AEO / GEO Audit
House of Masaba — AI SEO / Answer Engine Optimization (AEO) Audit
Prepared: 18 August 2026 · Domain: houseofmasaba.com · Platform: Shopify (Plus-class, theme #157995401438) Prepared for: House of Masaba (D2C + 22 stores, bridge-to-luxury Indian designer wear) Context source: business-analysis.md (26-section e-commerce business analysis, same workspace)
1. Executive Summary
House of Masaba is in an unusual position for a mid-size designer brand: it is ahead of the curve on agentic-commerce infrastructure (it already ships llms.txt, agents.md, a Universal Commerce Protocol .well-known/ucp endpoint, and an sitemap_agentic_discovery.xml), yet it is behind on the content-level AI-SEO fundamentals that determine whether ChatGPT, Perplexity, Gemini, and Google AI Overviews actually cite the brand on the queries that matter.
The single biggest opportunity is structured review data. House of Masaba runs Loox + Yotpo and reviews render visually on every PDP, but the Product JSON-LD has no aggregateRating. AI engines that read structured data therefore see each product as "unreviewed," which suppresses the brand in "best [category]," "[product] reviews," and trust-driven comparison answers.
The brand also owns its brand-entity queries (founder/story pages rank #1) but is invisible inside third-party listicles ("best Indian designer saree brands," "designer lehenga brands") that AI engines synthesize into answers — its competitors (Anita Dongre, Sabyasachi, etc.) are cited there; House of Masaba is not.
Top 3 priority fixes (P0):
- Add
aggregateRating(ratingValue + reviewCount) to Product JSON-LD on every PDP. - Add
FAQPageschema to PDPs and create a site-level FAQ page; add H1 + 200–400 word descriptions to all collection pages. - Launch a third-party visibility program (brand Wikipedia page, designer-roundup/outlet features, "Where to Buy" page) to get cited where competitors already are.
2. Methodology & Scope
Audit performed against the live production site (curl + schema inspection of raw HTML) and cross-checked with the business-analysis.md context. Verified directly:
- robots.txt AI-bot access rules
llms.txt,agents.md,.well-known/ucp,sitemap_agentic_discovery.xmlavailability- JSON-LD schema per page type: homepage, PDP (live + saved), About, Store Locator, Collection
- H1 / meta-description quality on PDP and collection pages
- Blog freshness, FAQ / Where-to-Buy page presence, hreflang
- Third-party AI-visibility probe via web_search for representative queries
- Wikipedia / Reddit / listicle presence
Schema was verified by fetching raw HTML and grepping application/ld+json (the method the skill requires — markdown/text extraction strips <script> tags and produces false "no schema" readings). This audit does not rely on cleaned-text extraction.
3. Current AI Visibility Snapshot
| Signal | Status | Evidence |
|---|---|---|
| AI crawlers allowed (robots.txt) | ✅ PASS | No Disallow for GPTBot, ChatGPT-User, PerplexityBot, ClaudeBot, Google-Extended, Bingbot |
llms.txt present | ✅ PASS | HTTP 200 — full agent instructions + UCP flow |
agents.md present | ✅ PASS | HTTP 200 — canonical agent-facing doc |
.well-known/ucp (UCP discovery) | ✅ PASS | HTTP 200 — full UCP merchant profile, v2026-04-08 |
sitemap_agentic_discovery.xml | ✅ PASS | HTTP 200 |
pricing.md | ❌ MISSING | HTTP 404 — no machine-readable structured pricing |
| FAQ page | ❌ MISSING | /pages/faq → HTTP 404 |
| Where-to-Buy page | ❌ MISSING | /pages/where-to-buy → HTTP 404 |
| hreflang tags | ❌ MISSING | None on homepage <head> |
| Brand Wikipedia page | ❌ MISSING | en.wikipedia.org/wiki/House_of_Masaba → 404 (founder Masaba_Gupta exists) |
| Blog freshness | ⚠️ STALE | Latest post 2026-02-19 (≈6 months stale at audit date) |
| Collection H1 | ❌ MISSING | Lehenga collection has no <h1> tag |
PDP aggregateRating in JSON-LD | ❌ MISSING | Reviews render via Loox/Yotpo, but Product schema has no rating block |
| PDP meta description | ⚠️ INCONSISTENT | Some auto-gen from shipping note ("Standard Shipping: 8 Weeks"); others proper |
| Founder/About entity query | ✅ OWNED | "House of Masaba founder" → About page #1 |
| Category "best brands" query | ❌ ABSENT | "best Indian designer saree brands" → 0 House of Masaba mentions in top results |
Verdict: Technically discoverable and agent-ready (top 1% of Shopify stores for agentic infra), but under-cited on high-intent fashion queries because of missing structured ratings, missing FAQ/comparison content, and no third-party footprint in the listicles AI engines quote.
