The B2B Podcast Index
The Ecommerce Toolbox: AI in Retail

How Noibu is automating ecommerce outcomes through verticalized AI

The Ecommerce Toolbox: AI in Retail · 2026-06-03 · 10 min

Substance score

34 / 100

Five dimensions, 20 points each

Insight Density7 / 20
Originality6 / 20
Guest Caliber9 / 20
Specificity & Evidence6 / 20
Conversational Craft6 / 20

What our scoring noted

Our reviewer’s read on each dimension, with quotes from the episode.

Insight Density

7 / 20

The episode delivers a handful of mildly useful framings—AI copilots as the wrong strategy, outcomes over queryable data—but they are underdeveloped product-marketing talking points rather than substantive operator insight. Most of the runtime is promotional filler and repetition of the same 'thousand interns' metaphor.

we actually think the AI copilot's the wrong strategy
people wanted less data and less AI queryable data and really what they wanted was outcomes

Originality

6 / 20

The 'copilot is wrong, outcomes are right' framing has a contrarian surface but is never argued with any rigor or evidence beyond product positioning. The verticalized-AI thesis is genuinely interesting but gets one sentence; the rest recycles common 2024-era AI-wrapper messaging.

we actually think the winning value is going to be connecting all of your data into a single source of truth
we think the future is verticalized tools, because not only is it the context window, but it's also the data structure in the MCP

Guest Caliber

9 / 20

The speaker is a founder/executive at a nearly decade-old ecommerce platform with real customer traction in mid-market and enterprise, giving them legitimate practitioner credibility—but the talk is delivered entirely in sales mode, which suppresses the depth of experience that caliber could otherwise provide.

We've served mid market and enterprise customers and Shopify in every platform for a long time, almost a decade.
we wanted to test this with some of the smaller brands to see how resilient it is

Specificity & Evidence

6 / 20

There is exactly one named brand example ('cars and cards on Shopify') with one concrete metric (conversion rate from 0.8 to 1.1% over 30 days) and a brief CLS mention—everything else is vague superlative claims with zero dollar figures, no cohort data, and no reproducible methodology.

In just about 30 days their conversion went from 0.8 to 1.1% on the website
A non technical user of ours was able to improve their CLs directly without their agency

Conversational Craft

6 / 20

The audience Q&A rescues the score slightly—a merchant raises a genuinely sharp concern about LLM hallucination risk—but the speaker deflects with 'let it speak for itself' and a sales anecdote rather than a technical or evidence-based answer; there is no host and no real follow-up pressure.

Claude is like, I'm not an engineer, but it's a language model. It might make mistakes.
seven days later, you're begging me not to turn it off is the experience we've seen so far

Conversation analysis

Computed from the transcript - who did the talking, and the verbal tics along the way.

Share of words spoken

  • Speaker A85%
  • Speaker B10%
  • Speaker C5%

Filler words

so22like8actually8kind of4you know2I mean2basically1right1

Episode notes

The current ecommerce landscape is saturated with AI tools that often increase the workload for lean teams rather than reducing it. In this session recorded at The Lead Summit Symposium, host Kailin Noivo explains why the common AI copilot strategy may be contributing to industry fatigue and offers a different path forward. By connecting disparate data sources into a single source of truth, brands can move away from merely querying data and start automating specific business outcomes. The conversation highlights how verticalized software provides the specialized context necessary to avoid common AI hallucinations and deliver reliable results. Kailin shares real world examples of how brands are using these tools to automate complex tasks like ROAS rebalancing and technical performance assessments. The discussion concludes with a vision for the future of digital commerce where automated workflows handle the heavy lifting of data analysis, allowing human teams to focus on high level strategy.

Full transcript

10 min

Transcribed and scored by The B2B Podcast Index.

