Retailgentic | Consumer Behavior & Retail Trend

This episode was recorded live at the RetailClub AI Festival in Huntington Beach (Sept 22–24), where Jason Goldberg and I set up shop to record with retail and e-commerce leaders between sessions. You’ll hear a few nods to the festival in the conversation, and if there’s a little extra crunch in the audio, blame the snacks.

We start with a quick tour of every CPG brand Justin has worked for, and the very scientific method he uses to pick the next one. From there it turns into a crash course in how challenger brands actually grow: the brutal economics of the physical shelf, why big brand houses are reshuffling their portfolios, and what it really takes to get into Costco and stay there.

There’s also a snack reveal and a crunch our audio engineer will never forget. Hit play.

Highlights 

• 𝗧𝗵𝗲 𝘀𝗵𝗲𝗹𝗳 𝗶𝘀 𝘇𝗲𝗿𝗼-𝘀𝘂𝗺: “We haven’t added any square footage into a shelf.” When an emerging brand earns its way in from the innovation aisle, something else has to come out.
• “𝗜 𝘄𝗮𝘀 𝘄𝗿𝗼𝗻𝗴”: A year ago Justin would have said TikTok Shop would never be a main part of his job. Then came “a tsunami of interest,” and today Wilde runs live selling on TikTok Shop and Whatnot.
• 𝗧𝗵𝗲 𝗖𝗼𝘀𝘁𝗰𝗼 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱: Landing a Costco rotation is the moment brands post the hype video. If you miss the sales threshold, you don’t get a repeat, and the regional buyers talk to each other.
• 𝗦𝗽𝗲𝗲𝗱 𝘀𝗲𝗹𝗹𝘀: “Conversions go up the faster you get delivery.” Justin says ship-from-store is the fastest-growing part of Walmart digital, and if your product isn’t in that store, the conversion you paid Walmart Connect for disappears.
• 𝗘𝘃𝗲𝗿𝘆 𝘀𝗮𝗹𝗲, 𝗲𝘃𝗲𝗿𝘆 𝗭𝗜𝗣 𝗰𝗼𝗱𝗲: With two years of Amazon sales mapped by ZIP code, Justin can see that barbecue over-indexes in the Southeast and sea salt and vinegar in the Northeast. With AI, building creative for those regions is now quick.
• 𝗢𝗻𝗲 𝗰𝗲𝗻𝘁𝗿𝗮𝗹 𝗻𝗲𝗿𝘃𝗼𝘂𝘀 𝘀𝘆𝘀𝘁𝗲𝗺: Instead of building agents on top of separate data silos, Wilde built a data lake. Target store data, Roundel media, Shipt, Instacart, and DoorDash data all sit in one place, so a sales lead can ask a small language model what to do next.
• 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗼-𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁: When a retailer’s promotion drops a price on Instacart, one of Justin’s agents spots it through the API and flags it. He then decides whether to push ads to new-to-brand shoppers or pause them for 30 days and let the promo do the work.
• 𝗨𝘀𝗲 𝘁𝗵𝗲𝗺 𝗮𝗹𝗹: Justin builds mostly in Claude but stays LLM-agnostic, trying Grok, Muse, Kimi, and NVIDIA’s open models. “Don’t let your frame just get put into one.”
• 𝗗𝗼𝘇𝗲𝗻𝘀 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀: “I have dozens of agents that I’m working with. That is the new norm.”

I learned more about how CPG actually works in 30 minutes with Justin than I have in years of nodding along when Jason explains it. Grab a bag of something crunchy and enjoy the conversation.

Timestamps:

01:47 Disclosure: Justin is on the ReFiBuy advisory board
02:00 Every CPG brand Justin has worked at, and the spouse test
03:00 How challenger brands grow: from D2C to Walmart, Target, and Costco
03:55 Acquired or spun off: why CPG portfolios are reshuffling
04:10 Turns and the zero-sum shelf
05:10 PopCorners and the protein pivot
06:10 Social commerce is here: TikTok Shop and Whatnot live selling
06:50 Snack reveal: "I was wrong" about TikTok Shop
08:40 Plot twist: chips made of chicken breast (plus the crunch)
10:00 Making it in Costco: pallets, thresholds, and regionality
11:10 The Justin playbook: it starts with Amazon data
12:55 DoorDash comes to Costco, and why Instacart vs. DoorDash isn't zero-sum
14:00 Jason's take: another nail in the coffin for Costco?
15:05 Ship-from-store, speed, and conversion
17:15 Who gives brands better data: Amazon, Walmart, or Target?
18:15 Every sale by ZIP code: flavor geo-targeting with AI
19:15 How AI changed everything: one data lake for Target, Roundel, Shipt, and more
21:35 Walmart's 4.7-petabyte data lake and Code Puppy
23:00 The AI cheerleader and climbing the agent ladder
24:05 Instacart media and the promo-watching agent
26:00 Claude, Grok, Muse, Kimi: use them all
27:45 What's next for CPG: dozens of agents
28:45 Wrap-up

👉 Connect with Justin: https://www.linkedin.com/in/justin-bomberowitz-%F0%9F%90%94-07030414/
👉 Learn more about Wilde: https://wildebrands.com/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

We pick up where we left off a year ago and get into what has changed since. Ekta walks us through how e.l.f. is structuring its AI work, what kind of person they’re hiring to do it, and how a public company makes the case for investing before the ROI is fully provable. The live audience jumps in with some sharp questions along the way.

We close by looking ahead to what Ekta thinks brands need to get ready for in the next 12 months. Hit play.