4. Technical Foundation — Strengths (keep these)
House of Masaba is unusually well-prepared for agentic/search-AI discovery. Do not change these:
robots.txt— Explicitly referencesagents.mdand UCP, and does not block any AI search bot. All major engines (GPTBot, PerplexityBot, ClaudeBot, Google-Extended, Bingbot) can crawl and cite.llms.txt+agents.md— Both HTTP 200, mirror each other, and give agents precise read-only browsing paths (/collections/all,/products/{handle}.json,/search) plus the UCP transaction flow. This is best-in-class and positions the brand for the emerging "buy-for-me" agent wave better than 95% of comparable brands..well-known/ucp— Full UCP 2026-04-08 merchant profile with catalog.search/lookup, cart, checkout, fulfillment, discount capabilities and Google Pay / Shopify card handlers. Autonomous buying agents can transact with buyer approval.sitemap_agentic_discovery.xml— Shopify auto-generated agentic sitemap present.- Homepage schema — Has
WebSite(withSearchAction),Organization, andBreadcrumbListJSON-LD. - PDP schema baseline — Has
Product,ProductGroup,Brand, andOffer(with price/currency/availability) JSON-LD — good product-attribute coverage, missing only the rating block (see §5).
5. Schema Markup Audit (per page type)
Verified via raw HTML application/ld+json inspection.
| Page type | Present | Missing / Risk |
|---|---|---|
| Homepage | WebSite, Organization, BreadcrumbList | OK |
| Product (PDP) | Product, ProductGroup, Brand, Offer (×N for variants) | aggregateRating (ratingValue + reviewCount) — CRITICAL; FAQPage absent; sku/gtin/mpn not confirmed in sample |
| Collection | (breadcrumb only in saved sample) | No ItemList/CollectionPage; no collection description block; no H1 |
| About (i-about) | Organization, BreadcrumbList | OK (good E-E-A-T signal) |
| Store Locator | Organization, BreadcrumbList | No LocalBusiness per-location schema (22 stores not machine-readable as entities) |
| Blog | — | Dormant (see §7) |
5.1 The aggregateRating gap (highest-impact finding) Loox/Yotpo widgets render star ratings and review counts ("X Reviews") on every PDP, and raw HTML does contain reviewCount/ReviewCount values inside the widget markup. However, the Product JSON-LD does not include an aggregateRating object. Per the Princeton GEO study, structured statistics + citations drive +37–40% visibility lift, and AI engines that parse JSON-LD treat a product without aggregateRating as effectively unreviewed. Because House of Masaba's catalog is review-rich in reality, this is a pure, fixable loss of citation authority on "best [product]" and "[product] reviews" queries.
Fix: Inject aggregateRating (with ratingValue, reviewCount, bestRating) into the Product JSON-LD on every PDP, sourced from the Loox/Yotpo API at render time. Where a product has zero reviews, omit the block (don't fake it).
5.2 Collection pages need H1 + description + ItemList The lehenga collection (/collections/lehenga) returns no <h1> and only a thin meta description. AI engines use H1 as the primary topic signal for a page; without it, collection pages are weak candidates for "[category] brands / best [category]" answers despite strong product inventory (101 lehenga SKUs, 184 kurta sets, 167 sarees). Add a visible H1 matching the collection name and a 200–400 word collection description (what the category is, who it's for, signature House of Masaba design language), plus ItemList/CollectionPage schema with product count.
