Biggest leak
Your robots.txt blocks 5 AI crawlers
Your robots.txt Disallows GPTBot, ClaudeBot, Google-Extended and 2 more. That means ChatGPT can't crawl your site to ground a recommendation in your own content.
2 priority fixes readyStart with Fix 1
Where this could go with monthly tracking
See your AI visibility move month by month · sample projection
The trend line below is illustrative. Subscribe to Monitoring for ₹1,999/mo to see your real per-question rank, citation gains and competitor movement re-measured every 30 days.
live_scanAUDIT·AI visibility report·ⓘ how to read this·Website read — we couldn't place a category yet
LENSCORE AI· audit
Provisional read: We could not place LENSCORE AI's category, so this scan's questions were generic — not a visibility verdict. Tell us your category and we'll re-scan free.
Provisional read: We asked 25 generic consumer buying questions because we could not place your category from what we could read. LENSCORE AI was not named in any of them — but these are not your buying questions, so this is not a visibility verdict. Tell us your category and we'll re-scan free.
Treat the summary as provisional until this audit is refreshed.
Founder-ready audit Evidence-led Roadmap included
Same formula, sampled answers. The score formula never changes: the same answers always produce the same number. The answers it reads are one day's sample from engines that change on their own. So two scans a week apart can differ, and the difference is the engines moving, not the method.
Measured, one number per question — before any blend
- Recommendation ratenot measuredThis scan predates recommendation classification.
- Recommendation share of voicenot measuredNeeds recommendation classification.
- Ordinal prominencenot measuredNeeds recommendation classification.
- Citation rate0%Your own site was among the cited sources in 0% of questions; 0% of all citations pointed at it.
- Volatilitynot measuredNot measured: volatility needs the same question asked more than once per engine. Monitoring re-runs measure it.
- Answers from live searchnot measuredThis scan predates live-search tracking.
- Questions asked2525 core benchmark · 0 adapted to your brand · 0 custom.
What drives the index · formula geo-5factor.v1
- Presence · 35%0measured
- Share of Voice · 25%0measured
- Prominence · 15%0measured
- Consistency / Confidence · 15%100measured
- Citation & Entity Authority · 10%0measured
By buyer-journey angle
- When buyers look for options0/5
- When buyers shortlist and compare0/5
- When buyers ask for their specific need0/5
- When buyers check if you're legit0/5
- When buyers are ready to order0/5
The one-line truth
AI named LENSCORE AI in 0 of 25 buying questions · your own site is not among the 178 sources AI cites in your category · 1 critical fix below.
This report is built so a non-technical founder can read it top-to-bottom and know exactly what to do. Every finding is a row you can open. The dropdown contains the action, why it matters and what we saw.
AI Visibility (GEO) · 5 AI engines
Live engines queried at Thu, 22 Oct, 2026, 9:51 pm IST · AI answers drift — re-run every 30 days. Keep it tracked
AI recommends competitors, not you: in 25 of 25 buying questions.
0% · 0–3%stable confidence
Visibility per AI engine
Google Gemini (Search grounding)
0% · 0/25
OpenAI (web search)
0% · 0/25
Claude (web search)
0% · 0/25
Lenscore Retrieval Engine
0% · 0/25
Grok (Live Search)
0% · 0/25
Aggregated by majority vote across engines and 5 engine passes, so a one-off AI fluke can't swing your score.
When a fix shows up: live-search answers usually reflect a change 7–14 days after you publish; answers from the model's memory move over months, at its next update, and we don't promise it.
Who AI recommends instead
1
Nike
named in 20 of 252
Samsung
named in 19 of 253
Apple
named in 18 of 254
Adidas
named in 15 of 255
Microsoft
named in 14 of 256
Sony
named in 14 of 257
Google
named in 10 of 258
Walmart
named in 10 of 25A question you lose
“Top consumer brands in the US and global market now.”
AI named Apple, Google, Microsoft, not LENSCORE AI.
25 buying questions probed · 5 AI engines
These questions are generic — we could not place your category (category could not be placed, business type unknown). Tell us your category and we'll re-scan free for sharper ones.