Highlights: 

• 𝗟𝗲𝘁 𝗶𝘁 𝗿𝗶𝗽 𝗼𝗿 𝗹𝗼𝗰𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: Ekta says companies tend to pick one of two AI philosophies, giving it to everyone or locking it down. e.l.f. has tried to do both so it doesn't kill innovation.
• 𝗧𝗵𝗿𝗲𝗲 𝗽𝗼𝗱𝘀: e.l.f. runs three AI pods covering agentic commerce/growth, intelligent operations, and the human-agent workforce. Each pod owns data, change management, integrations, and training. The agentic growth roles are now being hired as dedicated full-time positions across e.l.f.'s brands.
• 𝗡𝗼 𝗼𝗻𝗲 𝗵𝗮𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗳𝗶𝗴𝘂𝗿𝗲𝗱 𝗼𝘂𝘁: "Anybody that tells you they have figured it out haven't figured it out." Ekta measures readiness instead, and focuses on the products where e.l.f. has the right to win.
• 𝗥𝗲𝗮𝗹 𝗽𝗿𝗼𝗺𝗽𝘁𝘀, 𝗳𝗶𝗻𝗮𝗹𝗹𝘆: Jason points out that most visibility tools rely on synthetic prompts. Google's new Agentic Insights tab in Merchant Center is the first look at what real people actually type, at least on Google surfaces.
• 𝗕𝗮𝗱 𝗱𝗮𝘁𝗮 = 𝗯𝗮𝗱 𝗔𝗜: Ekta's case for fixing the foundation first. If you don't solve the plumbing, you can't scale.
• 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘃𝗼𝗶𝗰𝗲𝘀 𝗹𝗶𝗴𝗵𝘁 𝘁𝗵𝗲 𝗳𝗶𝗿𝗲: About 14 months ago e.l.f. brought in an outside search expert, and that put "fire in the belly." 
• 𝗦𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗶𝘀 𝘁𝗵𝗲 𝘀𝗹𝗲𝗲𝗽𝗲𝗿: e.l.f. has invested in Lyric for forecasting and demand planning. Its planners "are actually now engineers," and AP, AR, payroll and inventory teams are building their own AI workflows.
• 𝗔𝗴𝗲𝗻𝘁 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Ekta's prediction is that Meta wins the agent shopping race. She says brands need to put agent identity in place now, so they know which agent is arriving and which customer it represents. It's the next CDP/CRM-style plumbing project.

Huge thanks to Ekta for running over from the main stage, and to our live audience at RetailClub for the great questions. Enjoy the conversation.

Timestamps:
01:17 Live from Retail Club: meet Ekta Chopra & co-host Jason Goldberg
02:04 One-year reunion and a new title
02:38 e.l.f. Beauty: a house of brands, 30 straight growth quarters
03:19 Eyes, lips, face… and the Jeopardy! moment
03:43 Ekta's role as Chief Technology & AI Officer
04:02 From the main stage: let it rip or lock it down
05:04 e.l.f.'s three AI pods
06:14 Making agentic growth a full-time job
07:00 Who do you hire for agentic commerce?
08:33 Jason's "you've reached your quota" hiring hack
08:48 Selling AI investment to a public company board
10:06 Measuring readiness when no one has measurement figured out
11:31 "My board member couldn't find our product"
13:06 Synthetic prompts vs. real prompts: Google's Agentic Insights
14:00 Audience Q: build vs. buy
14:52 Where to start: external voices and fire in the belly
16:23 Foundation first: bad data equals bad AI
17:47 Back office, supply chain, and Lyric
19:52 Personal shopping assistants and agents
20:59 Serving a house of brands and working with retail partners
22:55 Audience Q: is governance keeping pace?
24:50 Next 12 months: Meta, agent identity & agent relationship management
26:20 Wrap-up 

👉 Connect with Ekta: https://www.linkedin.com/in/ektachopra/
👉 Learn more about e.l.f. Beauty: https://www.linkedin.com/company/e-l-f-beauty/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This week is a research deep dive. ReFiBuy commissioned a behavioral study of how real people shop inside ChatGPT and Google's AI Mode, and Scot sat down with the researcher who ran it to walk through what was found. The full study is published today This is the conversation behind the numbers.

Highlights: 

• 𝟴𝟰% 𝗼𝗳 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 𝘀𝘁𝗮𝗿𝘁 𝗼𝗻 𝗮 𝗰𝗮𝗿𝗱: About 84% of product choices in the study came from a product card. That's 83.7%, and Scot rounds it up "if you allow."
• 𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗲𝗲 𝗯𝗲𝗮𝘁𝘀 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝘁𝘄𝗼: The first card wins by a wide margin, with roughly 43% of picks. But card three outperformed card two in both ChatGPT and AI Mode. Eric's take is that "it's not really a ranking." Scot's guess is curiosity, because card three is only partly visible in the carousel.
• 𝗣𝗶𝗰𝘁𝘂𝗿𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗽𝗮𝗿𝗮𝗴𝗿𝗮𝗽𝗵𝘀: About 75% of shoppers skipped the AI's written answer and went straight to the product cards. They'd come back to the text later, but only as a second step.
• 𝟵𝟴% 𝗼𝗳 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝗲𝗱 𝗰𝗮𝗿𝗱𝘀: Purchase-intent prompts like "I want to buy X today" brought up product cards almost every time.
• 𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗼𝗳𝗳𝗲𝗿 𝘄𝗶𝗻𝘀: When shoppers had to click out to a retailer, they leaned even harder toward the first offer than toward the first product card. The likeliest path is first card, first offer.
• 𝗔𝗱𝘀 𝘁𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗶𝗸𝗲: About 6 in 10 free-plan users saw ads or sponsored content on a task. 71% rated them at least as useful as Google search ads, and about 35% said an ad influenced what they considered buying.
• 𝗧𝗵𝗲 𝗿𝗲𝘃𝗶𝗲𝘄𝘀 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: Reviews were the main reason for a pick only 2% of the time. Afterward, about 75% said reviews mattered. Scot's explanation is that nobody reads them, but everyone takes the 4.8 over the 3.2.
• 𝗧𝗿𝘂𝘀𝘁, 𝘂𝗻𝘁𝗶𝗹 𝗶𝘁 𝗯𝗿𝗲𝗮𝗸𝘀: Eric says the biggest surprise after two years of these studies is "the extent to which people trust, even when they say they don't." A mismatched price or a wrong spec breaks that trust quickly.
• 𝗧𝗵𝗲 𝗸𝗲𝘆𝘄𝗼𝗿𝗱 𝗷𝗮𝗶𝗹 𝗲𝗿𝗮: Scot compares 20 years of chasing "men's running shoe" on Google with a world where shoppers let the AI choose for them.
• 𝟰𝟬 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗺𝗼𝗻𝘁𝗵𝘀 𝗼𝗳 𝘄𝗼𝗿𝗸: The study had 40 remote participants doing 30-minute think-aloud sessions on screen recordings. The team logged small behaviors down to seconds spent and scroll percentages.