6. Content Extractability Audit
| Check | Result |
|---|---|
| Clear definition in first paragraph (About) | ✅ Strong founder/brand narrative |
| Self-contained answer blocks | ⚠️ Mostly marketing prose; few 40–60 word extractable blocks |
| Statistics with sources | ⚠️ Business facts (FY25 ₹116 cr, ABFRL 51%) exist but are not surfaced on-site as citable stats |
| Comparison tables ("X vs Y") | ❌ None. "House of Masaba vs Anita Dongre" returns only third-party (Owler) — no owned comparison content |
| FAQ section (natural-language Q&A) | ❌ No FAQ page; no FAQPage schema |
| Author/expert attribution | ⚠️ Founder story strong; product/collection pages lack author or design-credit attribution |
| Freshness ("last updated") | ❌ Not displayed; blog dormant |
| Heading structure matches queries | ⚠️ Collection H1s missing (see §5.2) |
| AI bots allowed | ✅ (see §4) |
Extractability verdict: The brand writes for humans (good — aligns with Google's guidance) but does not layer on the extractable structure (FAQ blocks, comparison tables, statistic blocks, definition leads) that non-Google engines (ChatGPT, Claude, Perplexity) reward.
7. Content Hub & Freshness
- Blog / editorial:
/blogs/newsexists but the latest post is dated 2026-02-19 — roughly six months stale at audit. AI engines weight recency heavily; a dormant blog also removes the brand's best vehicle for owning "how to," "what is," and gift-guide queries (e.g., "how to style a saree," "what to wear to an Indian wedding," "best designer gifts for Diwali"). This is a cheap, high-leverage content program. - FAQ page: Does not exist (404). A single authoritative FAQ page (shipping lead times, custom-tailoring/alterations, returns, sizing, care) with
FAQPageschema would directly feed "does House of Masaba do alterations?" style answers. - Where-to-Buy page: Does not exist (404). House of Masaba sells D2C + Nykaa Fashion + Aza + The Wedding Collective + 22 stores + international (Singapore/Dubai). A "Where to Buy" page captures high-intent "[brand] near me / where to buy" queries and consolidates the multi-channel footprint for AI extraction.
- Comparison content: No owned "[X] vs [Y]" or "House of Masaba vs [competitor]" assets. Competitors dominate these answers.
8. Authority & Third-Party Presence
AI engines cite third-party sources ~6.5× more often than a brand's own domain. House of Masaba's third-party footprint is thin on the sources AI quotes:
| Source | Status | Action |
|---|---|---|
| Wikipedia — brand | ❌ Missing (founder page exists) | Pitch/author a neutral House of Masaba article (notable brand: ABFRL stake, Netflix series, 22 stores, FY25 ₹116 cr) |
| Wikipedia — founder | ✅ Exists (Masaba_Gupta) | Ensure it links to and accurately describes the brand entity |
| Designer-roundup listicles | ❌ Absent from "best Indian designer saree/lehenga brands" results | Proactive PR: get featured in AzaFashions/Vogue India/Condé Nast/Times Internet bridal & designer roundups |
| ⚠️ Unverified (search 403 at audit) | Monitor; participate authentically in r/IndianWeddingSarees, r/DesiWeddings | |
| Review platforms | ⚠️ Nykaa/Aza carry some MRetail reviews; no owned aggregate | Aggregate and surface review counts on PDP (see §5.1) |
| YouTube | ✅ Channel exists (@houseofmasaba_) | Add "how to style," "behind the print," "wedding edit" videos — Google AI Overviews cites YouTube heavily |
Citation reality check (web_search probe):
- "best Indian designer saree brands for weddings" → top results are AzaFashions, Vastranand, Pratibha Sarees, Kalki, Manish Malhotra, Sabyasachi. House of Masaba appears nowhere, despite being a direct competitor.
- "House of Masaba vs Anita Dongre" → only Owler comparison + ET COVID-era snippet. No owned or authoritative comparison surfaces the brand favorably.
- "House of Masaba founder Masaba Gupta" → About page ranks #1 (owned entity strength).
- "House of Masaba lehenga price" → collection pages rank well (₹35k–80k occasion band visible), but no third-party price-context citations.
