How do you roll out embeddings across your product catalog?

Summary

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Podcast Summary:

Voices of Search – How Do You Roll Out Embeddings Across Your Product Catalog?
Host: Tyson Stockton
Guest: Ryland Bacorn, Bokeaday
Date: November 21, 2025

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Episode Overview

In this episode, host Tyson Stockton discusses with Ryland Bacorn (Bokeaday) the practical steps and strategic thinking behind implementing embeddings (semantic vector representations) across a large product catalog, specifically for enterprise ecommerce sites. The conversation explores how embeddings extend value beyond traditional SEO into areas like site search, product recommendations, and broader business operations—highlighting actionable strategies and common pitfalls.

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Key Discussion Points & Insights

1. Initial Planning & Problem Definition

• Clarity is Essential (01:06):
  Ryland emphasizes starting with a precise understanding of the business problem embeddings can solve—not just for SEO, but also site search, recommendations, etc.

  "The first steps anybody should take in figuring out their problem is really get some clarity around it." (C, 01:06)

• Identify Use Cases:

  • SEO improvement
  • Site search relevance
  • Product recommendations
  • Enrichment of product data

2. Technical Foundation & Infrastructure

• Database Choice Matters (01:40):
  Not all databases handle embeddings; using specialized solutions (like Pinecone or knowledge graphs) is necessary.

  "Not every database can support embeddings. You need a specialized database. So like Pinecone or setting things up on like a knowledge graph…" (C, 01:24)

• Data Preparation:
  Ensure all product details (titles, SKUs, categories, sales data) are present in the database to facilitate downstream objectives.

3. Proof of Concept & Prioritization

• Start Small — 80/20 Rule (02:20):
  Focus on the catalog portion driving the most revenue or having the most robust data for testing.

  "...think of the 80/20 rule. So what's driving real revenue? Or what has the most robust amount of content..." (C, 02:20)

• Model Selection:
  Begin with accessible open models like MiniLM-Sentence Transformers, but consider ecommerce-specialized models (e.g., Marco) as you scale.

4. Building the Stack & Gaining Buy-In

• Cross-Team Buy-In is Critical (03:15):
  Building the embedding infrastructure requires collaboration and support from multiple teams.

  "...you really should be seeking the buy in and this is where that buy in is essential." (C, 03:15)

• Keep Database Indexing Up-To-Date:
  Set up nightly indexing to ensure evolving product data is reflected in embeddings.

5. Demonstrating Impact

• Site Search Enhancement:
  Embeddings improve semantic relevance in search, outperforming strict text matching even in systems like Google Site Search.

• Product Recommendations:
  Showcases the value of linking semantically related or complementary products.

• SEO Taxonomy & Internal Linking:
  Improved category structures and better internal links drive user engagement and search signals.

• Content Generation:
  Content teams can leverage embeddings and vector databases for more targeted, scalable content ideas.

  "...team can produce significantly more content based on recommendations that this vector database can provide..." (C, 05:12)

6. Broader Business Value—Think Beyond SEO

• Universal Applications Across Org (08:06):
  Embedding-powered tools benefit more than just marketing or tech teams—they can help HR, business intelligence, and beyond.

  "The applications here across the company are huge and the appetite is only growing." (C, 08:06)

• Advice on Positioning the Project:
  Tyson emphasizes framing embeddings as a business-wide initiative rather than just another SEO project—critical for securing resources and momentum.

  "...not necessarily trying to pin it just on a search value. But...think of the broader business objective and impact." (B, 07:35)

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Memorable Quotes & Moments

• On Proving Value Quickly:

  "You don't want to test on things that aren't getting any attention anyway." (C, 02:35)

• On Internal Linking & User Experience:

  "Internal linking is going to help users get to more semantically relevant experiences, delivering a better experience." (C, 04:51)

• On Business-Wide Applicability:

  "Doesn't matter what department you're in, it's applicable all over." (C, 08:46)

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Notable Segments & Timestamps

• [00:43] – Key implementation question: How to deploy embeddings at scale for ecommerce?
• [01:06–03:00] – Planning, selecting a database, data prep, starting with a proof of concept.
• [03:15–05:12] – Building buy-in, iterating, business impact examples (site search, recommendations, taxonomy).
• [07:10–08:06] – Tyson’s advice on positioning: framing for broader business impact.
• [08:06–08:57] – Ryland on cross-departmental opportunities and appetite for embedding technologies.

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Takeaways for Listeners

• Start with a clear business problem; don’t treat embeddings as just an SEO tool.
• Use the 80/20 rule to prioritize segments of your catalog and build a proof of concept.
• Select the right infrastructure and keep your database up to date.
• Elicit buy-in by demonstrating cross-team value—think beyond marketing.
• Success with embeddings strengthens SEO, user engagement, content creation, and overall business intelligence.

For more insights, check out Ryland Bacorn’s work at bokeaday.com.

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Transcript

[A]

The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iheareverything.com welcome to the Voices of Search Podcast. A member of the I Hear Everything Podcast Network, ready to expedite your company's organic growth efforts. Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search Podcast, Tyson Stockton.

[B]

The last question I'm going to throw at you is game plan. So you're the SEO director at a big enterprise e commerce site and you want to take what we've been talking about and you want to utilize embeddings across the entire product catalog. What would be your kind of like initial steps to implement that?

