SEORCE - See where your brand is discovered and fix what blocks it

@cruise_chen 
Thank you 🙌 Really glad that clarity around brand awareness is coming through.

A few simple suggestions we usually recommend after you start seeing the data:

• Compare yourself with competitors in AI responses to spot positioning gaps

• Align your content with how AI already describes your category, then refine what’s missing

• Track changes over time, not just one snapshot, to see what actually improves visibility

• Use insights to tighten messaging across your site, blogs, and FAQs so AI picks it up more clearly

We’re also actively improving SEORCE based on feedback like this, so if there’s anything you feel could be clearer or more useful, we’d love to hear it 🚀

@urvashi_misal Great question. We treat AI answers as a new crawlable surface, similar to early search engines, but built for generative systems.

We continuously run structured, versioned prompt graphs across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews to account for model updates and response volatility.

Responses flow through an entity resolution layer that detects explicit and implicit brand mentions using semantic matching, alias graphs, and context validation (not simple string matching).

Mentions are then normalised into a unified visibility schema factoring prominence, sentiment, authority, and citation context, so insights remain comparable across fundamentally different models.

Finally, we snapshot everything over time to detect drift and changes in real time, and use SLMs to map visibility gaps to root causes and auto-fix recommendations.

The outcome is a real-time, model-agnostic visibility layer for AI-driven discovery, focused not just on what changed, but why and how to influence it.

@vik_sh 
Thank you so much 🙏 Really glad that framing resonated.

What teams usually uncover first are the quiet blind spots that don’t show up in traditional SEO tools. A few common ones:

• Missing entirely in AI answers for category-level questions, even though they rank well on Google

• Being mentioned, but positioned weakly (for example, listed as an option, not a recommendation)

• Competitors showing up for use cases or geographies they assumed they already owned

• AI pulling from third-party pages instead of their own site, which signals gaps in content or clarity

SEORCE surfaces these by running consistent prompts across AI tools and comparing responses over time, so teams can see where they’re invisible, why that’s happening, and what to fix first.

Most teams start by fixing just one or two of these blind spots and see clarity very quickly.

@chris_wyatt2 The pricing is available on the website, unless you are an enterprise / large agency, the pricing is up on the website here:

https://www.seorce.com/pricing

@dubd59 Thanks so much, really appreciate that 🙏

Would love to hear your thoughts once you’ve had a look.

We’re building this very openly and feedback helps a lot.

@vipin_jain3 
In AI answers, brands are often mentioned without any link at all, which makes classic tracking miss a huge part of discovery. SEORCE doesn’t rely on links to detect visibility.

Here’s how we handle it:

• We scan LLM responses directly and identify brand mentions even when there’s no citation or URL

• We capture the context of the mention ,whether you’re recommended, compared, or just referenced in passing

• We track frequency and consistency of those mentions across repeat runs, so it’s not based on a single response

• We compare those unlinked mentions against competitors to show who’s getting mindshare inside AI answers

This way, you can see where your brand is influencing AI-generated answers, even when there’s no click or backlink and spot opportunities to turn that visibility into clearer positioning or linked citations over time.

@karan_baror 
Here’s how we do it:

• Prompt coverage: For a defined set of category and use-case prompts, we track which brands appear in AI responses and how often

• Prominence: We look at how a brand appears — primary recommendation, secondary option, or passing mention

• Context weighting: Mentions are weighted based on strength (recommended vs referenced) and sentiment

• Competitive comparison: Your visibility is always measured relative to the same competitor set, so changes are meaningful

• Trend over time: We track this consistently, letting you see gains or losses as AI answers evolve.

  
  The result is an AI-specific share of voice score that shows who owns the narrative inside AI answers, not just who ranks on a page.

@xiangce_sun 
Appreciate you calling that out 🙏 That fragmentation was exactly what pushed us to build SEORCE this way. Our goal is to connect the signals across SEO, content, and authority so teams spend less time jumping between tools and more time actually acting on insights. Really glad that approach resonates!

@xiangce_sun Totally agree, and that fragmentation is exactly the problem we’re trying to remove.

SEORCE isn’t meant to be another dashboard you check once a week. It’s a connective layer that ties together signals that already exist but live in silos today - content, technical SEO, authority, and now AI-driven discovery.

Instead of asking teams to interpret five tools and guess what matters, we focus on:

Connecting cause → effect (what changed in AI answers and why)

As discovery shifts from links to answers, the real advantage isn’t more data - it’s clarity and continuity across systems. That’s the layer we’re building.

@on_ling Love to hear that, thanks for giving SEORCE a shot 🙌

That’s exactly how most teams start: track visibility over time, watch how different AI systems position you, and then spot patterns you simply couldn’t see before. As you test it, keep an eye on drift across models and intents, that’s usually where the biggest “aha” moments show up.

If you hit any questions or want to go deeper while you’re exploring, feel free to drop a note. Excited to hear what you uncover.