The Forensics Engine: Why Competitors Win in AI
Every AI response has a paper trail. We trace it. Citation Path Analysis reveals the exact sources AI models use to generate responses, giving you the intelligence to dominate.
Citation Path Tracing Methodology
Our engine breaks down the lifecycle of an AI response into four verifiable stages, providing granular visibility into how your content is utilized.
Processing Pipeline
Four verifiable stages of AI response lifecycle
Source Ingestion
Crawling and indexing of domain-specific content. Verification of 'robots.txt' compliance and sitemap parsing.
Vector Search & Matching
Semantic processing of user queries against your indexed content vectors. Calculation of relevance scores.
LLM Context Window
Analysis of the prompt injection into the model's context window. Monitoring of token usage and retrieval augmentation.
Output Analysis
Forensic audit of the final generated response. Verification of citations against original source URLs.
Actionable Insights
Translate forensic data into strategic brand protection.
Fabricated Pricing Policy
The engine detected a high-confidence hallucination where GPT-4 claimed your Enterprise plan is 'free for non-profits,' contradicting verified documentation.
Brand Authority Confirmed
Your 'Data Sovereignty Whitepaper' is successfully serving as the primary citation source for 85% of compliance-related queries in Claude 2.
Content Gap Analysis
Queries regarding 'API Rate Limiting' are citing competitors. Your documentation lacks structured headers for this topic, reducing AI ingestibility.
Core Capabilities
Advanced tools designed for reputation management teams and SEO professionals in the age of Generative AI.
Source Attribution
Definitively link AI-generated text back to your original documentation. Prove ownership and track content lineage.
Hallucination Detection
Automatically flag instances where LLMs fabricate pricing, features, or policies attributed to your brand.
Sentiment Analysis
Understand the tone of AI responses. Are they recommending your product or warning against it?
Dispute Evidence
Generate cryptographic proofs of misinformation to submit for correction requests with model providers.
Visibility Optimization
A/B test content structures to see which formats are most likely to be cited by GPT-4 and Claude.
Real-time API
Integrate forensic data directly into your existing dashboards via our low-latency GraphQL API.
Source Authority Decay: Your Hidden Win Opportunity
The Silent Killer: Source Authority Decay
Information has a half-life. AI models aggressively penalize stale data to avoid hallucinations. If your content isn't regularly updated, it loses its 'freshness score,' becoming less relevant and less likely to be prioritized by LLMs like GPT-4.
This 'decay' means even accurate information can become invisible, leading to your brand being overlooked in critical AI-generated responses. Static content dies in the age of generative AI.
Monitor content's 'Freshness Score' in real-time.
We monitor your content's 'Freshness Score' in real-time, alerting you the moment your documentation becomes too old to be prioritized by GPT-4's context window. Continuous updates signal relevance to LLMs, keeping your brand in the consideration set and protecting its authority.
Content Freshness Score
This visual represents how content authority diminishes over time. The older the data, the less likely it is to be considered authoritative by AI models.
How RAG Works (And Why You're Losing)
Retrieval-Augmented Generation (RAG) is the architecture that powers ChatGPT, Claude, and Perplexity. When you ask an AI a question, it doesn't generate an answer from memory. Instead, it follows a three-stage process:
Retrieves
Relevant web sources from its training data and live web crawl
Augments
Its response with citations from those sources
Generates
A synthesized answer that appears authoritative
The Problem:
If your brand isn't in the citation chain, you're invisible. Traditional SEO tools show you rankings, but they don't show you why the AI chose a competitor's Reddit thread over your official documentation.
The Solution:
Semantic structuring for maximum retrieval. By organizing your content with structured data, clear hierarchies, and query-answer patterns, you dramatically increase the probability that AI models will retrieve and cite your brand.
- •Structured headers and semantic markup align your content with vector embeddings used by GPT-4 and Claude
- •Query-answer pairs (QAPs) embedded in your content increase retrieval probability by 3-5x
- •Clear content hierarchies help AI models understand context and prioritize your documentation over competitors
Key Takeaways for Business Leaders
Visibility is Engineered
It's not magic. Winning the AI answer box requires structured data, not just good keywords. Treat data as infrastructure.
Freshness = Authority
Static content dies. Continuous updates signal relevance to LLMs, keeping your brand in the consideration set.
Protect the Path
A broken citation path is a lost customer. Monitor trace logs to ensure your funnel is open from query to answer.
Forensics Engine FAQ
Common questions about Citation Path Analysis and how the Forensics Engine works.
Ready to trace your citation paths? Get your free AI visibility report to see where your brand appears in AI responses.