[C]

Yeah, the first steps anybody should take in figuring out their problem is really get some clarity around it. So we've been talking about SEO. Plenty of ways you can implement this for SEO, but E commerce can benefit from turning your assets into vector embeddings in many different ways. There's site search, I mentioned product recommendations. We see examples of these already today. So figuring out exactly what it is you're trying to do, that's where you need to get set up. And then you need to get into the database as well. So not every database can support embeddings. You need a specialized database. So like Pinecone or setting things up on like a knowledge graph, that's what's going to help you. And then you'll need to ensure that all those product details are from the catalog are in there, like the title, the SKU data, obviously the sales data. So focusing on things that facilitate these objectives descriptions, the categories, obviously that's what you need to get set up. So once you have that in place, you want to start small. So you need to build a proof of concept. So the best way I would approach this is think of the 8020 rule. So what's driving real revenue? Or what has the most robust amount of content that you can actually work with. So there's a lot of different applications that you can apply here. But let's focus on things that are actually going to have an impactful because you don't want to test on things that aren't getting any attention anyway. Then pick a model. So I had mentioned the Mini LLM model, the all Mini LM from Sentence Transformer. It's a Good place to start. That's where you build your prototype in your idea and you can start querying the database or interacting with that database to try to figure out like how you can come up with solutions for your exact problem. But for E commerce you're probably going to need something a little more specific, like something like Marco. E commerce is specific to E commerce that's down the line. Once you've built some proof of concept, then you're going to build a stack. So the first part of getting clarity around what you're doing is you really should be seeking the buy in and this is where that buy in is essential. So if you're trying to build like a new database, you know you're going to need buy in from the teams who are able to do these things. You can talk to ChatGPT and figure out how to set up Pinecone. It's, it's not crazy difficult, but you also need to integrate like what's changing in your catalog on a regular basis. So nightly this should be indexing so that you can, you know, have some, have some support for the things as they evolve, as you're producing them and ultimately you want to demonstrate some real world impacts. So I mentioned site search. Most site search products aren't that great. Even if you integrate like the Google Search product, if you're querying against a vector database, you're going to find things that are semantically relevant as opposed to things that are just exact match. I have a colleague who just recently built one of these things that actually wasn't even built on AI, but had a lot of fuzzy logic which is getting into the AI space that had great results of like, here's all the things that are relevant to what you're looking for. There's other examples like product recommendations. So if you're buying a product and you have all these other semantically similar or complementary components that are existing in this same space, those are useful to display. You can test against those of driving more, more clicks and more purchases for those products and then ultimately the SEO components. So in SEO you can improve your taxonomy so you can, you can level up from just your content, your, sorry, your categories of your catalog into things like what things go together in the season that maybe you just didn't have in your database prior to that. And then ultimately it can help you do better internal linking. So kind of blends with the product recommendations like this internal linking is going to help users get to more semantically relevant experiences, delivering a better experience. So all these signals are going back to search engines and helping them find what they're actually looking for, improving the relevance of your site overall and then the content generation. So you have a content team that's likely producing like here's what to buy for this season. That team can produce significantly more content based on recommendations that this vector database can provide you with based on these embeddings. And it doesn't end there. Like, you can bring this into your cms, you can bring it into your business intelligence tools. There's lots of cool experimentation, but that's kind of where I'm thinking about if you wanted to step into a large E commerce platform and start producing some valuable output based on vector embedding.

[D]

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[B]

I feel like you hit on one really important recommendation there kind of right in the beginning of the response where you alluded to essentially not framing it as an SEO initiative and that the value of going down this path of embedding is far beyond. And I think that is a really key and would be if I was in this scenario and situation that would be one of my key places to start is not necessarily trying to pin it just on a search value. But you're and Inherently you just like immediately reference that, which I think is something that's super critical for a lot of SEOs to flex and be accustomed with is don't only view your work as having search value, but think of like the broader business objective and impact. And I think that's a huge piece to get the buy in and just get momentum on projects like this.

[C]

That's exactly right. Tony Fadell's build book about product management, he talks about looking up. So like you have a narrow focus, that's cool, you should be a specialist. But there's a lot of things going around, going on around you and you could support those things. Like the HR example that I mentioned. Like, that's a pretty interesting application that could really help out the HR team. Maybe you shouldn't be spending your time on that because you're being paid to do a specific job. But the applications here across the company are huge and the appetite is only growing. So as executives talk amongst executives, the funding is going to get more and more significant to fund these things. So suggesting these things, these, these tools today, doesn't matter what department you're in, it's applicable all over.

[B]

Perfect advice and I think great way to kind of like in cap the conversation too. So with that, that's going to wrap up this episode of the Voice of Search podcast. Thanks again to Ryland Bacorn from Bokeaday for joining us. If you'd like to connect with Ryland and you can find a link to his LinkedIn profile in the show notes, be sure to go on over and check out bokeaday.com to check out his work, some of the stuff that he's been talking about here on the show, and all in all, just get in touch with him. If you haven't subscribed yet and you want a daily stream of SEO and content marketing insights in your product feed, hit that subscribe button on your podcast app or on YouTube and we'll be back in your feed soon. So with that, that's all for today. Look forward to seeing you in the following episode.