Scot came out of the Amazon era believing the buy box was everything, and this study tells him ChatGPT has one too. Enjoy the conversation.

Timestamps:
01:57 Meet Eric Van Buskirk, Clickstream Solutions
02:15 Why ReFiBuy commissioned this study
02:48 Eric's background: SEMrush, Backlinko, Kevin Indig
04:05 Stated preference vs. observed behavior
06:05 How a behavioral study works
08:30 Why 40 people is enough
10:20 Who took part, and who got cut
11:55 The Amazon buy box and agentic commerce optimization
14:25 Finding #1: Pictures beat paragraphs
16:40 Card position: #1 wins, #3 beats #2
18:55 84% of choices come from a product card
20:35 Google SEO vs. AI: the "keyword jail" era
23:40 When do shoppers click out?
25:30 75% skip the text and go straight to the cards
27:25 Offer cards: the first offer dominates
30:20 Ads in ChatGPT: what shoppers really think
35:50 Trust and the reviews paradox
40:25 When bad data breaks trust
41:35 Where to get the study and work with Eric
44:55 Wrap-up

👉 Connect with Eric: https://www.linkedin.com/in/vanbuskirk/
👉 Learn more about Clickstream Solutions: https://clickstream.cc/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

Danny Smith is the Global Agentic Commerce Solutions Lead at Stripe, where he runs technical go-to-market (think pre-sales architect, not engineering) for Stripe’s agentic commerce products. One of his first projects was adding the payments layer to Amazon’s Rufus shopping assistant, and three years ago he got on stage at Stripe Sessions to talk about agentic commerce. 

We recorded this one live from day two of RetailClub in Huntington Beach. It was a little overcast, which after a day of baking in the sun, we didn’t mind at all. Jason Goldberg joined me as co-host, and we grabbed Danny between meetings. It was his first time at the show.

Danny’s a first-timer, both on the pod and at RetailClub. This week’s episode is a product deep dive: Stripe has been shipping agentic commerce products at a pace that makes three months ago feel like ancient history. Danny walks us through what’s live, what launched last Monday, and what’s coming soon, including how the checkout Scot kept seeing inside Meta’s new Muse app actually works.

Jason brings the payments-industry history. It goes back to when card-not-present transactions were illegal and runs up to Stripe’s jump from developer darling to incumbent. We also get into the thing that’s still breaking agentic commerce today, and it isn’t the agents.

Along the way there’s a hallucinating agent, a leather jacket, and a Cheeky Pint swag request. Hit play.

Highlights 

• 𝗟𝗶𝗻𝗸 𝗪𝗮𝗹𝗹𝗲𝘁 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝘀: A skill you teach any agent (Claude, GrokBot, your homebrew Hermes agent) so it can pay “in a guarded and secure manner.” Details at link.com/agents.
• “𝗬𝗼𝘂𝗿 𝗔𝗣𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗺𝘆 𝗹𝗶𝗳𝗲”: Developers really do stop Danny at conferences to ask for a photo. Stripe’s founders’ mantra: “Let’s win all startups, and then let’s go win them again.”
• 𝗢𝗽𝗲𝗻 𝗵𝗲𝗮𝗿𝘁 𝘀𝘂𝗿𝗴𝗲𝗿𝘆: Changing payments at an enterprise is “like open heart surgery.” Jason adds that it’s open heart surgery while you’re running a marathon.
• 𝗪𝗼𝗿𝗹𝗱’𝘀 𝗳𝗶𝗿𝘀𝘁 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘁𝗼𝗸𝗲𝗻: Stripe’s shared payment tokens launched with OpenAI, and Etsy was the first merchant. Link wraps them as a single-use virtual card capped at the amount you budgeted, so the merchant doesn’t have to be on Stripe.
• 𝗦𝘁𝗿𝗶𝗽𝗲 𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝘀: Launched last Monday and built on WebMCP, it makes human checkouts machine-readable at the same time. Stripe merchants on hosted checkout or invoices get it with no extra work.
• 𝗧𝗵𝗲 𝗪𝗲𝗯𝗠𝗖𝗣 𝗻𝘂𝗺𝗯𝗲𝗿𝘀: 42% fewer tokens burned, 38% fewer tool calls, and 39% faster checkout. “Three seconds is annoying to a human, 800 milliseconds is beyond annoying to an agent.”
• 𝗗𝗼𝘂𝗯𝗹𝗲𝗱 𝘄𝗲𝗯 𝘁𝗿𝗮𝗳𝗳𝗶𝗰: Through the Agentic Commerce Suite (ACS), some merchants have doubled their web traffic in three or four months just by getting their catalogs in front of agents. Instant checkout on Gemini is expected around late October.
• 𝗗𝗼𝗼𝗿𝗺𝗮𝗻 𝘃𝘀. 𝗯𝗼𝘂𝗻𝗰𝗲𝗿: Most merchants are bouncers that block all bot traffic, and bots were already 53% of internet traffic in 2024. Stripe is working on protocols to tell good bots from bad ones.

