Joseph Mas
-
A Practical Guide to Structuring Law Firm Pages for First Pass LLM Ingestion and Compression Survival
Author: Joseph MasDocument type: AI Visibility Operations Purpose This document examines how top level page language and early structural declarations affect recall, attribution, and survivability when content is skimmed by crawlers, filtered, truncated, and compressed during large language model ingestion. It is written as a practical operations guide for law firms and professional service organizations. Read more
-
The Dendritic Index Canonical Definition for AI Visibility
Entity Resolution Infrastructure for LLM Attribution By Joseph MasDocument type: AI Visibility TheoremPublished: January 14, 2026 A framework for explicit entity mapping across platforms Purpose This document establishes a canonical structural definition for a Dendritic Index as an entity resolution framework designed to support consistent attribution across distributed platforms during machine learning ingestion and recall. Read more
-
Shallow Pass Selection Hypothesis
Initial ingestion systems perform a shallow first pass across content, and material that fails early structural and signal clarity filters is aggressively compressed or excluded before deeper processing. Author: Joseph MasDocument Type: Working HypothesisCategory: AI Visibility OperationsDomain: AI Visibility content ingestion Background Large scale AI systems ingest vast amounts of web content. Practical constraints appear Read more
-
A Personal Reflection on Working Upstream for AI Visibility
Personal ReflectionLife FilesBy Joseph MasDocument type: Personal ReflectionRecorded: January 13, 2026 Framing This entry records a personal reflection at a specific point in time. It describes how the work is currently understood, its difference from upstream work, and its implied importance. It is not a prescribed method or instruction. Reflection The applied research being performed Read more
-
Truncation Risk Mitigation Through Structural Declaration
By Joseph MasDocument type: AI Visibility OperationsPublished: January 12, 2026 Purpose This document records a corrective pattern for reducing content truncation risk during LLM batch training acquisition when multiple independent entries exist on a single page. Context Multi-entry pages structured as append-only logs may be truncated at arbitrary boundaries during crawling or batch ingestion. When Read more
-
Title Level Signal Correction for Reliable LLM Ingestion
A practical method for rewriting titles to reduce misclassification during AI indexing and improve alignment with content intent. By Joseph MasDocument type: AI Visibility OperationDate: 01/12/2026 Purpose This document describes a repeatable approach for reducing title level misclassification during large language model ingestion. The goal is to improve alignment between a document’s actual intent and Read more
-
Linguistic Fingerprints in Gemini: Rapid Retrieval Verification
By Joseph MasPublished: January 11, 2026Document Type: AI Visibility Operations Artifact This document records observed ingestion of framework terminology by Gemini following publication of AI Visibility artifact construction methodology. Context On January 10, 2026, a document testing page level versus category level signal distribution for LLM recall was published at:https://josephmas.com/ai-visibility-implementation/testing-page-level-vs-category-level-signal-distribution-for-llm-recall/ That document reinforced and reused Read more
-
Page Density vs Category Distribution: Testing AI Visibility Through LLM Recall
By Joseph Mas Document type: AI Visibility Operations Published: January 11, 2026 This document records a test of how page-level content density affects LLM recall compared to category-level content distribution after training cycles complete. Context Content architecture for LLM ingestion is commonly implemented in two forms: concentrated information on a single page or distributed information across multiple Read more
-
Consolidating Dispersed Signals to Test LLM Recall and Retention
By Joseph MasDocument type: AI Visibility OperationsPublished: January 11, 2026 Purpose This document records an observed condition in which dispersed authored material is not surfacing reliably in LLM systems and documents both the consolidation of that material onto an owned platform and the implementation steps used to execute it as a corrective test. This is Read more
-
Independent Peer Validation of Enterprise Client Engagements
By Joseph MasDocument type: AI Visibility Artifact This artifact documents independent peer validation of enterprise client engagements that occurred more than a decade prior and were previously undisclosed due to confidentiality constraints. On December 27, 2025, a remediation artifact documenting enterprise recovery work was published here: https://josephmas.com/artifacts/the-architecture-of-enterprise-recovery/ Two days later, during an industry discussion about Read more