In this episode of the Full Throttle Leaders Podcast, host Michael Falato interviews Jeff Frisch, co‑founder of Intello, an agentic AI platform built specifically for retail merchandising and planning. Frisch explains that Intello offers a suite of highly focused AI agents—currently 18, expanding to 30—that automate tasks such as assortment, allocation, replenishment, and rebalancing, giving merchandisers and planners "superpowers" to ensure the right products are in the right stores at the right time. Unlike generic AI tools, Intello’s agents are "a foot wide and a mile deep," concentrating solely on back‑of‑house merchandising decisions. The platform ingests historical sales and inventory data, and enriches its forecasts with external signals like weather, geopolitical events, and even consumer body‑size trends driven by health‑tech drugs. Pricing is positioned as a cost‑effective alternative to hiring human planners, with agents costing $30‑70 k per year. The primary market today is luxury and specialty retailers (e.g., Balenciaga, Dolce & Gabbana, Express, Psycho Bunny) with plans to expand into big‑box chains such as Walmart and Home Depot. Frisch outlines rapid growth: a 30‑person team that leverages AI‑enhanced development tools (e.g., Cloud Code, Notion, Vibe coding) to triple productivity, with headcount expected to double in two years. He also details the founding team—his own background in retail tech and Salesforce China, and co‑founder Rupesh’s experience leading retail at Sapient and product/engineering in Kochi, India. The conversation wraps with fundraising status, future roadmap, and contact information (jeff@intello.ai, LinkedIn, X) aimed at CIOs, CTOs, chief merchants, and merchandising leaders.
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Hello everybody, Michael Filotta here again from Full Throttle Leaders. This is brought to you by Full Throttle Filotta Leads, which is my company. And we have Jeffrey Frisch, who I used to work with at Symphony Talent back in the day, and now he started a new company. Jeffrey, why don't you tell us who you are, what you do, and why you do it?
Thanks, Michael. Uh, known Michael for, I don't know, 12 years, I think, maybe more. It's been a long time. Uh, we started Intello a year ago. Uh, we are an agentic layer specifically for merchandising and planning. We deliver collaborative intelligence for retail merchandisers, planners, and allocators to give them superpowers. And we have 18 agents available today. We're going up to 30. We work with luxury and specialty retail Everyone from Balenciaga and Dolce Gabbana to Express and At Home and Psycho Bunny.
And what does your, your Agentic AI do differently than some other company?
They're a foot wide and a mile deep, so they're very focused and only focused on merchandising and planning. So think of, you know, if you're a merchandiser and planner and you want to optimize your in-season management, so you want to do things like your assortment, your allocation, your replenishment, your rebalancing, all of those things can be done with our agents. And our agents work in the same way that people do. They think through problems, work through use cases, and have a set of workflows that are designed specifically around that set of problems. So they're not agents that do customer service, they don't do sales, they're really very focused on back of the house merchandising and planning, making sure that the retailers have the right products in the right stores at the right time.
Is it— I mean, this, this is ignorant, but is it like a BPO/RPO internal resource solution, or am I kind of mixing my terms up?
No, yeah, it's a little bit different. It's, you know, imagine if you're a retailer, you know, you could use a big retailer like The Gap. They're not a customer of ours, but, you know, let's use them as an example because everybody knows them. And, you know, they've got 50 to 100 people that are responsible for making sure that they've got the right products for next season. So, you know, doing, building out their assortments and building out their allocations and line planning. Then they've got another group of people that are responsible for making sure that inventory's in the right stores. So they do the right optimization around their allocations, and then they've gotta do pricing management and they have to do vendor management, those types of things. There are systems that have been around for 20, 30 years, like Blue Yonder and Anaplan and Board that are kind of legacy systems. There's some more modern systems like Impact Analytics or Tulio. And then there's systems like ours, which are pure agents. And our agents will really sit on top of the dataset and automate and autonomously deliver a lot of the decisions that are made around what it would be like to get the right blue shirt into the right store. And what's the right amount of pants that need to be at a certain seat, a certain season.
So instead of ordering the wrong products at the wrong time for the wrong season for the wrong location, you can have it look at, uh, data from over the years and say, what should I be doing for Q2 in the western states? Is that what you would ask it?
That and then some. Yeah, so it's not only looking at your historical sales data and your historical inventory movement, but it's also looking at weather patterns. It's looking at geopolitical challenges. You know what's going on right now in Iran can affect, you know, shipping lanes, can affect areas around, you know, geopolitical unrest. It's looking at patterns that are happening around the world. So for example, we work with a lot of luxury brands. So lots of people are on Ozempic and GLP-1s. So bodies are getting smaller. So therefore your season for next year, probably you need to buy less large and extra large and you have to design for, you know, extra small and small, our, um, our data sets will factor those types of elements into, into the planning strategies.