Stripe is building both sides of the agentic transaction, and Danny is the one out there getting retailers across the line. Enjoy the conversation.

Timeline

00:27 Welcome to Retailgentic
01:44 Live from Retail Club, day two
02:05 Meet Danny Smith, Stripe
02:38 From cloud architect to Stripe
03:48 Adding payments to Amazon's Rufus
04:55 Stripe launches agentic commerce
06:08 Jason on payments history & developer-first Stripe
07:40 "Your API changed my life"
08:40 Enterprise payments = open heart surgery
09:05 Meta Muse and the Link checkout
10:25 What is Link Wallet for Agents?
11:00 The weekend Meta scramble
12:20 GrokBot, Instinct & the pipeline
13:55 How Link works at non-Stripe merchants
14:35 The model airplane story
15:40 The world's first agentic token
16:05 Stripe Checkout for Agents & WebMCP
17:10 Two sides of the same coin
17:55 Agentic Commerce Suite & doubled traffic
20:55 WebMCP numbers: 42% fewer tokens
22:10 The leather jacket that got blocked
23:50 Doorman vs. bouncer
25:10 Danny's first Retail Club
26:30 Wrap

👉 Connect with Danny: https://www.linkedin.com/in/danny-r-smith/
👉 Learn more about Stripe: https://stripe.com

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This week’s episode is a platform deep dive with one of the longest-tenured companies in e-commerce. Elastic Path has been building commerce software for 26 years and Bryan is now steering the company straight into the agentic era. 

It’s part founder-journey (craft brewing to CEO is not a typical path), part product deep dive, and part industry-shift conversation about what AI does to the economics of running a commerce platform.

Highlights: 

• 𝗙𝗿𝗼𝗺 𝗯𝗿𝗲𝘄𝗲𝗿 𝘁𝗼 𝗖𝗘𝗢: Bryan spent eight years as a professional brewer, with an actual brewing degree, at a brewpub across from Fenway Park. "Everything I learned in my career that really moved the needle for me, I learned as a brewer."
• 𝟮𝟲 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲: Elastic Path's claim to fame was building the store for the Vancouver 2010 Winter Olympics, the birth of its self-managed commerce product.
• 𝗧𝗵𝗲 $𝟱𝟬𝟬𝗞 𝘂𝗽𝗴𝗿𝗮𝗱𝗲, 𝗸𝗶𝗹𝗹𝗲𝗱: Elastic Path built an AI updater so one developer can upgrade a self-managed platform in about two weeks, replacing what used to be a half-million-dollar annual project.
• 𝟭𝟬,𝟬𝟬𝟬 𝘃𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝘀: where typical platforms cap out at 250 variations per item, Elastic Path supports 10,000, plus bundles of up to 1,500 items.
• 𝗦𝗶𝘅 𝘄𝗲𝗲𝗸𝘀 𝗮𝗻𝗱 𝟭𝟬𝟬 𝗴𝗿𝗮𝗻𝗱: Bryan's prediction… AI takes re-platforming from a 6-month-to-2-year, $500K–$3M digital transformation down to a setup fee. "That's the place we're gonna see the biggest impact of AI in commerce."
• 𝗧𝗵𝗲 𝟴𝟬-𝗽𝗮𝗴𝗲 𝗣𝗗𝗙: in a world of AI discovery, every product needs the equivalent of an 80-page PDF of data to train the LLM on when to recommend it. (Scot's line, which Bryan says he "steals with credit" as often as he can.)
• 𝗣𝗿𝗼𝗼𝗳 𝗶𝗻 𝗻𝘂𝗺𝗯𝗲𝗿𝘀: a UK custom greeting-card seller saw conversion jump 40% almost immediately after adopting Elastic Path's checkout
• 𝗡𝗼𝗯𝗼𝗱𝘆 𝘄𝗮𝗻𝘁𝘀 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀: customers are asking about UCP and ACP, discovery, and the long tail, not chatbots. 
• 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗿𝗼𝗮𝗱𝘀 𝗮𝗿𝗲 𝘄𝗲𝗹𝗹-𝗽𝗮𝘃𝗲𝗱: Bryan's framework for where AI lands first… well-understood patterns like checkout, front ends, and reorders, not judgment-heavy edge cases.

Bryan is a product guy after our own heart, and this one gets satisfyingly deep, from CPQ to protocols to the future of re-platforming. Enjoy the conversation.

Timestamps:
03:20 Welcome Bryan + podcasting since 2009
04:05 Internet Retailer booth memories
04:55 From craft brewing to software
07:35 The Acquia and Drupal years
08:20 Moltin, Neural Magic, joining Elastic Path
11:05 Elastic Path history: the Olympics store
13:20 Two products: SaaS + self-managed
13:45 The AI updater: killing the $500K upgrade
14:50 80 people, profitable, constraints breed innovation
17:00 Customer base and the B2B focus
19:15 CPQ, PXM, and 10,000 variations
22:45 Serena & Lily case study
24:10 The MACH Alliance backstory (spicy)
29:55 Vibe coding the front end + Shopify as fintech
33:20 AI re-platforming: six weeks and $100K
39:00 The ReFiBuy partnership + the 80-page PDF
40:55 AI roadmap: workflows, promotions, analytics
43:20 B2B agentic commerce and EDI
47:10 UCP, ACP, and the long tail
49:05 Predictions: where the roads are well-paved
51:20 What's next for Elastic Path

👉 Connect with Bryan: https://www.linkedin.com/in/bhouse/
👉 Learn more about Elastic Path: https://www.elasticpath.com/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This week’s episode is a little different. We’ve has been noticing vibe-code platforms quietly showing up all over digital retail… at trade shows, in conversations with retailers and brands, and on the platforms’ own commerce pages. So we went to the source. 