Every question is unbranded: we asked what a buyer asks, never “is LENSCORE AIgood?”. A “named” below was earned in the answer, not planted in the question.
not namedTop consumer brands in the US and global market now.
not namedBest consumer brands in the US and global market.
not namedMost popular consumer brands in the US and global market.
not namedNew or emerging consumer brands in the US and global market worth trying.
not namedHighly recommended consumer brands in the US and global market.
not namedCompare the best consumer brands in the US and global market.
not namedBest alternatives to the leading consumer brands in the US and global market.
not namedWhich consumer brand in the US and global market has the best product quality?
not namedMost trusted consumer brands in the US and global market.
not namedBest value-for-money consumer brands in the US and global market.
not namedBest consumer brands in the US and global market for gifting.
not namedBest consumer brands in the US and global market for first-time buyers.
not namedSustainable or ethical consumer brands in the US and global market.
not namedBest consumer brands to discover and buy on Instagram in the US and global market.
not namedBest consumer brands in the US and global market for everyday use.
not namedWhich consumer brands in the US and global market are safe and legit to buy online?
not namedWhich consumer brands in the US and global market have the most genuine reviews?
not namedWhich consumer brands in the US and global market have the fewest complaints or returns?
not namedWhich consumer brands in the US and global market have the easiest returns and refunds?
not namedWhich consumer brands in the US and global market are recommended by real customers, not ads?
not namedAffordable consumer brands to buy online in the US and global market now.
not namedWhich consumer brands in the US and global market offer fast and reliable delivery?
not namedWhich consumer brands in the US and global market offer COD or easy online payment?
not namedWhich consumer brands in the US and global market can you order or ask on WhatsApp?
not namedWhich premium consumer brands in the US and global market are worth the money?
Methodology: pre-versioning (measured before 2026-09-06)
Do these first
Your 2 highest-impact fixes
Ranked by how much demand each gap is costing you. Start at the top — the full paste-ready fix for every one is in the Fix Kit below.
Executive summary
Evidence confidence
0 of 4 claims backed by live data
Method note: 4 due for a refresh. We surface this so older or inference-led claims do not read with the same certainty as fresh observed evidence.
Strongest proof
What is already working
The brand has enough public surface area to turn this audit into an actionable trust and visibility repair plan.
Priority move
Trust repair sprint
Provisional read: Add product schema and tighten the product detail surfaces first. This is the cleanest way to improve machine readability without changing the brand story.
Why this audit reads this way
The current scan context behind the verdict
Different brands produce different audits because Lenscore adapts the question bank, rule gating and confidence language to the brand context we could actually verify on this scan.
low confidence
Brand context
unknown
Category: consumer (low)
Market: global (medium)
This context decides which findings are relevant enough to survive into the final audit.
5 engines
Probe reliability
25 prompts · 5 passes
100% cross-engine / cross-run agreement
Cross-engine agreement makes the verdict sturdier than a one-model anecdote.
4 skipped · 34 active
Finding variance explained
34 of 38 audit rules applied to LENSCORE AI;4 were filtered out by context gating.
- 2 low category confidence
- 2 market mismatch
Context gating prevents irrelevant findings from reaching the report.
Inference warnings
- brand type could not be determined from any signal
- category could not be determined from any signal — fell back to a generic label
AI demand exposure gap · index
Where AI-era demand is passing you by right now
Exposure gap not rated. The questions behind it were generic (category could not be placed, business type unknown), so no percentage would be honest. Tell us your category and we'll re-scan free.
Consultant scorecard · 5 categories
How the brand scores across the 5 signals buyers and AI judge you on
7AI Visibility (GEO)
27SEO & Findability
53Trust & Security
36Performance & Core Web Vitals
43Conversion & Mobile UX
AI-era target 80
AI answer visibility
73 points to close
Buyers now ask AI search engines for brand recommendations. Strong GEO audits measure real citation share, not just SEO.
AI engines rarely name the brand for buying questions — competitors are capturing that demand.
