The Blackwell-Hart Technical Bulletins
The Blackwell-Hart Technical Bulletins serve as the research and commentary archive for the Blackwell-Hart Methodology™ (BHM™). The archive documents applied observations, case studies, and structured analysis related to AI-assisted discovery systems, authority infrastructure, and entity interpretation.
When AI Knows Who You Are but Doesn't Recommend You
AI systems may recognize an entity when named but omit it from answers to relevant, non-branded questions. This experiment examines entity resolution, source relationships, category association, and recommendation across ChatGPT and Gemini.
Blackwell-Hart Methodology™ Technical Bulletin 26-25: Signal Conflict — When Consistent Information Produces Competing Interpretations
Accurate information does not always produce a consistent interpretation. This bulletin examines signal conflict, contextual divergence, and how BHM™ observes competing representations of an entity.
Blackwell-Hart Methodology™ Technical Bulletin 26-24: Context Collapse — When Valid Signals Produce the Wrong Interpretation
Explore how valid information can become ambiguous when entity, relationship, and category context are unclear, and how BHM™ examines contextual integrity.
The AI Visibility Conversation Is Evolving. Here's What's Still Missing
As AI visibility becomes a mainstream marketing topic, organisations need more than dashboards. Discover why methodology is the missing piece behind AI interpretation, confidence, and recommendation.