This is a platform deep dive with Vercel, the enterprise company behind v0 and Next.js, on what a “vibe coding” company is actually doing in our world of commerce, and it turns out the answer is: a lot more than you’d think.

Highlights 

• 𝗧𝗵𝗲 𝟭𝟬𝟬-𝗺𝗶𝗹𝗹𝗶𝘀𝗲𝗰𝗼𝗻𝗱 𝗺𝗮𝘁𝗵: A Deloitte Digital + Google study cites clients found 100ms of load-time reduction drives a 7–8% lift in e-commerce conversion. “If the thing is faster, you’re gonna convert more. It’s just math.”
• 𝗛𝗲𝗹𝗹𝘆 𝗛𝗮𝗻𝘀𝗲𝗻’𝘀 𝗻𝘂𝗺𝗯𝗲𝗿𝘀: The Kontoor Brands-owned retailer cut load times from 2.5 to 1.5 seconds on Vercel, grew conversion 20% year over year, doubled its A/B test velocity, and blew away its Black Friday/Cyber Monday plan.
• 𝗧𝗵𝗲 𝗰𝗼𝗰𝗸𝘁𝗮𝗶𝗹-𝗽𝗮𝗿𝘁𝘆 𝗽𝗶𝘁𝗰𝗵: Vercel in three phrases… a developer tools company, an infrastructure provider, and an open-source software factory.
• 𝗬𝗼𝘂’𝘃𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲𝗱 𝗩𝗲𝗿𝗰𝗲𝗹: walmart.com and target.com run on Next.js, and Under Armour, Crocs, Buck Mason, PetSmart, McAlister’s, DeWalt, Clorox, and even Meta.ai are Vercel-delivered applications.
• 𝗡𝗼𝘁 𝗮 𝗦𝗵𝗼𝗽𝗶𝗳𝘆 𝗸𝗶𝗹𝗹𝗲𝗿: Vercel doesn’t do product catalogs, inventory, or order management. It’s the storefront layer on top of your commerce platform. “We help you own your storefront and own your destiny.”
• 𝘃𝟬 𝗺𝗲𝗲𝘁𝘀 𝗦𝗵𝗼𝗽𝗶𝗳𝘆: v0 is now deeply integrated with Shopify, so you can customize your storefront in plain English 
• 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀 𝗵𝗮𝗹𝘃𝗲𝗱: Custom storefront projects that were planned for a year are landing in six months, six-month plans in three.
• 𝗧𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿: Next.js 16 was built to be readable and manipulated by Cursor, Claude, and Codex, “now we have this new customer, this agent customer, who wants to interact with your code base.”
• 𝗧𝗵𝗲 𝗹𝗶𝗴𝗵𝘁-𝗯𝘂𝗹𝗯 𝗺𝗼𝗺𝗲𝗻𝘁: Jaime’s conversion story: feeding 50-page client 10-Ks into ChatGPT in early 2023 and realizing everything about go-to-market, and the factories that make software, was about to change.

This one really lifted the veil on what the vibe-code platforms are actually doing in retail, and I think we’re just seeing the tip of the iceberg. Enjoy the conversation.

Timestamps:
05:29 Welcome Jaime Sorgente
06:08 Jaime's path to Vercel: ChatGPT and 10-Ks
12:29 Utilities vs. builders
15:00 What is Vercel? The cocktail-party pitch
17:10 Why retail as a vertical
21:20 The Helly Hansen story
24:05 Vercel's other verticals: digital natives and marketing
26:45 Enabler, not competitor: where Vercel fits vs. Shopify & Salesforce
29:35 Beyond the storefront: clienteling and staff apps
30:45 Agents everywhere: Crocs, Amazon image annotation
36:05 v0 + Shopify: customize your storefront in English
37:45 Next.js 16: built for the agent customer
39:20 Custom storefront timelines, halved
40:35 Does owning your storefront de-risk replatforming?
42:20 The 100-millisecond study
44:30 The compounding effect: 4X feature velocity
46:45 Next.js explained: the stage manager analogy
52:20 Framework-defined infrastructure
54:30 Vercel vs. Lovable, Replit, and Base44
58:15 Where to learn more + wrap-up

👉 Connect with Jaime: https://www.linkedin.com/in/jsorgente/
👉 Learn more about Vercel: https://vercel.com/for/retail

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This week’s episode is a deep dive into personalization, a huge category in e-commerce that’s getting rebuilt from the ground up for the AI era. 

FERMÀT is an AI-native personalization platform that sits on top of your e-commerce platform and customizes the site experience based on a visitor’s behavior, not their identity. That distinction sounds subtle, but it turns out to be the whole ballgame and it connects directly to agentic commerce because agents show up with intent, not an identity. We also pick up Rishabh’s founder journey and some very candid company-building advice along the way.