In Claude, we have a connector. Takes two seconds to set up. Not only do we connect all your behavioral data captured by neibu into Claude, we also connect your third party, your Shopify, all your ads data, and then all you need to do is run a prompt automatically and you could pick your schedule. And it will not only actually find the performance improvement, it will build the business case as to why it should be done, assess the roi, and from there, it'll actually do a merge request automatically and send that to your development team so that they can review it and merge it. Hey, folks, super excited for today's episode. Had the chance of doing a short talk at the lead in New York. It was phenomenal. We got into all things AI. We have some really big things cooking here at NOIBU in terms of AI. So if you're curious about how AI can work for you and how you can implement it into your DTC business, then I would definitely highly recommend listening to this. All right, folks, nice to meet you all. Quick show of hands. Who's been asked or is asking their teams to implement AI into their businesses? Perfect. From there, it's something that we've seen across the board on our customers from small to large, but on the other side of it, who's getting a little bit of AI fatigue because it seems like every product you use is like sending you an email with some stars and saying, hey, great news. We've just launched an AI copilot. We actually think that the AI copilot's the wrong strategy, and I'll explain to you why. So we actually think the winning value is going to be connecting all of your data into a single source of truth to actually connect everything and harmonize across multiple channels. We built a product called noibu. And for those in Ecom, which is, I'm assuming everyone here, we effectively collect heat maps, your UTM parameters, some of your marketing data, your session replays, your errors, and all that really, really valuable data that we typ had locked up at a dashboard. But we've been really starting to lean in AI first and trying to understand what that means for our customers. And really what that means is actually not only connecting our data to your system of record for AI, like your Claude or your OpenAI, but it's also connecting your other ones. And what that enables you to do is unlock some pretty crazy value that I'll talk about today. So just going back super quickly, what would you do if you had a thousand interns tomorrow? Well, you'd probably watch every session Replay, you'd probably look at every heat map. We've taken that approach by enabling folks to connect their data to an LLM but then also providing them specialized skills to be able to extract the value. Not going to ask you to raise your hands again, but I bet you it resonates with pretty much anybody that you're being asked to do more with less. Integrate AI. Everyone I talk to feels very, very constraint when it comes to resources and no one's looking for more projects. So what we realized is people wanted less data and less AI queryable data and really what they wanted was outcomes. So what we built was effectively prepackaged skills that run automatically on your store to do jobs to be done. Great example of that is how often do you rebalance your ROAS between channels? How often do you look at the heat map data from the people landing on the actual website and then correlate it back to informed decisions? I bet you if you had a thousand interns you'd probably do it every day. How often do you take a look at every URL and every campaign to understand why it's slow and open up a ticket with your agency? Well, we've automated that too and the results are pretty crazy. So we've served mid market and enterprise customers and Shopify in every platform for a long time, almost a decade. But we wanted to test this with some of the smaller brands to see how resilient it is. And some of the early outcomes for this brand cars and cards on Shopify is they're able to automate some of their ROAS rebalancing. And no, it's not the Terminator scenario where it's making changes without your permission. It's more serving work to you. Like hey, I noticed that when this happens your ROAS decreases. Do you want to make this change? And the results are pretty crazy. In just about 30 days their conversion went from 0.8 to 1.1% on the website. Here's some quotes. This one's really interesting. A non technical user of ours was able to improve their CLs directly without their agency, which is a core web vital metric, which is really really really cool. But really more importantly, we're talking about the NOIBU MCP which enables you to connect all of your behavioral data into an LLM and then we give you the skills which are effectively the prepackaged prompts to be able to deliver outcomes pretty automatically. I'm going to provide a quick little demo here. So in Claude we have a connector takes two seconds to set up in the connector, not only do we connect all your behavioral data captured by NOIBU into Claude, we also connect your third party, your Shopify, all your ads data and then all you need to do is run a prompt automatically and you could pick your schedule. And it will not only actually find the performance improvement, it will build the business case as to why it should be done, assess the ROI and from there it'll actually do a merge request automatically and send that to your development team so that they can review it and merge it or whoever does that in your business. All of this is automated because in E Comm everyone's teams are lean, everyone's being asked to do more with less and implement AI, but their targets go up every year and the budgets stay flat and in some cases they decrease. So you need leverage somewhere and, and that's why we went down the path of building this product. Lastly, before I wrap up and take questions, what is the data we bring to the table? We bring all of your acquisition data, your behavioral data, all your heat maps, your sessions, your user events, everything related to technical site performance errors, all your session replay. This has always been valuable data, but it's been pretty much locked behind resource capacity issues. No one has the time to look at every session replay, no one has the time to look at every product review and then look at the user journey. We've automated all of that to the tune of a thousand interns. Kind of like how we like to position it as part of a wrap up. And thanking you for taking the time to chat with us today or take a look at my chat at least if you take a demo, we're willing to give you a month free of Claude first 10 people. We're also willing to run this free assessment for you folks. And yeah, I'm going to pause to see if there's any questions, but we're really, really excited about this and we think it's a game changer. Great presentation. You know, as a merchant, when I walk around here, what's different to a couple of years ago is every point solution is now trying to be an everything solution. What I mean is earlier someone was just an email provider. Now that email provider is saying thanks to AI, I can also be your reporting provider, I can be your ROI improvement provider. And so I think for me as a merchant it's confusing. So like, should I move from my 10 point solutions to this one solution while I have you, I'll just ask a second part. As you answer that, I noticed that this is amazing because Basically, what you're doing is connecting all my data to Claude. What I have found is despite the models getting really good, they are not the best statistical models, you know, so Claude is like, I'm not an engineer, but it's a language model. It might make mistakes. So even though you might be giving it really good context data, Claude could be giving me poor hallucinations back. So those are my two questions. Great questions. First off, coming from an X point solution, it's you guys that brought us here to be the platform, because that was the number one feedback we'd get. So everyone was building point solutions. Now everyone's building consolidated platforms, candidly, because that's what people are asking for. They don't want to pay for a monitoring system that has this and then another system that has that, but it kind of has part of this, and then you replicate that over 12 systems. Then you're paying multiple times for the same data capture and storage. So first off, we think that's the dominant strategy. On the second front, 100%. That's why we think the future is verticalized tools, because not only is it the context window, but it's also the data structure in the MCP that's really, really valuable. And I mean, look, candidly, our sales process is evolving to just be connected for you and give it to you, and then seven days later, you're begging me not to turn it off is the experience we've seen so far. So we kind of want to let it speak for itself. And our answer to that is we're a vertical software. We only sell to merchants. We have platform integrations, so we have a high level of context. And then from there we have specialized skills that have been developed over the nine years of our experience. And when you merge all those things together, it's really, really powerful. And we're so confident in it, we're letting people kind of take it for a spin themselves with just take it for a drive. Thank you all for the time and I really appreciate the chat. The E Commerce Toolbox AI in retail is brought to you by noibu. To find out more about noibu and how we unify error monitoring, site performance and experience analytics to uncover growth opportunities and skyrocket your revenue, visit www.noibu.com that's n o y bu.com and then make sure to search for the EE Commerce Toolbox AI and retail on Apple Podcast, Spotify or anywhere else podcasts are found and click subscribe so you don't miss out on any future episodes. On behalf of the team here at Noibu, thanks for listening.

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