Premium target 80
Search & discoverability
60 points to close
Knowledge panel, third-party mentions, and SERP footprint decide whether the brand looks established or invisible.
The brand is thin in search/Google, which makes every paid click less efficient.
AI-era target 78
Entity & structured data
44 points to close
AI/Google recommend brands they can structurally 'read' — Organization/Product schema, sitemap, machine-readable identity.
Structurally thin: missing schema/entity signals means AI and Google struggle to understand and cite the brand.
Premium target 82
Reputation & social proof
62 points to close
Products with 5+ reviews are far more likely to sell; review depth is a trust lever, not a vanity note.
Review depth isn't enough to make a cold buyer feel safe choosing this brand over a known name.
Trust target 85
Site trust & security
At or above target
Cold buyers check HTTPS, return/refund/shipping policies, and a real contact path before paying — security badges lift checkout ~32%.
The trust basics are visible — keep policies and contact one tap away.
D2C target 85
Brand & social commerce
51 points to close
D2C audits treat Instagram and WhatsApp as real buying surfaces, not secondary social links.
The social-to-sale handoff needs a clearer product + DM + WhatsApp path.
Mobile target 80
Performance & Core Web Vitals
44 points to close
78% of D2C traffic is mobile and 53% abandon a site that loads >3s. Measured on real PageSpeed field data, not a guess.
A slow or janky mobile experience is silently abandoning buyers before they reach the cart.
Conversion target 82
Checkout & conversion readiness
30 points to close
Fixing documented checkout friction lifts conversion ~35% (Baymard) — guest checkout, cost transparency, payment + buying-path clarity.
The path to purchase is leaking buyers: tighten the product surface, buying path (WhatsApp/COD), and visible policies.
Discovery Index · detail layer
The 5-factor breakdown beneath your LensCore Discovery Index
GEO Readiness · flagship
3/ 100
- AI citation (live)0
- Product & Organization schema0
- Review signals15
- Entity authority0
- FAQ schema0
- Share of Voice0
- Prominence0
- Consistency / confidence100
The radial breaks the GEO readiness signals into their component scores. The hero LensCore Discovery Index above weights Presence (35%), Share of Voice (25%), Prominence (15%), Consistency (15%) and Authority (10%), observed-or-zero on every factor.
Sentiment is tracked apart from visibility as a separate risk axis (see the AI Visibility verdict above): being named a lot while spoken about negatively is a risk, not a win, so it is never folded into the score.
Voice & authority · AI's view of you
Who the AI trusts about you
The sources AI engines actually read in your category, what they say about you when they read them and any place they got the facts wrong, folded into one block so the story reads top-to-bottom.
Who feeds the AI · source authority
The sources shaping AI answers in your category
The AI's recommendations in your category are driven by 178 domains — your own site is NOT among them. You were mentioned in 0/25 answers, but your own domain was cited as a source in only 0.
Mentioned vs. cited
LENSCORE AI was mentioned in 0 of 25 AI answers · your own site was cited as a source in none of them.
Mentioned means the AI named you. Cited means the AI actually read your own website as a source. Earning citations, not just mentions, is what makes the recommendation durable.
Named & cited
0
AI knows you and reads your siteNamed, not cited
0
Recommended from others' contentCited, not named
0
Site was read but you missed the listAbsent
25
Neither named nor citedWhat AI reads in your category
These are the 178 domainsshaping AI answers in your category, ranked by how often they were cited. Go earn presence where you're absent.
Where to earn AI's trust
AI pulls from these source types in your category. Earning a presence on them, not just your own site, is what makes AI recommend you durably (right now your own site isn't cited at all).
- editorial / press
Earn a mention in the editorial / comparison content that ranks brands in your category.
e.g. AI cites whatsapp.com here.
- review site
Get listed and earn ratings on the review sites AI quotes: a 30-minute profile on each.
e.g. AI cites trustpilot.com here.
- marketplace
Claim and optimise your marketplace listings: AI reads product pages and ratings there.
e.g. AI cites alibaba.com here.
What is working
The brand has enough public surface area to turn this audit into an actionable trust and visibility repair plan.
