Discovery
Does the product appear when the buyer asks for options without naming it?
Independent audit for AI-assisted buying
Find out whether AI buyers can actually understand your product.
AgentRank runs a small, evidence-led audit of how AI assistants describe, omit, compare, and recommend your product when buyers ask what to choose.
Human reviewed
Evidence attached
No ranking promises
Audit Lite / sample
Scorecard excerpt
Evidence, not another dashboard.
Sample score
58
Usable but uneven
Visibility
16 / 30
Accuracy
18 / 25
Evidence
9 / 20
Open discovery
Target absent while competitors appear.
Repair path
Facts page, comparison proof, retest prompt.
Why this matters
AI recommendations are becoming a shortlist layer before the sales conversation.
Most teams already know how they appear on Google. Fewer know what happens when a buyer asks ChatGPT, Perplexity, Gemini, Qwen, or Doubao to shortlist tools. We test that layer with dated prompts and preserved evidence.
Audit surface
Five failure points we check.
Description
Does the assistant explain category, fit, features, limits, and risk accurately?
Comparison
Which competitors become defaults, and what language makes them seem safer?
Sources
What public facts, citations, pages, FAQs, and proof points appear missing?
Repair
Which website or evidence changes should be made first and then retested?
Example signal
Named prompts are not enough.
A product can be understood when mentioned directly, but still fail to appear in open buyer discovery. That is a visibility problem at the exact moment a buyer is forming a shortlist.
Prompt state
Observed signal
Repair path
Open category search
Competitors listed; target absent
Use-case page + category facts
Named comparison
Target known, but described narrowly
Product facts + FAQ cleanup
Final recommendation
Competitor framed as safer default
Comparison proof + risk answers
Field study / July 2026
Five form backends. Thirty AI answers. One repeated discovery gap.
Three of five products were absent from every open buyer shortlist across Gemini and Perplexity, yet both surfaces understood them when named directly.
Read the full field studyWhat you get
A focused audit package, not another dashboard.
The first engagement is intentionally small. It gives enough evidence to understand the gap, decide whether to fix public assets, and retest honestly.
5
buyer-intent prompts
4
available AI surfaces max
3-5
competitors mapped
48h
target turnaround
Evidence packet
Prompt wording, surface access status, screenshots or transcripts, and a concise reading of what the assistant did.
Scorecard
Visibility, description accuracy, evidence strength, competitive position, and fixability scored with stated confidence.
Repair roadmap
Specific public pages, FAQs, comparison content, and evidence assets to create or tighten before retesting.
01
Define the buyer
Product URL, category, competitors, market, and buyer scenarios.
02
Run dated prompts
Available surfaces are tested; blocked surfaces are disclosed, not replaced.
03
Map evidence gaps
Claims, omissions, competitor defaults, and source weaknesses are documented.
04
Prioritize fixes
Each recommendation maps to a public asset and a retest prompt.
Boundaries
Strict about what the audit can and cannot claim.
We do not guarantee AI recommendation lift, fake reviews, create synthetic citations, buy spam links, bypass login gates, or claim one test represents every model, country, and future answer.
Read the methodologyRequest an audit
USD 99
Audit Lite is a one-time, evidence-led review for one product category.
Send us
Your product URL, category, market, and 3-5 competitors.
We reply with
Scope fit, payment next step, and the evidence we need.
Suggested email
Hi AgentRank, Product URL: Product category: Target market: Main competitors: I want to check whether AI buyers can discover, describe, and compare our product accurately. Please let me know if this fits the $99 Audit Lite scope.
Direct email: agentrank@walletaudit.me