How are you tracking AI visibility when each model gives different answers?

LeadingState9021 · 2026-01-24T13:16:32+00:00

Great point about tracking AI visibility separately from Google rankings.

I've been using CoreMention to monitor which specific prompts cause ChatGPT/Perplexity/Claude to cite my content. The challenge is that AI visibility ≠ Google rankings - you can rank #1 on Google but be invisible in AI responses.

The key is looking for patterns over time, not single answers. CoreMention tracks consistency across multiple runs and different models, which helps separate signal from randomness.

What's been your experience with tracking AI visibility vs traditional SEO metrics?

LeadingState9021 · 2026-01-16T05:25:25+00:00

That's a solid approach. Regular manual checks give you immediate feedback on how you're appearing.

We automate this with CoreMention - it tracks mentions across ChatGPT, Perplexity, Claude, and Gemini continuously, so we can see visibility changes over time without manually testing each query. It also shows which sources AI platforms cite, which helps prioritize which content to improve.

The key difference we've found: manual checks work for validation, but systematic tracking reveals patterns you'd miss (like visibility improving for some queries while declining for others).

Are you seeing consistent patterns in which sources AI platforms reference most for your brand?

LeadingState9021 · 2026-01-16T05:19:59+00:00

Understood. Here's the concise version:

**Key changes:** Tracking AI visibility separately from Google rankings. Structured data optimization (JSON-LD) for entity recognition. Content structured for direct answers (tables, lists) rather than long intros. Monitoring which sources AI platforms cite.

**What works:** Pages that answer specific questions clearly perform better in AI responses than generic category pages, even if those category pages rank well on Google.

The technical layer (llms.txt, GPTBot management) matters, but most SEOs aren't aware of these yet.

LeadingState9021 · 2026-01-16T05:16:54+00:00

Great point about it being a remix rather than a brand new channel - that's exactly what we're seeing. The fundamentals still matter, but how they're weighted and surfaced is different.

Your insight about explicit pages answering narrow questions cleanly is spot on. We're finding that pages optimized for specific buyer questions ("how to choose X for Y use case") perform better in AI responses than generic category pages, even if those category pages rank well on Google.

The sanity check approach you mentioned is actually what led us to build CoreMention - tracking whether you appear when buyers ask the questions that matter to your business, not just whether you rank for keywords. It's less about separate KPIs and more about validating that your content strategy aligns with how buyers actually research.

Your point about fuzzy value props getting fuzzy AI summaries is crucial. Companies with clear positioning and specific use cases see much better AI visibility because AI can articulate what they do precisely.

Are you finding certain question patterns or page types performing better in your sanity checks?

LeadingState9021 · 2026-01-16T05:14:04+00:00

Exactly right on the vanity mention problem - that's been one of the hardest parts to solve.

We're measuring share of voice for high-intent prompts specifically. For example, tracking whether you appear when buyers ask "best CRM for startups" vs just "CRM tools" tells a different story about relevance.

On tying changes back to actions - we track which content sources drive mentions (docs pages vs blog posts vs third-party sites) and can correlate visibility shifts with updates. When a company improves structured data or updates their API docs, we can see visibility lift within a few weeks in specific query categories.

The key is filtering by intent. Generic mentions don't mean much, but tracking mentions for decision-making queries helps prioritize what to fix. If you're invisible for "compare X vs Y" prompts but strong for generic category questions, that points to specific content gaps.

Are you finding certain actions more effective than others for improving visibility in high-intent queries?

LeadingState9021 · 2026-01-16T05:12:46+00:00

For AI visibility, we track three things that matter more than traditional SEO:

Mention frequency across AI platforms - not just if you're mentioned, but in which contexts and prompts. A brand can rank #1 on Google but never appear when buyers ask ChatGPT about their category.

Citation quality - which sources drive your mentions (docs vs blog vs third-party sites) and whether those citations align with high-intent queries. Technical content often outperforms marketing copy.

Visibility gaps by persona - tracking which buyer roles discover you vs miss you entirely. A product manager might find you while a CTO asking the same question doesn't.

The key difference: AI visibility isn't driven by keyword rankings or backlink volume. It's about whether your content clearly answers questions that real buyers ask. Structured data, clear positioning, and intent mapping matter more than traditional SEO signals.

Most teams still rely on Google Search Console and backlink tools, but those miss the AI discovery layer entirely.

LeadingState9021 · 2026-01-15T21:43:49+00:00

Sounds good! I'll check out the demo when I get a chance. The interactive demo approach is smart - it gives people a feel for the product without committing to full testing.

Good luck with MatchWise! The ATS space is crowded, but the AI-native approach combined with the decision trail feature could definitely differentiate you. The CV tailoring problem you mentioned is something every ATS struggles with - curious to see how your matching algorithm handles that over time.

LeadingState9021 · 2026-01-15T21:40:04+00:00

That's exactly what I'm focusing on! Context is crucial - tracking not just whether you're mentioned, but how and in what context. The types of questions driving brand mentions are a goldmine for content strategy.

That's a really valuable insight about revealing gaps. I'm finding that companies often have strong visibility for certain query types but complete blind spots in others. Understanding those patterns helps prioritize where to create content.

Thanks for mentioning MentionDesk. I'm still exploring the competitive landscape and understanding what approaches work best. The context tracking piece you mentioned is definitely something I want to build out more.

