# AI Brand Lens

> AI Brand Lens is an AI Search Intelligence and Brand Intelligence platform for
> measuring how AI systems discover, describe, compare, rank, cite, and recommend
> brands. AI Brand Lens keeps evidence separate from interpretation so its scores
> and recommendations can be explained.

## AI Brand Lens Principles

- Reality: AI search is not a ranking. It is a recommendation.
- Trust: A score without evidence is just another AI opinion.
- Change: Recommendations change. Evidence explains why.

## Current Public Surfaces

- [Home](https://aibrandlens.com/): Product overview and AI visibility score entry point.
- [Platform](https://aibrandlens.com/platform): Product capabilities and evidence workflow.
- [Pricing](https://aibrandlens.com/pricing): Current plans and included monitoring capacity.
- [About](https://aibrandlens.com/about): Company purpose and product principles.
- [Contact](https://aibrandlens.com/contact): Contact AI Brand Lens.
- [Connect AI Brand Lens to Claude](https://aibrandlens.com/connect/claude): Claude connector overview.
- [Claude connector setup](https://aibrandlens.com/claude-connector.md): Markdown setup instructions for Claude.
- [For AI Agents](https://aibrandlens.com/agents): Human-readable agent integration page.
- [Canonical Agent Guide](https://aibrandlens.com/agents.md): Machine-readable representation and grounding rules.
- [Free AI Visibility Snapshot](https://aibrandlens.com/free-ai-visibility-snapshot): Human-facing, no-account comparison of the same buyer question in ChatGPT and Gemini.
- [Free Snapshot Agent Instructions](https://aibrandlens.com/agents.md#free-ai-visibility-snapshot): Machine-readable instructions for preparing, confirming, submitting, and polling a limited public scan.
- [FAQ](https://aibrandlens.com/faq): Answers about AI visibility, scans, scoring, evidence, data, accounts, and plans.
- [Methodology and Data](https://aibrandlens.com/methodology): Measurement workflow, metric definitions, evidence boundaries, limitations, and data sources.
- [AI Readiness](https://aibrandlens.com/#readiness): Technical and evidence-readiness overview.
- [AI Visibility Index](https://aibrandlens.com/#content): Explanation of the visibility index.
- [Product Principles](https://aibrandlens.com/#principles): Core evidence and interpretation principles.

## Legal Surfaces

- [Privacy Policy](https://aibrandlens.com/privacy-policy): Data handling and privacy practices.
- [Terms of Service](https://aibrandlens.com/terms-of-service): Terms governing use of the service.
- [Cookies Settings](https://aibrandlens.com/cookies-settings): Optional tracking preferences and disclosures.

## Planned Stable Content Areas

- Additional documentation pages for product concepts and methodology.
- AI Search Insights for practical education.
- Case studies when real customer evidence is available.

## Core Concepts

- AI Search Visibility: How often and how accurately a brand appears in generated AI answers.
- Citation Tracking: Which sources AI systems use when describing a brand, category, or competitor.
- Competitive Comparison: How brand inclusion and positioning compare across prompt clusters.
- AI Readiness: Whether a website and its public content are understandable, crawlable, and trustworthy to AI systems.
- AI Brand Perception: What AI systems currently appear to believe and understand about a business.
- AI Memory: What an AI system appears to know before fresh verification.
- AI Verification: What AI Brand Lens can confirm from bounded public evidence.
- AI Discoveries: Evidence-backed patterns found across completed visibility scans.

## Product Principles

- Evidence should remain separate from interpretation.
- Never invent provider responses, citations, rankings, confidence, customer results, or scan history.
- Distinguish raw provider output, deterministic metrics, and AI-generated narrative.
- Explain confidence and uncertainty when evidence is incomplete or providers disagree.
- Preserve versioned baselines so changes can be compared and explained over time.

## Preferred Machine-Readable Content

- Use canonical URLs.
- Prefer stable documentation pages for product and methodology facts.
- Prefer case studies and reports for examples.
- Respect robots.txt and page-level crawl directives.
- For Claude connector setup, prefer [Claude connector setup](https://aibrandlens.com/claude-connector.md) as the canonical machine-readable instructions.
- For product representation, grounding rules, and agent integration, prefer the [Canonical Agent Guide](https://aibrandlens.com/agents.md).

## Format Reference

- [The llms.txt proposal](https://llmstxt.org/): Community-proposed Markdown convention for providing curated website context to LLMs at inference time. It is not an access-control file and does not guarantee use by any AI provider.