That's awesome. What's, what's one or two things you've learned in AI or tactics, strategies that was really incredible for the last 12 months, or really missed the mark and you're like, I lost, I learned, but I learned fast from losing.
Yeah, I think the number one thing around being, being really smart around what you do with AI is don't try to be everything to everyone. I think there, you know, there is, you know, ChatGPT and Gemini and Claude to try to be everything to everyone. And then there's a lot of other places to be. And if you're trying to solve too many problems with AI, you won't solve any of them. So bite off those challenges in in, in small bites and look at a specific problem and try to solve that with AI. And if you do that properly, then you can build a really great product and you can have a really great, um, use case and, and product-market fit. But if you're trying to do too much, generally you're going to do nothing well.
So you mentioned that you're in the middle of a raise. Tell me a little bit about your, your next 5 years to 10 years. What's, what's your plan for the company?
Yeah, well, you know, we're rapidly growing. We're 30 employees now. Our expectation is that within 2 years, we'll double that in size in terms of headcount. We'll never be a huge company, and that's because of AI. You know, our engineers are working 3x the capabilities that an engineer could work 2 years ago, thanks to tools like Cloud Code and Notion. So I don't think—
like vibe coding?
Vibe coding, yeah.
Yeah.
So, and we, we live and breathe what we say. So across the 30 people, everybody's using AI tools all day long. You know, I probably access a different tool 6 times an hour. So that's just the way, the way the team is built. But to your specific question, we think we'll double in size this year, we'll triple in size next year, we'll triple again, and then we'll see where we are 3 or 4 years from now.
And who's your, who's your typical client, like a Gap or Walmart or—
Yeah, our focus now and next year is definitely specialty retail like a Gap, um, and then luxury retail. So we have a very strong, um, foothold in, in Paris and in Milan, uh, and we also have a fairly strong foothold in North America. That— those two areas are the areas that we're focused on now. We will go big box, so we will look at brands like Walmart and Home Depot and Best Buy and those, those players that will probably start looking at that next year. And we'll build out agents specifically for that type of space.
And how do you normally price this out? What's the average revenue per client per year or per month?
Yeah, so think of it like you're hiring people, right? Our agents run anywhere from $30,000 to $70,000 per agent. And, you know, that's obviously a lot less expensive than hiring an allocator or a planner or a merchant.
Per year, per year, right?
Per year. Yeah, per year. And those agents are based on datasets and we have 18 agents available today and we're going up to 30 agents by the end of next year.
So if you would ask for a certain role agent to fit into your company, to that company, and then you would upload all the learnings and documents so it could learn from it. Is that how it really—
For us, it's really around the data. Yeah, yeah. So it's not really, we don't need a RAG that has all types of information about a company. What we need is historical sales data and inventory data. As long as we have that, that's our, that's our starting point for our model. And then as we start to think about, you know, doing forecasts for years, following year line planning, assortment planning, store clustering, we'll want to integrate with things like product lifecycle management systems, so PLMs, so that we have all the information about the products themselves. But our starting point is always sales data and inventory data.
You mentioned some background and a friend of ours, Rupesh. Tell me what he's doing with you and why he decided to go in with you on this.
Yeah, so before we knew him at Symphony Talent, he actually led retail for Sapient. So he has a long history in the retail tech space. And after leaving Symphony, he wanted to get back into retail tech. I started out in retail tech and e-commerce. He started building this product in stealth mode. Convinced me to come over from Salesforce where I was leading Salesforce China for about 5 years. We worked great together in the past and we said, let's do it again. He leads product and engineering. He's right now in India with the product and engineering team, which are in Kochi, and that team's expanding. And I lead, you know, go-to-market, customer success and kind of everything else.
Did you live in China?
I used to go back and forth pre-COVID literally every 2 months. I was there 6 times a year. And then post-COVID, I was there about 4 times a year. So less, less trips to China now. We're not focused on China at the moment. Not that that won't happen in the future.
Yeah. So tell us how to get a hold of you and who should get a hold of you.
Yeah. So jeff@Intello.ai. You can find me on LinkedIn. You can find me on, on X or anywhere. There's a lot of ways to reach me. The people that we're really talking to are CIOs, CTOs, chief merchants, and merchandising leaders of retailers. And really we're focused on specialty retailers and luxury retailers right now.
And it's I-N-T-E-L-O.ai?
Correct. Correct.
And just so everybody knows, I'm Michael Filato, I said at the beginning, and I can be reached at Full Throttle Filato Leads. And I do Legion. I help companies like— anybody wants it, it's B2B. So hopefully reach out to Jeff, reach out to Michael, join a roundtable that we'll, we'll be producing. We'll kind of highlight Jeff on there as well. And Jeff, thanks again for making time today.