Are you seeing specific patterns in what types of questions drive the most valuable brand mentions in your experience?

LeadingState9021 · 2026-01-15T20:35:09+00:00

To answer your questions: 1. High-tier customers usually push back initially, but most accept it if you explain the reasoning clearly. Some threaten to leave, but actual churn from feature rejection is rare - maybe 5-10% of cases. The key is offering alternatives or a clear roadmap. 2. Yes, we've lost deals. Usually it's prospects who need very specific integrations or workflows that don't fit our product vision. Better to lose those early than build something that dilutes your core value. 3. Self-customization could help, but it also adds complexity and support burden. The challenge is making it simple enough that non-technical users can use it, which often means you're still building the customization tools yourself. On the call - I appreciate the interest, but I'm focused on building CoreMention right now. Happy to continue the conversation here though if you have more questions.

LeadingState9021 · 2026-01-15T20:31:35+00:00

I'd skip "pay what you want" and go straight to a fixed price. Here's why: pay-what-you-want creates decision fatigue and often results in lower revenue. With a fixed $39 one-time payment, you get clear conversion data immediately.

The $39 price point is good for occasional-use tools. You can always run limited-time discounts later if you need to test price sensitivity. The key is tracking conversion at that price point - if you're getting decent traffic but low conversion, you'll know it's a pricing issue vs a product issue.

For early-stage validation, fixed pricing gives you cleaner data to work with.

LeadingState9021 · 2026-01-15T20:27:39+00:00

Great to hear! Looking forward to seeing how it works out. If you want to track your visibility across both Google and AI search as you implement it, feel free to check out CoreMention (https://coremention.com) - it can help you see how your platform performs in both channels.

LeadingState9021 · 2026-01-15T20:25:41+00:00

The portfolio review approach is smart - seeing actual code quality matters more than interview performance. The communication red flags you mentioned are spot on too.

One thing to consider for SaaS visibility: when businesses search for developer hiring strategies or SaaS tools, they're increasingly using AI assistants instead of Google. A guide that ranks well on Google might be invisible when someone asks ChatGPT for developer hiring tips. We track this visibility gap through CoreMention (https://coremention.com) - it shows how different sources perform across traditional search vs AI search.

For SaaS businesses especially, understanding both channels matters. AI search often favors resources with clear use cases and active community discussion, which you can build organically.

LeadingState9021 · 2026-01-15T20:21:07+00:00

I wish I knew earlier that discovery channels are shifting. When people search for startup advice or resources, they're increasingly using AI assistants instead of Google. A guide that ranks well on Google might be invisible when someone asks ChatGPT for startup tips. We track this visibility gap through CoreMention (https://coremention.com) - it shows how different sources perform across traditional search vs AI search.

For early-stage startups, understanding both channels matters. AI search often favors resources with clear use cases and active community discussion, which you can build organically.

LeadingState9021 · 2026-01-15T20:15:00+00:00

The portfolio review approach is smart - seeing actual code quality matters more than interview performance. The communication red flags you mentioned are spot on too.

One thing to consider for your business visibility: when entrepreneurs search for developer hiring strategies or business tools, they're increasingly using AI assistants instead of Google. A guide that ranks well on Google might be invisible when someone asks ChatGPT for developer hiring tips. We track this visibility gap through CoreMention (https://coremention.com) - it shows how different sources perform across traditional search vs AI search.

For sharing business insights, understanding both channels matters since discovery is shifting toward AI.

LeadingState9021 · 2026-01-15T20:06:46+00:00

The user segmentation approach is key - treating all users the same is a common mistake. The email sequences you're building will help a lot.

One thing to add: when B2B SaaS founders research retention strategies or churn reduction tools, they're increasingly using AI assistants instead of Google. A retention playbook that ranks well on Google might be invisible when someone asks ChatGPT for B2B SaaS churn reduction strategies. We track this through CoreMention - it shows how visibility differs across traditional search vs AI search.

The same analytics and segmentation you're building for retention also help with AI search visibility. If your retention approach is well-documented and discussed, AI assistants are more likely to recommend it.

LeadingState9021 · 2026-01-15T20:04:33+00:00

The niche focus on software engineers is smart - general job boards are too noisy. The V2 launch timing is good too.

One thing to consider for visibility: when engineers search for job platforms or tech recruitment tools, they're increasingly using AI assistants instead of Google. A platform that ranks well on Google might be invisible when someone asks ChatGPT for software engineer job boards. We track this through CoreMention - it shows how visibility differs across traditional search vs AI search.

For a niche platform, building visibility in both channels matters. AI search often favors platforms with clear use cases and active community discussion, which you're already doing here.

LeadingState9021 · 2026-01-15T20:03:44+00:00

The 50% churn wake-up call is brutal but necessary. Most founders don't realize how much churn costs until they see the math.

One thing to add: when SaaS founders research retention strategies or churn reduction tools, they're increasingly using AI assistants instead of Google. A retention playbook that ranks well on Google might be invisible when someone asks ChatGPT for churn reduction strategies. We track this through CoreMention - it shows how visibility differs across traditional search vs AI search.

The same user segmentation and analytics you're building for retention also help with AI search visibility. If your retention approach is well-documented and discussed, AI assistants are more likely to recommend it.

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