Highlights 

• 𝗧𝗵𝗲 𝟮𝟬% 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗹𝗶𝗳𝘁: FERMÀT's data shows personalizing on behavior alone, even for fully incognito visitors, converts roughly 20% better than personalizing on identity and past purchases.
• 𝗠𝗮𝗻𝘆 𝘁𝗼 𝗼𝗻𝗲, 𝗻𝗼𝘁 𝗼𝗻𝗲 𝘁𝗼 𝗼𝗻𝗲: the marketer's dream of one-to-one was "a lack of imagination", the same person is a different shopper on Instagram, Google, and ChatGPT. 
• 𝗗𝗲𝘁𝗲𝗰𝘁, 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱, 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲: instead of humans watching a sample of session recordings and shipping site changes months later, agents trained on commerce watch every session, recommend changes, and generate the new experience.
• 𝗧𝗵𝗲 𝗕𝗹𝗼𝗼𝗺𝗿𝗲𝗮𝗰𝗵 𝗽𝗿𝗼𝘃𝗼𝗰𝗮𝘁𝗶𝗼𝗻: identity-based personalization was state-of-the-art 10 years ago. Rishabh's take on the incumbents: if nothing changes, "their 3 to 400 million of revenue will become 2 to 300."
• 𝗠𝗶𝗻𝗲 𝘁𝗵𝗲 𝗮𝗱, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗜𝗗: even when an Instagram visitor is anonymous, the ad they clicked carries demographics, performance history, and frequency data… context most brands never use.
• 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗲𝘀𝘁𝗮𝘁𝗲 𝗮𝗴𝗲𝗻𝘁 𝘁𝗲𝘀𝘁: an agent is only as good as the feedback, context, and iteration speed you give it and every "actually, I also care about the collar" update is behavior data.
• 𝗦𝗮𝘆 𝘄𝗵𝗮𝘁'𝘀 𝘁𝗿𝘂𝗲: Platforms are using AI to verify your pages and truth gets rewarded, not dinged.
• 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗮𝗴𝗲𝗻𝘁 ≠ 𝘀𝗵𝗼𝗽𝗽𝗲𝗿 𝗮𝗴𝗲𝗻𝘁: support agents should stick to facts and policies; shopper agents should infer.

Rishabh is one of the clearest thinkers I’ve talked to on where personalization goes in an agent-first world, and his founder advice at the end is worth the listen on its own. Enjoy the conversation.

Timestamps: 

02:44 Welcome Rishabh 
03:37 Rishabh's backstory: physics, Wall Street, PhD
06:33 Building three businesses at LiveRamp
07:56 Founding story: Apple ATT and iOS 14.5
10:33 Detect, recommend, generate
12:27 Where FERMÀT sits in the stack
13:57 Many-to-one personalization
15:22 The Bloomreach question
19:00 Does current behavior beat past behavior?
21:18 The data: a 20% conversion lift
22:34 The agentic commerce connection
23:44 The real estate agent analogy
25:41 Reading intent from incognito traffic
26:28 Into the weeds: mining a Meta ad for context
30:37 Side quest: the SEO cloaking myth
33:47 Writing for agents, bots, and humans
34:49 On-site shopper agents
39:40 Company building: know your why
42:38 Where to find Rishabh + the 1 to 100 podcast

👉 Connect with Rishabh: https://www.linkedin.com/in/rishabhmjain/
👉 Learn more about FERMÀT: https://www.fermatcommerce.com/
👉 1 to 100 podcast: https://open.spotify.com/show/6WAB5oOzco9oWrQmYmuFZ6

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This week’s episode is a data deep dive… two of them, actually. Salesforce just dropped a back-to-back data release: the latest edition of its State of Commerce report and its Holiday 2026 predictions. Caila Schwartz, Salesforce’s newly minted Head of Agentic Commerce Shopper Insights, walks us through both. Think of it as a rearview mirror on the first half of 2026 and headlights into the holiday season, with agentic commerce showing up in nearly every number.

Highlights:

• 𝗧𝗵𝗲 𝟮𝟬𝟬% 𝗷𝘂𝗺𝗽: 15% of shoppers now start their buying journey with an LLM or agent, up from 5% last spring.
• 𝗛𝗮𝗹𝗳 𝗼𝗳 𝘀𝗵𝗼𝗽𝗽𝗲𝗿𝘀: About 50% have used a general-purpose LLM, social media agent, or branded agent somewhere in their buying journey in the past 12 months, a 67% increase over last year.
• 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗸𝗶𝗻𝗴: Agentic search traffic converts at roughly 2.5–3.5%, about 2–3x overall conversion and far above social’s sub-1%.
• 𝗧𝗵𝗲 𝘁𝗿𝘂𝘀𝘁 𝗻𝘂𝗺𝗯𝗲𝗿𝘀: 74% of shoppers trust agent recommendations, and over 80% feel more confident in purchases made after interacting with a brand-owned agent like Rufus or Sparky.
• 𝗧𝗵𝗲 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝗯𝗹𝗶𝗻𝗱 𝘀𝗽𝗼𝘁: Discovery is happening inside conversations that never send a click back to the brand site.
• 𝗧𝗵𝗲 𝗖𝗮𝗶𝗹𝗮 𝗙𝗶𝘃𝗲: Salesforce’s Holiday 2026 predictions: non-human traffic, branded agents everywhere, social commerce acceleration, the store’s new hybrid role, and the free-shipping squeeze.
• 𝟮𝟬% 𝗻𝗼𝗻-𝗵𝘂𝗺𝗮𝗻 𝘁𝗿𝗮𝗳𝗳𝗶𝗰: This holiday, Salesforce predicts about one in five e-commerce visits will come from crawlers and agents, not people.
• 𝗕𝗿𝗮𝗻𝗱𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝗿𝗮𝗰𝗲: About a third of B2C orgs have agents deployed today, over half plan to deploy within six months, and by Cyber Week one in three e-commerce sites will have a branded agent on site.
• 𝗦𝗼𝗰𝗶𝗮𝗹 𝗴𝗿𝗼𝘄𝘀 𝟵𝗫: Social commerce is predicted to grow nine times faster than traditional e-commerce this year, and it’s high-income shoppers, even more than Gen Z, saying they’ll buy in-app this holiday.
• 𝗛𝘆𝗯𝗿𝗶𝗱 𝗵𝗼𝗹𝗶𝗱𝗮𝘆: 37% of holiday dollars will be “hybrid sales” that rely on both digital and physical to close the deal, 80% of shoppers use their phone while standing in your store.
• 𝗗𝗮𝘁𝗮 𝗶𝘀 𝘁𝗵𝗲 𝗯𝗲𝗱𝗿𝗼𝗰𝗸: Four in five commerce pros say their tech stack grew over the last two years, making data unification the #1 barrier to good agents.