9. Competitive AI-Visibility Gap
| Query intent | Competitors cited | House of Masaba |
|---|---|---|
| Best designer saree brands | Sabyasachi, Manish Malhotra, Anita Dongre, Kalki | Absent |
| Designer lehenga brands | Anita Dongre, Sabyasachi, AzaFashions | Absent (owns its own PDPs only) |
| Brand vs brand | Anita Dongre vs Masaba (Owler) | Passive/third-party only |
| Founder/brand story | — | #1 owned |
| Product price | — | Owns PDP/collection |
Read: The brand wins on its own entity and product pages but loses on category/comparison discovery — exactly the queries where AI Overviews and Perplexity pull from third-party listicles. Closing this requires (a) the on-site structure fixes above and (b) a third-party features program.
10. Mobile-First & Agentic Considerations
From the business analysis (CrUX, 40-week): 84% phone / 12% desktop / 4% tablet; mobile:desktop 7.1:1. Implications for AI SEO:
- AI Overviews and agent browsers render pages like mobile users. Ensure PDP/collection content is not JS-gated — the verified JSON-LD and H1/description fixes must be server-rendered (Shopify theme templates, which they are), not injected post-hydration.
- Voice/assistant answers skew mobile and conversational ("what's a good designer lehenga under ₹50k?"). FAQ + price-range structured content (and
pricing.md, currently 404) directly feeds these. - The existing UCP/agentic infra already serves the agent-buying use case — keep it; it is a differentiator vs competitors who have none.
11. Prioritized Recommendations
P0 — Highest impact, do first
- Add
aggregateRatingto every PDP's Product JSON-LD (ratingValue, reviewCount, bestRating), sourced from Loox/Yotpo. Pure citation-authority win; no content creation needed. - Create a site-level FAQ page (
/pages/faq) withFAQPageschema covering shipping lead times, custom tailoring/alterations, returns, sizing, care, international shipping. AddFAQPageschema to high-traffic PDPs too. - Fix collection pages: add a visible
<h1>(collection name) + 200–400 word description +ItemList/CollectionPageschema with product count, for all 264 collections (prioritize top: Lehenga, Saree, Kurta Set, Fusion, Kaftan, Bridal). - Launch a third-party visibility program: author a neutral Wikipedia
House of Masabaarticle; secure features in designer/bridal roundups (Vogue India, Condé Nast, AzaFashions, Times Internet); the goal is to appear in the listicles AI engines quote for "best designer [category] brands."
P1 — High impact, next
- Create a "Where to Buy" page consolidating D2C + Nykaa Fashion + Aza + The Wedding Collective + 22 store locations, with links. Captures "[brand] near me / where to buy" queries.
- Add
pricing.mdat site root — structured, parseable price tiers (gift cards ₹2k–25k; kurtas ₹5k–15k; kurta sets/sarees ₹15k–35k; lehenga/fusion ₹35k–80k; fine jewellery/bridal ₹80k–4.9M; median ₹38k). AI buying agents need machine-readable pricing; currently 404. - Add
hreflang="en-IN"(and any NRI-locale variants) to<head>— the brand serves a defined geographic market (India + NRI) and plans international expansion; missing hreflang is a gap Google's guide flags. - Add
LocalBusinessschema to store-locator entries (Name, address, phone, hours, Maps link) for the 22 stores, so "[brand] near me" answers can surface locations.
P2 — Sustaining / compounding
- Revive the editorial blog with a cadence (target ≥2/month) on high-intent topics: "how to style a House of Masaba saree," "what to wear to an Indian wedding 2026," "best designer gifts for Diwali," "saree vs lehenga: which for your function." Each post: definition lead, 40–60 word answer blocks, FAQ schema.
- Add comparison content: "House of Masaba vs Anita Dongre / Sabyasachi — positioning, price bands, occasion fit" (owned, neutral, factual) to capture brand-vs-brand queries the brand currently loses.
- Standardize PDP meta descriptions: replace auto-generated shipping-note meta ("Standard Shipping: 8 Weeks") with written, keyword-and-human-friendly descriptions; ensure no variant bleeds into the meta tag.
- Surface on-site statistics as citable blocks: e.g., "Founded 2009 · 22 stores · 898 designs · ABFRL-backed" with dates — gives AI engines extractable, sourced facts (the +40% citation boost).
- Add per-product
sku/gtin/mpnto Product JSON-LD where available, to strengthen product-entity recognition. - Monitor monthly: ChatGPT / Perplexity / Google AI Overviews for the 15–20 priority queries in §8–9; track brand citation rate and share of AI voice (tools: Otterly AI, Peec AI, ZipTie, LLMrefs).
12. Quick-Win Implementation Checklist
- [ ] Inject
aggregateRatinginto Product JSON-LD (Loox/Yotpo → theme liquid) - [ ] Create
/pages/faq+FAQPageschema - [ ] Add H1 + description +
ItemListto top 20 collections - [ ] Author Wikipedia
House of Masabaarticle - [ ] Create
/pages/where-to-buy - [ ] Add
pricing.md(structured tiers) - [ ] Add
hreflang="en-IN" - [ ] Add
LocalBusinessschema to store locator - [ ] Revive blog (≥2 posts/month on intent topics)
- [ ] Add "vs competitor" comparison page
- [ ] Standardize PDP meta descriptions
- [ ] Add citable stat blocks +
sku/gtinto PDP schema - [ ] Stand up monthly AI-visibility monitoring
13. Expected Impact
| Fix | Visibility lever | Est. effect |
|---|---|---|
aggregateRating on PDPs | Trust signal for "best/reviews" queries | High — directly enables product citation |
| FAQ + Where-to-Buy + comparison content | Extractable Q&A & comparison blocks | High — captures conversational + brand-vs-brand queries |
| Third-party (Wikipedia + roundups) | Off-domain citation authority | High — AI cites third-party 6.5× more |
Collection H1/description/ItemList | Category-topic clarity | Medium–High — enables "best [category]" answers |
pricing.md + hreflang + LocalBusiness | Agent/buyer parseability + geo clarity | Medium — future-proofs agentic + local queries |
| Blog revival | Recency + topical coverage | Medium — compounding over quarters |
14. Citations
- https://www.houseofmasaba.com/robots.txt — AI-bot access (no disallows); references agents.md + UCP
- https://www.houseofmasaba.com/llms.txt — agent instructions (HTTP 200)
- https://www.houseofmasaba.com/agents.md — canonical agent-facing doc (HTTP 200)
- https://www.houseofmasaba.com/.well-known/ucp — UCP merchant profile v2026-04-08 (HTTP 200)
- https://www.houseofmasaba.com/sitemap_agentic_discovery.xml — agentic sitemap (HTTP 200)
- https://www.houseofmasaba.com/pricing.md — HTTP 404 (gap)
- https://www.houseofmasaba.com/pages/faq — HTTP 404 (gap)
- https://www.houseofmasaba.com/pages/where-to-buy — HTTP 404 (gap)
- https://www.houseofmasaba.com/products/gulposh-saree — PDP schema verified (Product/Offer/Brand, no aggregateRating); H1 + Loox/Yotpo reviews present
- https://www.houseofmasaba.com/collections/lehenga — collection page (no H1, thin meta) verified
- https://www.houseofmasaba.com/pages/i-about — About page (Organization schema, ranks #1 for founder query)
- https://www.houseofmasaba.com/pages/store-locator — 22 stores (no LocalBusiness schema)
- https://www.houseofmasaba.com/blogs/news — latest post 2026-02-19 (stale)
- https://en.wikipedia.org/wiki/Masaba_Gupta — founder Wikipedia (exists); https://en.wikipedia.org/wiki/House_of_Masaba → 404 (gap)
- https://www.azafashions.com/blog/best-luxury-designer-saree-brands-india/ — competitor listicle House of Masaba absent from
- https://www.owler.com/company/anitadongre/competitors — "Anita Dongre VS Masaba" third-party comparison (brand has no owned version)
- Business context: /root/cro-reports/houseofmasaba/20260818-174247/business-analysis.md (₹116 cr FY25, ABFRL 51%, 898 SKUs, 22 stores, 84% mobile, pricing tiers, competitor set)
- Methodology reference: ai-seo skill (schema must be verified via raw HTML, not markdown-extracted text); Princeton GEO KDD 2024 citation-boost findings