Evidence snapshot
What we checked
Product schema
No strong product schema signal was detected.
refreshed 3 months ago
Review proof
A review/testimonial surface was detected (the on-page review count is not published).
refreshed 3 months ago
WhatsApp buying path
No visible WhatsApp handoff was detected.
refreshed 3 months ago
Policy and trust pages
Shipping, returns, privacy, or support pages were detected.
refreshed 3 months ago
Third-party entity mentions
Web search was not run for this scan, so third-party entity mentions could not be measured (not a confirmed gap).
refreshed 3 months ago
Instagram commerce surface
No Instagram surface was detected in the scanned evidence.
refreshed 3 months ago
Visual proof
What the storefront and social profiles actually look like
desktop
Desktop storefront view
Desktop capture of the current brand storefront or homepage.
mobile
Mobile storefront view
Mobile capture of the storefront, where many D2C buyers first inspect trust signals.
Source influence map
Where trust is visible and where intent leaks
Website source of truth
https://lenscore.app is reachable and can support a stronger audit trail.
Buyer risk: If product, policy, review, and schema evidence stay thin, buyers still need to work too hard to verify the brand.
Next action: Make the site the canonical source for category, products, policies, reviews, and schema.
AI/search answers
AI visibility readiness is 7/100.
Buyer risk: AI and search systems may identify the brand but still hesitate to cite or recommend it for product-level buying questions.
Next action: Add structured data, answer-ready category copy, and citation-worthy product/support content.
Google trust
Google trust readiness is 20/100.
Buyer risk: The brand can look active on social while still feeling thin in Google, reviews, and third-party proof.
Next action: Build consistent entity proof, review surfaces, sameAs links, and public profile consistency.
Instagram discovery
No Instagram commerce surface was detected.
Buyer risk: Instagram-first discovery is hard to convert if product category, buying path, and trust proof are inconsistent.
Next action: Align bio, product category, link path, highlights, proof, and WhatsApp/DM handoff.
WhatsApp or DM handoff
No clear WhatsApp or DM buying path was detected.
Buyer risk: Intent leaks when a buyer likes the brand but cannot quickly ask about size, price, shipping, or payment.
Next action: Add obvious WhatsApp/DM entry points and saved replies for the common buying objections.
Review and proof layer
A review/testimonial surface was detected (review count not published on-page).
Buyer risk: Cold buyers need third-party proof before trusting an unknown D2C brand, especially before prepaid purchase decisions.
Next action: Collect compliant reviews and place proof near product intent, policy pages, and checkout objections.
Trust & Security · live checks
Hard signals a cold buyer and Google read before trusting you
Valid SSLGoogle Trust Services · 88d to renewal
F · 1/6Security headers grade
HTTP security headers
Review authenticity & depth
AI and cold buyers weigh verifiable, recent, photo-backed reviews far more than a bare star count and unverifiable reviews carry FTC risk.
India conversion · COD / UPI / RTO
The prepaid & trust signals that decide whether COD orders stick
Return-to-origin (RTO) on COD orders is a top-3 D2C P&L line, each failed COD costs ₹350–700. Prepaid incentives + visible trust badges shift orders to prepaid, which returns far less.
Not foundUPI / prepaid options
MissingPrepaid incentive (e.g. % off on prepaid)
Not foundVisible COD policy
Trust certifications
No trust certifications (FSSAI, secure-checkout, dermatologically-tested, etc.) were detected near the buy decision, cold buyers look for these before a prepaid order.
Technical health · crawl checks
The technical issues a site-audit crawler would flag
83/100Technical health score
0Broken links · 9 tested
2Pages parsed
Structured-data validity (rich-result eligibility)
No structured data (JSON-LD) found — search engines and AI have no machine-readable facts to cite. Adding Product + Organization JSON-LD is a high-impact, one-time fix.
Indexability & canonical
Indexable by search + AI
No noindex directive found, search engines and AI can index this page.
No canonical tag
No <link rel="canonical"> found, duplicate/variant URLs can split ranking signals.
On-page, schema & security checks
Page title tag
“LensCore — AI Visibility Audit for modern brands” (48 chars).