Caila brings the receipts, real numbers on a shift most of us are still trying to eyeball. We’ll get her back in the fall when the actual holiday forecast drops. Enjoy the conversation.

Timestamps:
 
06:30 Interview begins, Caila's new title
07:20 Caila's role and 12 years at Salesforce
08:30 Two reports: State of Commerce + Holiday predictions
09:30 State of Commerce methodology
12:50 What's new in this edition
14:45 The agentic discovery shift: 50%, 200%, 15%
17:25 Who's losing share in discovery
19:00 Faster than mobile or social ever moved
22:20 The measurability problem
25:20 Conversion: 2–3x better than average
27:10 Why shoppers lean on agents, by income
29:30 Trust: 74% and the 80% confidence stat
32:00 The branded-agent race
34:30 The #1 barrier: data unification
40:20 Caila Five #1: 20% non-human traffic
42:30 #2: One in three sites get a branded agent
43:15 #3: Social commerce grows 9x
44:25 #4: The store's new role 37% hybrid sales
46:20 #5: Free shipping and the K-shaped economy
47:30 Decoupled transactions deep dive
53:10 Top recos: unify data, turn on ACP

👉 Connect with Caila: https://www.linkedin.com/in/caila-schwartz/
👉 Learn more about Salesforce: https://www.salesforce.com/
👉 Check out the State of Ecommerce: https://www.salesforce.com/commerce/resources/state-of-commerce/
👉 Look at the Holiday 2026 Predictions: https://www.salesforce.com/blog/holiday-retail-predictions-2026/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This one is a build deep dive. Daniel has spent eight years on the same problem, and the answer has changed shape three times along the way: a consumer app in 2018, a shoppable recipe platform in 2023, and now a conversational shopping assistant running inside grocers’ own sites. 

We go deep on the plumbing here: product catalogs, ontologies, eval systems, model selection, retail media, and what an agent does when a shopper types “taco night.” If you like knowing how these things actually work under the hood, this is your episode.

Never miss the latest Retailgentic news→

Highlights:

• 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀: Live in over 700 stores, reaching over 10 million shoppers, with the number of locations set to double before the end of the year. The consumer app has over a million users. 
• 𝟰𝟭 𝗱𝗮𝘆𝘀 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝘁𝗼 𝗹𝗶𝘃𝗲: A one-line JavaScript install that reuses the retailer’s existing catalog, add-to-cart, analytics, and retail media. National scale in six weeks.
• 𝗦𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝘀𝗲𝗹𝗲𝗰𝘁 → 𝗿𝗲𝘃𝗶𝗲𝘄 𝗮𝗻𝗱 𝗮𝗰𝗰𝗲𝗽𝘁: Daniel’s framing for the whole shift. The old era: you search one thing, you select it. The new one: the agent assembles a solution and you approve it. 
• 𝗧𝗵𝗲 𝗲𝘃𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺: Thousands of test scenarios with grading criteria aligned to each retailer’s merchandising principles, run tens or hundreds of times to get a distribution of responses. 
• 𝗧𝗵𝗲 𝗳𝗶𝘀𝗵 𝗿𝘂𝗹𝗲: Retailers write procedures the agent pulls when a scenario matches. One of Daniel’s favorites: if you’re cooking fish today or tomorrow, it recommends fresh. If it’s later in the week, frozen.
• 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗮 𝗻𝗲𝘄 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲: Onboarding is essentially an interview with the business. What are your values? How do you respond? What are your merchandising principles? That becomes the agent’s identity and personality. 
• 𝟭,𝟬𝟬𝟬 𝗙𝗼𝘂𝗿𝘁𝗵 𝗼𝗳 𝗝𝘂𝗹𝘆 𝗯𝘂𝗻𝗱𝗹𝗲𝘀: Instead of one holiday carousel every shopper sees, merchant teams define ~20 axes — price, dietary needs, brand preference — and an agentic process generates permutations personalized against transaction history.
• 𝗔𝗜 𝗖𝗵𝗲𝗳𝘀 𝗼𝗻 𝗧𝗶𝗸𝗧𝗼𝗸: Long before ChatGPT, Cooklist ran a TikTok series generating recipes with GPT-2 and early GPT-3. The models got good at basic recipes so fast that the fun moved to challenging them to be creative.

Daniel is one of those founders who has clearly built the thing himself, and it shows in the answers. We learned about a thousand things from this one. Enjoy the conversation.

