Independent semantic integrity analysis
AI ScanLab provides expert-led audits of how information is interpreted, transformed, and propagated by AI systems. Our work focuses on identifying semantic drift, interpretive instability, and latent risk in environments where AI-mediated interpretation affects decisions, compliance, or reputation.
We operate as an independent analytical consultancy. We do not optimize content, train models, or intervene in production systems. We audit interpretation.
What we do
Organizations increasingly operate in environments where AI systems continuously interpret their content, brand narratives, and documentation. Across search engines, language models, and autonomous agents, meaning shifts without warning. By the time interpretive failures surface as operational errors, reputational damage, or compliance issues, the opportunity to intervene has passed.
AI ScanLab conducts structured semantic integrity audits identifying where meaning preservation succeeds, where it weakens, and where it fails. Our analyses surface risk before it generates operational, legal, or reputational consequences.
We evaluate how AI systems interpret input information, where semantic drift begins, how it accumulates, and when interpretive behavior becomes unstable or incoherent. This work does not focus on visibility metrics or engagement rates. It focuses on whether the meaning that drives those metrics remains aligned with the intent that created it.
Methodological foundation
AI ScanLab’s analytical frameworks are grounded in original research and empirical validation. Our work builds on Semantic Relativity Theory (TRS), a comprehensive theoretical framework for analyzing semantic behavior in AI systems, and CHORDS++, a topological stability analysis methodology.
Both frameworks are supported by published research with public DOIs and have been validated across multiple AI systems and real-world scenarios. Operational methodologies, calibration processes, and computational implementations remain proprietary.
Published Research:
- Semantic Relativity Theory v3.0 (DOI: 10.5281/zenodo.17611607)
- Additional research accessible via Research & Case Studies
Our frameworks do not rely on black-box assumptions or proprietary AI system access. Analysis is conducted through systematic external observation, controlled interaction, and expert evaluation of interpretive behavior as it manifests to real users and downstream systems.
Who we work with
AI ScanLab works with organizations where AI-mediated interpretation carries operational, legal, or strategic consequences.
Regulated industries use our services when compliance language, regulatory disclosures, or safety information must preserve semantic precision under AI transformation. Financial services, pharmaceuticals, biotechnology, and medical device manufacturers require verification that meaning remains stable when regulators, auditors, or the public encounter disclosure language through AI-powered systems.
Technology organizations engage us when semantic stability affects competitive positioning, technical differentiation, or multi-agent system reliability. Companies launching AI-powered products, operating agentic commerce systems, or maintaining complex documentation ecosystems require assurance that interpretation preserves intent across models and contexts.
Publishers and media organizations work with us when AI summarization, content reinterpretation, or attribution dynamics affect how audiences encounter their work. When meaning preservation determines whether content reaches audiences as intended or through distorted intermediaries, semantic integrity becomes essential infrastructure.
Public institutions and governance bodies use our analysis when interpretation errors carry legal, ethical, or social consequences. When AI systems mediate how policy, guidance, or public communication reaches stakeholders, semantic accountability requires independent verification.
How we work
All AI ScanLab engagements are delivered as bespoke expert analyses. There is no tooling, subscription dashboard, or automation layer. Every audit is conducted manually by experts with deep knowledge of semantic behavior in AI systems.
Our process:
Engagements begin with scope definition via email. Clients specify materials, systems, or contexts requiring evaluation. We establish audit boundaries, deliverable expectations, and timeline.
Analysis proceeds through systematic observation and controlled interaction. We do not access internal systems, proprietary algorithms, or confidential datasets. Audits operate entirely at the interpretation layer—evaluating how AI systems behave externally as experienced by real users or downstream agents.
Findings are documented as structured reports suitable for strategic decision-making, governance oversight, or regulatory preparation. Reports identify vulnerabilities, map risk conditions, and provide actionable intelligence without exposing the proprietary techniques that generated them.
We do not implement solutions, optimize content, or modify systems. Our role ends at diagnosis and recommendation. Implementation remains client responsibility.
Independence and integrity
AI ScanLab operates independently of AI platform providers, content optimization vendors, and model training organizations. We maintain no commercial relationships that could compromise analytical objectivity.
We do not accept engagements where findings must conform to predetermined conclusions. We do not modify analysis to satisfy client preferences. We document observable interpretive behavior and its implications—whether comfortable or inconvenient.
Clients receive findings as they emerge from analysis. If vulnerabilities exist, we document them. If positioning is structurally sound, we verify it. Our value derives from accuracy, not reassurance.
Research and thought leadership
AI ScanLab contributes to public understanding of semantic behavior in AI systems through published research, case studies, and analysis of emerging interpretive dynamics.
Selected work is available through our Research & Case Studies section and via academic repositories. We share methodological insights where doing so advances understanding without compromising proprietary techniques.
Our blog addresses interpretive patterns, risk dynamics, and semantic integrity challenges facing organizations operating in AI-mediated environments. Recent analysis includes validation of theoretical predictions against leaked AI system architecture and longitudinal drift studies in pharmaceutical and technology sectors.
Leadership
AI ScanLab is led by José López López, an independent researcher specializing in semantic field analysis and AI interpretation dynamics.
López López developed Semantic Relativity Theory (TRS), a comprehensive framework for analyzing how meaning behaves in AI-mediated environments, and CHORDS++, a topological methodology for evaluating structural stability of text under AI interpretation.
His research is published with public DOIs and has been empirically validated against real-world AI system behavior, including confirmation through leaked infrastructure documentation from deployed systems.
ORCID: 0009-0007-8862-5177
Engagement
AI ScanLab accepts engagements where semantic integrity carries strategic, operational, or compliance significance. We work with organizations prepared to act on findings and capable of implementing structural adjustments when analysis reveals vulnerabilities.
If AI systems mediate how your information reaches stakeholders, regulators, customers, or decision systems, interpretation is accountable.
