S26
This case study examines how pre-launch discourse, documentation, and early leaks shape interpretive trajectories for high-profile consumer technology products.
The analysis focuses on:
- Pre-launch narrative formation
- Regional semantic divergence
- Stability and drift across technical communities and mainstream discourse
- Predictive indicators of adoption, skepticism, or rejection
The study demonstrates how semantic diagnostics can identify strategic risk and opportunity before products reach the market.
Case 3
Pharmaceutical Drift Detection
Ozempic® Semantic Field Inversion Analysis (2017-2025)
This case study tracks an 8-year longitudinal drift from FDA-approved diabetes indication to dominant off-label weight loss interpretation in community discourse.
The analysis focuses on:
- Baseline establishment vs community interpretation divergence
- Drift acceleration patterns and threshold breaches
- Cross-model variance in AI-mediated interpretation
- Regulatory contradiction emergence
- Irreversibility prediction and intervention timing
The study demonstrates how semantic drift can invert official positioning despite unchanged regulatory status, creating operational and compliance risk invisible to traditional monitoring systems.
Methodological note
The analyses presented in this section are public research applications.
They do not constitute commercial audits, investment advice, or privileged access to internal data.
Each case applies the same analytical principles used in AI ScanLab’s audit services, without exposing proprietary computational methods.
From research to Audit
The purpose of this research is not academic publication alone.
It exists to support rigorous, independent audits of semantic integrity in AI-mediated systems.
If AI systems influence how your organization, product, or intent is interpreted, semantic behavior can be analyzed before outcomes become irreversible.
