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Documentation Index

Fetch the complete documentation index at: https://ardocs.autonome.in/llms.txt

Use this file to discover all available pages before exploring further.

Analysis features are available on paid plans.
The Analysis page is the root-cause layer of AgentRank. It goes beyond scorekeeping and explains why performance looks the way it does.

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What Analysis contains

Analysis is built around four connected views:
  • Authority
  • Relevance
  • Readiness
  • Competitive gap
Together, they answer whether the problem is missing trust, weak product-query fit, incomplete catalog structure, or a direct competitor advantage.

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Authority

What it measures: How well your brand is cited and supported by external sources that influence tracked responses. The Authority section includes summary metrics plus a citation gap table showing which source domains cite competitors but not you. Low authority indicators:
  • Your brand appears in AI results mainly as a self-reference (your own website)
  • Competitors dominate editorial “best of” lists in your category
  • Your brand is rarely mentioned in comparison articles from trusted domains
How to improve: Build third-party coverage — pitch your products to relevant publications, pursue editorial reviews, and earn mentions from authoritative comparison sites in your category.

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Relevance

What it measures: How closely your products match the attributes and intent being asked for in each prompt. This section aggregates repeated attribute gaps across responses so you can see which missing or weak signals keep hurting you. Low relevance indicators:
  • Your brand appears for broad queries but not specific ones (e.g., visible for “running shoes” but not “trail running shoes for overpronation”)
  • Product titles and descriptions don’t match the language buyers use in their queries
  • Missing attributes like use case, terrain, feature specs, and size fit
How to improve: Align your product content with the language in your tracking prompts. Update titles, descriptions, and metadata to reflect specific use cases and buyer intents.

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Readiness

What it measures: How complete and structured your product data is for AI consumption. When Shopify is connected, Readiness can use richer catalog data. Without it, the section can still surface limited or proxy-style readiness signals where available. Low readiness indicators:
  • Missing product metafields (material, fit, terrain, use case)
  • Short or generic product descriptions
  • No structured data (JSON-LD schema) on product pages
  • Collections that don’t reflect how buyers search
How to improve: Follow the recommendations in the Actions tab, then inspect the exact affected rows on the Products page. Readiness work is often the most systematic to fix because it usually maps to catalog, schema, media, taxonomy, and metadata improvements.

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Competitive gap

The competitive gap view compares your store directly against active competitors. It shows where they beat you and which diagnostic dimension is driving the gap. This is where Analysis becomes most actionable. If one competitor consistently beats you on Authority, you likely need source and coverage work. If another competitor wins mostly on Readiness, your catalog structure is probably the bottleneck.

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When to use Analysis

Open Analysis when Overview tells you something is wrong but does not explain why. Typical workflow:
  1. Spot a drop on Overview.
  2. Open Analysis to identify the dominant failure mode.
  3. Open Sources if the issue looks citation-driven.
  4. Open Actions once you know what to fix.
  5. Open the linked products to inspect exact evidence and jump to Shopify or the storefront page.