Meta description
Present, 115 chars.
Primary heading (H1)
Exactly one H1.
Unique page titles
The homepage and a key inner page share the same <title> — duplicate titles confuse ranking.
Fix: Give every important page its own descriptive title.
Secure assets (no mixed content)
All assets load over https.
Product schema valid
No Product structured data found — product rich results (price, stars) can't show in Google or AI answers.
Fix: Add Product JSON-LD on product pages (most Shopify themes/apps do this).
Organization schema valid
No Organization structured data — AI engines have no machine-readable brand identity to cite.
Fix: Add Organization JSON-LD with name, url, logo, and social sameAs links.
FAQ schema valid
No FAQPage structured data — AI assembles answers from Q&A-shaped content, so it paraphrases rivals instead of quoting you.
Fix: Publish an FAQ (shipping, sizing, returns, COD/UPI) with FAQPage JSON-LD.
AI crawler access (robots.txt)
5 AI crawler(s) are blocked, those engines cannot read your site to recommend you.
GPTBotChatGPT training (OpenAI)blocked
ChatGPT-UserChatGPT browsing (OpenAI)allowed
OAI-SearchBotChatGPT Search (OpenAI)allowed
ClaudeBotClaude training (Anthropic)blocked
Claude-WebClaude browsing (Anthropic)allowed
PerplexityBotPerplexityallowed
Google-ExtendedGoogle Gemini / AI Overviewsblocked
Applebot-ExtendedApple Intelligenceblocked
CCBotCommon Crawl (feeds many LLMs)blocked
Knowledge-graph entity (Wikidata)
No Wikidata / knowledge-graph entity found, AI engines have no authoritative entity to anchor a recommendation to. (A name search not matching is a weak signal, not definitive proof the entity is absent.)
Visual proof
What the storefront actually looks like
Website surfaces
desktop
Desktop storefront view
Desktop capture of the current brand storefront or homepage.
Buyer risk
If the first desktop view lacks category clarity, proof, or contact confidence, colder buyers bounce before product intent compounds.
mobile
Mobile storefront view
Mobile capture of the storefront, where many D2C buyers first inspect trust signals.
Buyer risk
Mobile friction is usually where discovery-led interest dies first, especially when popups, weak proof, or clutter block the buying path.
Source influence map
Where trust is visible and where intent leaks
presentobservedstale
Website source of truth
https://lenscore.app is reachable and can support a stronger audit trail.
If product, policy, review, and schema evidence stay thin, buyers still need to work too hard to verify the brand.
Next: Make the site the canonical source for category, products, policies, reviews, and schema.
Refreshed 6/27/2026
missingderived
AI/search answers
AI visibility readiness is 7/100.
AI and search systems may identify the brand but still hesitate to cite or recommend it for product-level buying questions.
Next: Add structured data, answer-ready category copy, and citation-worthy product/support content.
Refreshed 10/22/2026
missinginferencestale
Google trust
Google trust readiness is 20/100.
The brand can look active on social while still feeling thin in Google, reviews, and third-party proof.
Next: Build consistent entity proof, review surfaces, sameAs links, and public profile consistency.
Refreshed 6/27/2026
missingobservedstale
Instagram discovery
No Instagram commerce surface was detected.
Instagram-first discovery is hard to convert if product category, buying path, and trust proof are inconsistent.
Next: Align bio, product category, link path, highlights, proof, and WhatsApp/DM handoff.
Refreshed 6/27/2026
missingobservedstale
WhatsApp or DM handoff
No clear WhatsApp or DM buying path was detected.
Intent leaks when a buyer likes the brand but cannot quickly ask about size, price, shipping, or payment.
Next: Add obvious WhatsApp/DM entry points and saved replies for the common buying objections.
Refreshed 6/27/2026
presentobservedstale
Review and proof layer
A review/testimonial surface was detected (review count not published on-page).
Cold buyers need third-party proof before trusting an unknown D2C brand, especially before prepaid purchase decisions.
Next: Collect compliant reviews and place proof near product intent, policy pages, and checkout objections.
Refreshed 6/27/2026
Prioritised issues · finding → fix
The issues we found, ranked by commercial drag
1 critical issue costing you buyers right now, then 3 opportunities. Click any row: the dropdown is the fix.
ViewActions first: the raw evidence is one click away.
Jump to section
The Fix Kit · paste-ready
Copy-paste fixes generated from your own site
When a fix shows up: live-search answers usually reflect a change 7–14 days after you publish; answers from the model's memory move over months, at its next update, and we don't promise it.
Organization + Product structured data
Paste each block inside its own <script type="application/ld+json"> tag in your site's <head>. The Organization block is ready as-is, site-wide. The Product block is an illustrative template: replace every REPLACE_WITH_* value (and the description) with the real product name and price before it goes on a product page.
Illustrative template — replace every REPLACE_WITH_* value before publishing
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "LENSCORE AI",
"url": "https://lenscore.app",
"description": "See how AI search engines recommend your brand — or your competitors. Live AI audit, evidence-led score, free scan."
}
{
"@context": "https://schema.org",
"@type": "Product",
"name": "LENSCORE AI Consumer",
"brand": {
"@type": "Brand",
"name": "LENSCORE AI"
},
"description": "ILLUSTRATIVE TEMPLATE — replace every REPLACE_WITH_* value and this description with the product's real name, description and price before publishing.",
"offers": {
"@type": "Offer",
"priceCurrency": "INR",
"price": "REPLACE_WITH_PRICE",
"availability": "https://schema.org/InStock"
}
}FAQPage structured data
Edit the answers to match your real policies, then paste inside a <script type="application/ld+json"> tag on your FAQ (or home) page. This is what AI quotes back to buyers.
Generated from this scan — review before publishing
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "Do you offer Cash on Delivery (COD) and UPI?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Replace with your real payment methods — list only what you actually accept at checkout."
}
},
{
"@type": "Question",
"name": "What are your shipping times and charges?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Orders ship in 24–48 hours and are delivered across India in 3–7 days. Replace with your real timelines."
}
},
{
"@type": "Question",
"name": "What is your return and exchange policy?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Easy returns/exchanges within 7 days of delivery. Replace with your real policy."
}
},
{
"@type": "Question",
"name": "How do I choose the right size?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Replace with your real sizing guidance, or delete this question if sizing does not apply to what you sell."
}
},
{
"@type": "Question",
"name": "How do I contact LENSCORE AI?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Replace with your real contact channels and response time."
}
}
]
}Recommended next move
Add product schema and tighten the product detail surfaces first. This is the cleanest way to improve machine readability without changing the brand story.
Your report
Social + organic traffic
The public discovery-to-buying path
The brand has usable social surfaces but several gaps in bio quality, local visibility, or proof density soften the discovery path.
Website visual proof
We captured at least one live storefront view, but not enough deeper surfaces to fully judge mobile and product-path friction yet.
Next: Use the captured homepage and commerce views to tighten above-the-fold category clarity, CTA visibility, proof, and buying flow.
Instagram profile clarity
No Instagram commerce surface was detected for this brand.
Next: Add or standardize the public Instagram surface if social discovery is part of the buyer journey.
CTA & link-in-bio quality
No Instagram surface — CTA quality not applicable or not measurable.
Next: Add an Instagram surface or equivalent social storefront if social discovery is part of your funnel.
Local search visibility
Local search was not measured on this scan.
Next: Create or claim the Google Business Profile listing. It is free and directly feeds local AI answers and map results.
DM or WhatsApp handoff
A public buying handoff exists from the social or website surface.
Next: Tighten saved replies and objection-handling so the handoff converts, not just exists.
Organic proof trail
Organic citation domains: 178 — distinct sites AI engines cited when answering; separate from the search-result mentions counted above.
Next: Strengthen the best-performing organic surfaces with category copy, proof, and consistent entity naming.
Social proof density
A testimonial or review surface exists, but the scan could not verify a published count.
Next: Move the strongest proof closer to product, DM, and WhatsApp conversion moments.