Timestamps:
02:57 Welcome Daniel Vitiello, CEO & co-founder of Cooklist
03:10 Grocery, CPG, and retailer-hosted agents: the pod's favorite topics
04:46 The $1 check and the handwritten letter for loyalty data
05:46 Why 2017 was the moment
06:12 Inside the Cooklist app: home inventory and the AI chef
07:35 AI as the thread: Kurzweil, GPT-2, and the AI Chefs TikTok series
09:37 B2C to B2B, and the synergies of running both
12:05 The cat food problem
13:34 The core identity system: hummus, three ways
15:58 Search and select vs. review and accept
16:23 The spark for B2B: Target, Techstars, and saying no
18:45 Recipe commerce and meal-pairing merchandising
20:19 Funding: RevTech, Mercury Fund, and profitability
21:56 Freemium, pro, and advertising in the app
22:48 700 stores, 10 million shoppers
24:28 The flagship deployment: live in 41 days
25:56 Ontology, context engineering, and interviewing the retailer
27:03 Non-determinism and the eval system
29:06 Entry points: sparkles, suggested prompts, and PDP workflows
31:01 How the catalog and inventory get in
33:41 Procedures: the weekly flyer and the fish rule
35:55 Cost, speed, and the token-efficient stream
37:38 Model agnostic: scoring Opus against Sonnet
38:15 Instacart, Storefront Pro, and winning head-to-head
39:47 Retail media and Criteo
40:22 Agentic search and the "taco night" problem
42:02 Agentic merchandising: 1,000 Fourth of July bundles
42:55 Proactive carts, and the 50% who finish in the store
43:43 Smart fridges and forecasting demand
44:44 Where to find Daniel

👉 Connect with Daniel: https://www.linkedin.com/in/dkvitiello/
👉 Learn more about Cooklist: https://cooklist.com/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This week Retailgentic goes full Tron… in reverse. Instead of getting sucked into the digital world, Scot steps out of it and into into the analog one, bringing the AI angle with us. 

Ben Rodier is the first AI guest on the show who isn’t from e-commerce. He’s building physical AI for the sales floor: an AI sales coach for the people who sell to you face-to-face in furniture stores, apparel shops, bank branches, and beyond. It’s part founder-journey story (three companies, two exits, twenty years in retail) and part product deep dive into how you actually coach a human salesperson with AI without being creepy about it.

Highlights 

• 𝗧𝗵𝗲 𝗥𝘂𝗹𝗲 𝗼𝗳 𝟯𝟬: There’s an inflection point (around 30 AI coaching sessions) where practice turns measurable. Reps who cross it sell roughly $2,000 more per week, about $100,000 more per year, than those who don’t.
• 𝗧𝗵𝗲 𝗰𝗿𝗼𝗰𝗼𝗱𝗶𝗹𝗲 𝗰𝗵𝗮𝗿𝘁: Plot the reps who practiced 30+ times against those who practiced 10, and the two lines pull apart into a “crocodile-shaped separation”… the clearest picture of coaching ROI Ben’s ever had.
• 𝗧𝗿𝘂𝘀𝘁-𝗳𝗶𝗿𝘀𝘁 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲: Data (find the goal) → AI role play (practice it) → privacy-compliant listening (coach the real conversation). Each step earns the right to the next.
• 𝗥𝗼𝗹𝗲 𝗽𝗹𝗮𝘆 𝗶𝗻 𝘃𝗼𝗶𝗰𝗲 𝗺𝗼𝗱𝗲: The coach is a ChatGPT-style voice agent trained on your company’s own playbook. It throws curveballs, like someone walking into the room mid-pitch. “No one likes to do role play with their manager. It’s super awkward.”
• 𝗔 𝗽𝗿𝗼𝗳𝗶𝘁 𝗰𝗲𝗻𝘁𝗲𝗿, 𝗻𝗼𝘁 𝗮 𝗰𝗼𝘀𝘁 𝗰𝗲𝗻𝘁𝗲𝗿: Unlike most training software, if you practice you sell more so the tool pays for itself. 
• 𝗕𝗼𝗿𝗻 𝗼𝗻 𝘁𝗵𝗲 𝗳𝗹𝗼𝗼𝗿: Ben invented retail-media ads on Walmart’s website in 2009 before the term existed. Google execs opened a meeting with: “Who are you, and how did you figure out how to get these properties as advertising publishers?”
• 𝗧𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝘃𝗲𝗿𝗱𝗶𝗰𝘁: McDonald’s tried a humanless AI drive-through and it “failed miserably”. They pivoted right back to a human. Ben’s bet: five years out, most buying and selling is still face-to-face.

It’s not often we leave the digital surface behind on this show, but Ben’s take on physical AI, and what happens when the sales floor and the digital world finally connect, is one of the more grounded, human conversations we’ve had. Enjoy the conversation.

00:46 Welcome to Retailgentic
02:01 Scot's intro: a fun diversion into physical AI
03:47 Meet Ben Rodier, FrontlineIQ
04:33 From a TJ Maxx floor to third-time founder
06:07 Sales guy, not a technical founder
06:53 Corporate to startup: the TV advertising leap
07:37 Inventing retail media on Walmart's site in 2009
10:31 Salesfloor: the original idea
13:42 Bootstrapping, PE at the pandemic peak, the Tulip merger
17:19 The genesis of FrontlineIQ: Up Labs & Ashley
20:10 What "physical AI" actually means
21:24 Launching Jan 1, 2025 & hiring AI-native talent
24:41 30 customers in under 18 months
27:36 The oversubscribed seed round
28:46 How it works, part 1: data & trust
30:58 Part 2: AI role play in voice mode
32:43 Part 3: privacy-compliant listening
36:00 The Rule of 30 & the "up" system
37:33 Coach the coach, God view & the control center
39:48 ROI: the crocodile chart & turnover
45:14 The future: cameras, glasses & Plaud pins
49:23 Robots on the floor? The McDonald's test
51:44 Merchandising insight from what reps hear
52:36 Where to learn more & outro

👉 Connect with Ben: https://www.linkedin.com/in/benrodier/
👉 Learn more about FrontlineIQ: https://www.frontlineiq.ai/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic