Tilsa

Our crawler hit friction reaching Tilsa's site. The signal: only 31% of the pages we tried actually returned useful content. AI engines like GPTBot, ClaudeBot, and Google-Extended will face the same friction. AI bots typically time out faster (5-10s) than our crawler, so they're likely getting even less of your content than we did, which directly limits how often Tilsa can be cited in AI answers.

When AI systems mention Tilsa, they cite Tilsa's own site only 2% of the time (out of 174 total citations captured). The rest are third-party sources: aggregators, review sites, competitor pages, and editorial content describing Tilsa from the outside. This means Tilsa does not control its own narrative in AI answers, third parties do. When those sources change or disappear, visibility changes too.

Tilsa has no Wikipedia article. Wikipedia is AI's most-cited neutral reference, ChatGPT, Gemini, and Perplexity all pull entity descriptions, founding dates, and category labels from Wikipedia when answering brand queries. Without an article, AI models must rely entirely on your own website or third-party review sites to describe Tilsa, sources that carry less authority weight in training data. Brands with Wikipedia articles are described more accurately and with higher confidence by AI models.

Reddit shows only 0 thread-level mentions of Tilsa across the platform. D2C and consumer categories are where Perplexity, ChatGPT web-search, and Gemini lean most heavily on Reddit for 'real-user' sentiment citations in 'Tilsa reviews', 'best X for Y', and comparison prompts. A low mention floor means AI has no community evidence to cite, and will default to competitor narratives.

Tilsa's pages have inconsistent canonical tags, some point to www, others to non-www. This splits link authority and can cause AI crawlers to index duplicate pages, diluting citation quality.

Tilsa has a FAQ page at https://tilsa.in/help but it doesn't appear to use a question-answer HTML structure (dt/dd, h3/p pairs, or details/summary). AI models extract FAQs most reliably from pages with clear Q&A markup, and FAQPage JSON-LD schema.

Tilsa has no detectable collection or audience-specific landing pages. Shoppers ask 'best [category] for {use-case}' or 'best [category] for [audience]', and AI cites brands that publish explicit /collections/[theme] or /for-[audience] pages instead.

Tilsa has a 32% visibility gap between platforms: ChatGPT presence is 90% vs Google AIO at 58%. This usually means Tilsa's content is well-indexed by ChatGPT's retrieval but not by Google AIO's. The two models use different crawling and grounding mechanisms, content that works for one doesn't automatically work for the other.

Tilsa appears in 0/0 runs but with an average strength score of 39%, meaning most mentions are neutral listings ('you could consider Tilsa') rather than active recommendations ('I recommend Tilsa because…'). Neutral mentions don't drive clicks or consideration. AI systems recommend brands more strongly when they can cite specific, verifiable proof.

Tilsa appears in 0/0 runs (good presence) but with an average position score of 0.46, meaning it typically ranks 3rd or 4th when it does appear. Position matters because AI answers present brands in order of relevance, and buyers read top recommendations first. Mama Earth is consistently ranked above Tilsa in shared prompts.

Tilsa's homepage has no Organization JSON-LD schema. AI models use Organization schema to reliably extract: brand name, category, founding date, number of customers, website, social profiles. Without it, AI answers basic factual questions about Tilsa ('what does Tilsa do?', 'when was Tilsa founded?') from training memory, which may be outdated or imprecise. Brands with Organization schema are described more accurately and consistently across AI platforms.

Tilsa does not have an llms.txt file at https://tilsa.in/llms.txt. llms.txt is an emerging standard (proposed by Answer.AI in 2024) that lets site owners describe their company, key pages, and content directly to LLMs, similar to how robots.txt communicates crawl rules. AI systems that support llms.txt can use it to ground answers about your brand more accurately, attribute the right content to you, and understand your site structure without needing to crawl every page. It takes under an hour to create and publish.

Tilsa's pricing page at https://tilsa.in/customers has crawlable price text but no Product or Offer JSON-LD schema. AI models extract pricing from structured schema far more reliably than plain text, schema-annotated prices appear in AI buying-intent responses ('how much does Tilsa cost?') with the brand name attached. Without schema, the price text is deprioritised against competitors who have structured it.