27 May 2026
Tilsa appears in 74% of AI-engine answers, making it the most frequently mentioned brand in this competitive set. Minimalist follows with 14 mentions and Forest Essentials with 12 mentions, while Tilsa's absences are most concentrated in core intents, where it is missing from 3 queries. Commercial, trust, and use-case intents each carry at least one absence as well.
Who wins your category
Who AI names most when buyers ask about this category, and how much of the conversation each brand owns.
What they own that you don't
The features AI gives your rivals, not you. When buyers want these, they pick someone else.
When buyers ask, AI credits rivals with these 8 features. We couldn't read your site this scan, so we can't say which you already have.
Treat this as a checklist, not a gap list. Tick the ones you already do, those just need to be readable.
Where you lose
When the AI skips you, these rivals get named instead. Each column is a different kind of buyer question.
| Competitor | Category discovery | Specific use-cases | Pricing research | Vs competitors | Trust signals | Brand-name search |
|---|---|---|---|---|---|---|
| Minimalist | 5 (36%) | 2 (18%) | 4 (44%) | 6 (27%) | 1 (20%) | 3 (50%) |
| Forest Essentials | 4 (29%) | 4 (36%) | - | 5 (23%) | 2 (40%) | 2 (33%) |
| Foxtale | 3 (21%) | 3 (27%) | - | 4 (18%) | - | - |
| Plum | - | 2 (18%) | - | 3 (14%) | 2 (40%) | 1 (17%) |
| Pilgrim | 2 (14%) | - | 3 (33%) | 2 (9%) | - | - |
Tilsa's citation readiness scores 49.3 of 100, rated Partial, with retrieval strong at 84% but structure weak at 20% and density partial at 47%. The attribute match rate of 47% indicates AI responses do not reflect the brand's own positioning claims. Four site checks are failing: has_canonical, has_schema, has_faq_schema, and has_llms_txt, alongside flagged pages missing titles, h1 tags, and meta descriptions.
What's pulling your score up vs down
Weighted signals drive content readiness. The bottleneck axis is where the most points are being lost.
What to fix
Specific gaps surfaced by the scan, ordered by impact on your readiness score.
- 1Attribute match rate is 47%, AI responses do not reflect the brand's own positioning claims.
- 2Audit flagged: pages missing titles, pages missing h1, pages missing meta descriptions. These gaps reduce how consistently AI can parse and cite structured facts from this site.
Technical site checks
Whether your site is set up for AI engines to crawl, parse, and cite, schema, canonical tags, llms.txt, and friends.
What AI says about Tilsa
Attributes AI consistently uses to describe your brand. These are the words AI reaches for when answering buyer questions about you.
Missing from how AI describes Tilsa
Attributes AI uses about category leaders but doesn't yet attach to Tilsa. Surfacing them in your owned content increases the chance AI picks them up.
Competitors like Minimalist and Forest Essentials are framed around ingredient transparency and integration signals, 'ingredient transparency automation,' 'GST-aware skincare formulation,' 'modular natural personal care', while AI describes Tilsa through basic features and friction: 'skincare formulation processing,' 'customer database,' 'manual workflows.' The gap is product-depth language Tilsa doesn't own in AI outputs. The content move: publish structured, factual content explicitly addressing clean-ingredient sourcing capabilities, ingredient transparency coverage (PF/ESI/TDS/PT), and integration ecosystem to shift AI's associative framing away from basic functionality and toward comprehensive natural personal care capability.
Sample AI response mentioning Tilsa
A representative answer where AI actually named your brand. The framing here is the framing buyers will read.
Where AI gets its info
Source mix across 174 citations found in AI answers about Tilsa. Diverse third-party citations make your AI presence durable.
Each fix below turns a diagnosis into something you can ship today - schema code, email sequences, guides, and drafts. Generate, copy, and deploy.
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 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 press or newsroom page. When AI answers questions about brand credibility or 'is Tilsa legit', press coverage is a strong citation signal. Without a press page aggregating coverage, earned media value is lost.
NPOV-compliant stub article ready to submit via Wikipedia's Article Wizard. Add citations before submitting.
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 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.
Structured markup that tells AI engines who you are - your name, URL, description, founding year, and social profiles. Paste into your homepage <head>.
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.
Structured pricing data that AI uses to answer "how much does X cost?" queries. Add to your pricing page <head>.
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 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.
Verify your fixes
Mark a finding done from your Recommended Roadmap.
After your next scan, this section surfaces exactly what improved, score by score, against the scan you actioned each fix on.