Everything that happens between "scan" and "fixed."

Natural Buyer Question Modeling

We don't query engines with basic keyword phrases like "Northwind Cycles overview." AnswerLens maintains an adaptive bank of high-intent consumer prompts reflecting actual purchasing behavior:

• Direct comparison prompts ("Is Brand X or Brand Y better for long commutes?")
• Transactional policy checks (shipping rates, international customs, warranties)
• Unprompted category recommendations ("Top 5 sustainable sneaker brands in 2026")

Source-of-Truth Semantic Extraction

Point AnswerLens at your domain once. Our crawler parses your canonical product feeds, terms, return policies, and FAQs, extracting atomic factual assertions into a cryptographic verification graph.

Whenever an LLM asserts a claim about your business, the model's statement is parsed into atomic claims and matched against your source-of-truth baseline to catch semantic drift.

Answer Share & Sentiment Benchmarking

For any unprompted recommendation prompt where multiple brands could be suggested, AnswerLens quantifies:

• Primary Recommendation Rate: How often your brand is named #1
• Co-Citation Frequency: Which competitor is named alongside you
• Sentiment Polarity: Tone balance across ChatGPT, Claude, and Perplexity

Automated Fix Brief Compiler

Detecting a hallucination is only half the battle. AnswerLens automatically builds structured content briefs that tell your editorial and web engineering teams exactly what to publish:

• The exact page URL requiring content updates
• JSON-LD Schema markup (MerchantReturnPolicy, HowTo, Product)
• Concise semantic prose formatted for frontier crawler ingestion