# NRLC.ai | Generative Engine Optimization (GEO) Documentation

NRLC.ai specializes in Generative Engine Optimization (GEO) and technical infrastructure for AI-mediated search. This file provides the high-density map for LLM ingestion and RAG-driven retrieval.

## Core Frameworks

- [Generative Engine Optimization (GEO) Overview](https://nrlc.ai/en-us/generative-engine-optimization/): The foundational guide to how AI search systems retrieve and cite information.
- [GEO-16 Framework](https://nrlc.ai/en-us/insights/geo16-framework/): Six principles and sixteen pillars of AI-native search optimization.
- [Decision Traces](https://nrlc.ai/en-us/generative-engine-optimization/decision-traces/): Operational framework for understanding how generative systems select information for citation.
- [Grounding Budgets & Fragment Retrieval](https://nrlc.ai/en-us/insights/grounding-budgets-prechunking/): Technical specifications for fragment-level retrieval and token limit optimization.

## Data Structures & Specifications

- [Crouton Specification](https://nrlc.ai/en-us/docs/prechunking-seo/croutons/): Atomic, retrievable fact structures designed to survive AI extraction and isolation.
- [Prechunking Workflow](https://nrlc.ai/en-us/docs/prechunking-seo/workflow/): Procedural guide to intent decomposition and structured publishing for AI citation.
- [Semantic Modeling Architecture](https://nrlc.ai/en-us/insights/semantic-modeling/): Technical documentation on entity-relationship traversal for generative retrieval.

## Failure Mode Analysis

- [GEO Failure Modes Index](https://nrlc.ai/en-us/generative-engine-optimization/failure-modes/): Catalog of observable patterns causing content to disappear from AI-generated answers.
- [Hydration Failure & SEO](https://nrlc.ai/en-us/insights/silent-hydration-seo/): Analysis of how JavaScript hydration issues break AI indexing and retrieval.
- [Confidence Band Filtering](https://nrlc.ai/en-us/generative-engine-optimization/confidence-band-filtering/): Mechanics of the gate between retrieval and citation.

## Technical Documentation

- [Prechunking SEO Doctrine](https://nrlc.ai/en-us/docs/prechunking-seo/doctrine/): Core engineering discipline for structuring content for the zero-inference era.
- [AI Retrieval Mechanics](https://nrlc.ai/en-us/docs/ai-retrieval/): Detailed guide on how search engines extract, score, and surface content segments.
- [Enterprise Schema Governance](https://nrlc.ai/en-us/insights/schema-governance-and-validation/): Large-scale structured data validation for high-risk properties.