LensCoreA real AI-visibility audit for a B2B SaaS tool. See how ChatGPT, Gemini, Claude, Perplexity and the Lenscore Retrieval Engine answer the 25 questions SaaS buyers actually ask — and where your brand disappears from the answer.
This is our own product, scanned with the same method as any customer. The data is what AI engines said on the day of the scan.
Where this could go with monthly tracking
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.
Provisional read: AI recommends a rival over LensCore in 23 of 25 real buying questions, Profound is taking that demand.
Provisional read: AI recommends LensCore in only 2 of 25 AI visibility audit software buying questions (8% AI presence). Profound appears more often. This audit shows the questions you lose and how to close the gap.
Treat the summary as provisional until this audit is refreshed.
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
What drives the index · formula geo-5factor.v1
By buyer-journey angle
The one-line truth
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) · 6 AI engines
Live engines queried at Thu, 10 Sept, 2026, 2:17 am IST · AI answers drift — re-run every 30 days. Keep it tracked
Visibility per AI engine
Aggregated by majority vote across engines and 6 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. In this scan, 25 of 25 answered questions came from a live search.
How AI talks about LensCore
Who AI recommends instead
A question you lose
“Best AI visibility audit software software tools in the US and global market right now.”
AI named Amadora AI, HubSpot AI Search Grader, SEO.com, not LensCore.
25 buying questions probed · 6 AI engines
Every question is unbranded: we asked what a buyer asks, never “is LensCoregood?”. A “named” below was earned in the answer, not planted in the question.
Methodology 2026.09.s69 · benchmark core-25.v1 · questions packs-5x5.v1
Do these first
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.
Biggest leak
The brand was named in only 2 of 25 AI visibility audit software buying questions (8% AI presence) on Google Gemini (Search grounding) + OpenAI (web search) + Claude (web search) + Lenscore Retrieval Engine + Google AI Mode + Perplexity (Sonar) (1× each). Profound appears more often. (Measured for the inferred category "AI visibility audit software" in the US and global market.)
Strongest proof
The brand has enough public surface area to turn this audit into an actionable trust and visibility repair plan.
Priority move
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
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.
b2b saas
Category: AI visibility audit software (high)
Market: global (high)
This context decides which findings are relevant enough to survive into the final audit.
25 prompts · 6 passes
99% cross-engine / cross-run agreement
Cross-engine agreement makes the verdict sturdier than a one-model anecdote.
35 of 38 audit rules applied to LensCore;3 were filtered out by context gating.
Most rules applied to this brand.
AI demand exposure gap · index
Directional range: ≈ 46–56% (±5% from the index, set by input-signal quality).
A relative diagnostic index of how far each area is from a healthy target, weighted by typical revenue impact — NOT a measurement of revenue lost, and NOT a prediction or guarantee of rupees gained.
How this is calculated:a weighted index of the gaps below — each area’s distance from a healthy target, weighted by how much revenue that area typically drives. It shows where AI-era demand is passing the brand by and what to fix first; it is a directional signal, not a measured rupee amount or a promised recovery.
Share of AI buying-question answers vs an 80/100 target. ~30% of buyers research via AI; LLM-referred traffic converts 30–40%.
Product surface, buying path (WhatsApp/COD), visible policies vs an 82/100 target. Documented checkout friction ≈ 35% conversion lever (Baymard).
HTTPS, security headers, policies, contact vs an 85/100 target. Cold buyers verify these before a first prepaid order.
Review depth, rating, source breadth vs an 82/100 target. Proof density is a direct trust lever for unknown D2C brands.
Real PageSpeed mobile performance vs an 80/100 target. 78% of D2C traffic is mobile; 53% abandon a load >3s.
Consultant scorecard · 5 categories
AI-era target 80
60 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
52 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
8 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
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
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
13 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
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
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
GEO Readiness · flagship
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
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 AI's recommendations in your category are driven by 337 domains — your own site is NOT among them. You were mentioned in 2/25 answers, but your own domain was cited as a source in only 0.
LensCore was mentioned in 2 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
2
Recommended from others' contentCited, not named
0
Site was read but you missed the listAbsent
23
Neither named nor citedThese are the 337 domainsshaping AI answers in your category, ranked by how often they were cited. Go earn presence where you're absent.
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).
Earn a mention in the editorial / comparison content that ranks brands in your category.
e.g. AI cites seoptimer.com here.
Get listed and earn ratings on the review sites AI quotes: a 30-minute profile on each.
e.g. AI cites g2.com here.
Claim and optimise your marketplace listings: AI reads product pages and ratings there.
e.g. AI cites amazon.com here.
Accuracy · is AI getting you right?
Checked 5 AI answers against what we verified about LensCore — no inaccuracies detected.
We checked the AI engines' own answers for false claims about LensCore: wrong category, made-up products, wrong origin, or “discontinued” mistakes, against what we verified from your site. None were found. (We report nothing when nothing is wrong, honesty over theatre.)
The brand has enough public surface area to turn this audit into an actionable trust and visibility repair plan.
Evidence snapshot
No strong product schema signal was detected.
refreshed 1 months ago
A review/testimonial surface was detected (the on-page review count is not published).
refreshed 1 months ago
No visible WhatsApp handoff was detected.
refreshed 1 months ago
Shipping, returns, privacy, or support pages were detected.
refreshed 1 months ago
Web search was not run for this scan, so third-party entity mentions could not be measured (not a confirmed gap).
refreshed 1 months ago
Instagram was detected as part of the commerce surface.
refreshed 1 months ago
Visual proof
desktop
Desktop capture of the current brand storefront or homepage.
mobile
Mobile capture of the storefront, where many D2C buyers first inspect trust signals.
desktop
Public Instagram profile capture used to inspect bio clarity, proof surface, and the path from profile to product or WhatsApp.
Source influence map
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 visibility readiness is 20/100.
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 readiness is 28/100.
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 is part of the scanned commerce surface.
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.
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.
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
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
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.
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
Structured data looks valid — Organization, WebSite carry their required rich-result fields.
Recommended additions (valid without them, stronger with them):
Organization.sameAsrecommendedOrganization JSON-LD is missing recommended field `sameAs` — valid, but stronger with it.
No noindex directive found, search engines and AI can index this page.
No <link rel="canonical"> found, duplicate/variant URLs can split ranking signals.
All 1 sampled images have alt text, good for image discovery and screen-reader buyers.
“LensCore · AI Visibility Audit for modern brands” (48 chars).
Present, 113 chars.
Exactly one H1.
The homepage and a key inner page share the same <title> — duplicate titles confuse ranking.
Fix: Give every important page its own descriptive title.
All assets load over https.
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).
Valid Organization structured data with name + url + logo.
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.
5 AI crawler(s) are blocked, those engines cannot read your site to recommend you.
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
Website surfaces
Desktop capture of the current brand storefront or homepage.
If the first desktop view lacks category clarity, proof, or contact confidence, colder buyers bounce before product intent compounds.
Mobile capture of the storefront, where many D2C buyers first inspect trust signals.
Mobile friction is usually where discovery-led interest dies first, especially when popups, weak proof, or clutter block the buying path.
Public social surfaces
Public Instagram profile capture used to inspect bio clarity, proof surface, and the path from profile to product or WhatsApp.
If the public profile does not explain the category, trust, and next step in seconds, discovery stays social but conversion intent leaks before the site visit.
Source influence map
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 9/9/2026
AI visibility readiness is 20/100.
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 9/9/2026
Google trust readiness is 28/100.
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 9/9/2026
Instagram is part of the scanned commerce surface.
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 9/9/2026
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 9/9/2026
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 9/9/2026
Prioritised issues · finding → fix
3 critical issues costing you buyers right now, then 1 opportunity. Click any row: the dropdown is the fix.
The Fix Kit · paste-ready
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.
Paste each block inside its own <script type="application/ld+json"> tag in your site's <head>. Put the Organization block site-wide; put a Product block (with the real name/price) on each product page.
Illustrative template — replace every REPLACE_WITH_* value before publishing
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "LensCore",
"url": "https://lenscore.app",
"description": "See how AI search engines recommend your brand or your competitors. Live AI audit, evidence-led score, free scan.",
"sameAs": [
"https://instagram.com/lenscore.app"
]
}
{
"@context": "https://schema.org",
"@type": "Product",
"name": "LensCore Software And Developer Platforms",
"brand": {
"@type": "Brand",
"name": "LensCore"
},
"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"
}
}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": "Yes — we accept COD, UPI, cards, and net banking 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": "See our size chart on each product page, or message us on WhatsApp for help."
}
},
{
"@type": "Question",
"name": "How do I contact LensCore?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Reach us on WhatsApp or email — we reply within one business day."
}
}
]
}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's social surfaces are complete enough to judge the full discovery path. Focus on conversion and proof density.
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
The Instagram bio states the product category and includes a buying path — a strong foundation for social discovery.
Next: Keep the bio tight — each word competes with the scroll. Test one clear category + one CTA.
CTA & link-in-bio quality
The Instagram bio directs visitors toward a next step, whether a link, DM prompt, or website click.
Next: Test link-in-bio tools (Linktree, Beacons, own landing page) that route visitors to the right product without friction.
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: 337 — 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.