1. "My brand never appears"
Why this happens
The query is too broad (e.g., "best skincare brands in India").
AI models don’t have enough structured signals about your brand.
The brand name has variations or spelling differences.
The prompt doesn’t anchor the right audience, geography, or use case.
How to fix it
Get specific: include product type, geography, audience, and brand purpose.
Example: "Best gut-health supplements for dogs in India (DTC brands only)."Test known-fit queries: e.g., category queries where you know you are strong.
Check brand name variations: e.g., "Unleash Wellness," "Unleash Pet Wellness," "Unleash Dog Supplements."
Improve online presence (speculative but proven to help): strengthen structured pages, FAQs, reviews, and internal linking.
Run Snezzi’s visibility scan to confirm if AI engines currently “understand” your brand profile.
Pros
Quick wins by adjusting prompt structure.
Reveals whether issue is prompt-related or visibility-related.
Cons
Some models naturally deprioritize smaller brands until visibility improves.
Requires ongoing testing across engines.
2. "Results vary across models"
Why this happens
Different LLMs have different training data windows.
Some prioritize international brands; others boost local or DTC brands.
Model specialization (e.g., Gemini skews toward Google-indexed sources; Claude leans toward editorial quality).
What to do
Treat variance as normal.
Track patterns and trends, not single responses.
Don’t optimize solely for one engine — Snezzi’s GEO strategy balances Google + ChatGPT + Claude + Perplexity.
Pros
Cross-engine visibility gives stronger omnichannel discoverability.
Helps brands identify which model they need to "train" via content and distribution.
Cons
Inconsistency may confuse early users without guided interpretation.
3. "It shows competitors I don’t recognize"
Why this happens
Models often surface emerging, adjacent, or international competitors.
Some have similar product benefits but different categories.
Sometimes they surface benchmark brands, not market-share competitors.
How to evaluate
Check if they share:
Your category
Your use case
Your benefits
Your audience
If irrelevant, note them — they often indicate content gaps or weak contextual signals for your brand.
Pros
Surfaces new positioning insights or untapped subcategories.
Helps discover competitors you weren’t tracking.
Cons
Noise increases when prompts are broad or unstructured.
Global models sometimes over-index on US/EU brands.
4. "My brand appears sometimes and disappears other times"
Why this happens
AI engines use probabilistic reasoning — borderline brands fluctuate.
Missing structured content signals (FAQs, authoritative pages, category pages).
Weak presence in top-cited sources (blogs, reviews, listicles, UGC).
Fixes
Strengthen category authority via programmatic SEO + FAQs.
Add clear brand description blocks on key pages.
Increase external references (reviews, listicles, PR).
Use Snezzi’s prompt tracking to see patterns by day/model/query.
5. "The prompt output contradicts my brand positioning"
Why this happens
Model relies on general category averages.
Brand context provided is too thin.
Competitors dominate informational content.
Fixes
Add brand anchors into prompts: USP, ingredients, purpose, geography.
Example: "Natural gut-health supplements for dogs in India with pumpkin, prebiotics, probiotics."
Pros
Creates more consistent brand-driven outputs.
Reduces hallucinations and generic phrasing.
Cons
Slightly longer prompts required.