The Future Of Retail Robotics: How Simbe's Tally Is Transforming Store Operations | Spotlight Series

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I'm one of your co hosts for today's interview, Chris Walton.

And I'm Anne Mazinga.

And Anne, I've been, I've been spouting my mouth off for, I think it's been probably about the past six months now or so, maybe even longer, about why 2025 is going to be the year of the robot.

Yeah, I know, right?

I already, we already did our top, you know, our top retail headlines of the year.

I was already going 2024.

It was my retail, it was my store technology of the year.

So.

I know, and I, I took umbrage on that.

I feel like I suddenly got preempted.

But anyway, in, in true Omnitalk fashion, we are kicking off 2025 with the leading in store retail robotics provider.

So it is with great pleasure that we introduce today's guest, Brad Bolia, the co founder and CEO of simbi.

Brad, welcome to omnitalk.

It is wonderful to have you with.

Us today, Chris, and thank you both for having me.

Yeah, this is going to be such a good conversation, Brad, like Chris said, we've been so excited to do this.

Simbi has, excuse me, had a huge year in 2024 and I can't wait to see what happens in 2025.

But for those who might be new to OmniTalk or they haven't followed our coverage of robotics, which is seemingly impossible, give us a little background on Simbi and specifically how it came to be and what it does, if you don't mind.

Yes, absolutely.

SIBI was founded with this mission of really digitizing physical stores through automation and computer vision.

In fact, our technical team came out of a world renowned robotics and computer vision think tank that was in Silicon Valley.

And I had spent the years prior building several companies in the energy and utility space, solving a very similar kind of instrumentation challenge where utilities wanted to know what was going on across the grid.

And so we came together with this shared belief that there was a way to create a better retail experience for all by actually knowing what was happening on store shelves.

And in 2019, or I'm sorry, in 2015, we announced the concept of tally, the world's first shelf auditing and analytics robot.

And if we fast forward to date, we've had the unique opportunity to work with nearly 40 different retail banners and deploying Tally in about five different countries and have raised more than 100 million in venture capital specifically focused on this problem.

Well, Brett, okay, I gotta ask you, okay, so, but how did you pinpoint retail?

Like, we go back to me, like, were you guys all sitting around, like, were you sitting around a bar and you're like, hey, we've been doing this thing, let's go into retail.

Like, how'd that come about?

Yes, you know, it really came about because we were interested in leveraging robots that could do automated data capture.

In the early days, we looked at a number of markets, ret building and construction, sort of health care.

But the retail sort of pain point really stuck out to us just personally, you know, our own personal experiences of going to stores and, you know, a couple items on your list aren't on store shelf or you walk into a store and you're like, which of these 20 aisles is this product actually sort of located?

And it became clear to us that, you know, these stores were such deep fabric to our lives that this was the place to apply our energy.

That makes sense.

I mean, Ann and I, the reason I asked you that question is because, you know, and I headed up Target Store the future project for a number of years.

And, and, and we agree because we realized doing that work, like how little data actually exists in understanding the physical store experience, especially in comparison to E commerce, you know, I would call out too, and so, you know, but trying to design a system of interconnected technologies to change that is, Is really complicated.

So, so you've talked about it a little bit, but so how do you view that issue?

Like, how do you solve that problem?

Absolutely.

I mean, we see the shelf and again, the shelf is where the vast majority of consumers are making their purchasing decisions.

But we see this as kind of like the last major data desert in the world of physical retail.

Right.

And this desert is not just affecting retailers themselves, but store teams, brands, online delivery players.

And as a business, we view the shelf as like the linchpin to the whole supply chain.

So with the advent of technology like autonomous mobile robots and you know, full stack sort of computer vision, we have this world where we can actually provide true visibility and know, you know, precisely the XYZ coordinates of every product in a store.

Are products, you know, properly stocked?

Are they in the right place?

Are prices in promos sort of accurate?

And Using this newly derived data set fused with any retailer back office data, right?

Their inventory data, their price system data, their point of sale data.

This combined becomes an incredibly powerful lens through which to look at their business.

But Brett, so the next obvious question though is like, how do you expect retailers to actually use that data?

So you're collecting all this data from the data desert that previously was the shelf, but you have tally going on in the store, running the store, seeing what's on shelf.

How do you expect the retailers to actually use the data?

Because that's a different unlock that has to be accomplished, I would imagine.

Yeah, absolutely.

And the data that we collect, I mean, there's certainly a data tsunami here, but our job is to really help derive intelligence and sort of action out of it.

What we focus on first is really empowering the front line of workers, you know, store teams that are actually on the ground.

So instead of them looking for problems, right, performing shelf audits, understanding which products they may have backstock for on a secondary display, etc, these are all things we automate for them.

So which products should they put on a pool list?

Which price tag should they go change?

You know, where are there sort of planogram discrepancies in the store with sort of changes?

So we start with the store teams first, but the layers continue to bubble up.

So we give store leadership teams sort of a dashboard where really for the first time in their history, they actually know what their true on shelf availability rates are and how their stores actually stack up to other stores throughout the chain.

And then it continues to go on from there.

I mean, certainly this data is bubbled up at a corporate level so they can see how stores across the chain are performing and leveraging this as a new performance sort of benchmark sort of tool.

And it goes beyond that, right.

As retailers have begun to sort of scale this technology across their footprint, we see them making this data available to their merchant partners and others within their ecosystem as well.

Well, Brad, I have to ask you, and I know you might be a little biased because you started and you're running a robotics company right now, but there are, there are a lot of other technologies out there, a lot of companies who agree with this idea that you have, you know, like that the, the shelves are really the desert.

So why robotics?

Like, can you dive a little bit more specifically into why you think that that's the best answer for really improving the data quality that retailers can get out of their stores?

And then even more examples, I guess, because I think that's always the important part to hear is like, what is this actually changing for the stores, for the store associates too?

Sure, absolutely.

You know, we'd say from our experience, you know, what we've been able to prove to the market is, you know, the largest number of chain wide adoptions of this type of technology and sort of a proven ROI model that sort of comes with it.

We do believe strongly that robotics are sort of the most accurate, comprehensive and cost effective way to drive this level of intelligence.

So if we get into the specifics, we've kind of compared this technology head to head to back office AI that looks at, you know, point of sale data signals.

We've compared this to E commerce data and sort of mispicks.

We've compared this to employee driven whole shoots, employee driven photo capture and sort of standalone sort of fixed cameras.

And what we found is sort of the accuracy, the frequency and the fidelity.

The other solutions aren't really sort of comparable because we're actually seeing, sensing and decoding what's actually happening on you know, these physical store shelves at a very sort of frequent basis.

It's not depending on labor, it's not depending on sort of a planogram to sort of perform analysis.

We're decoding every barcode, every piece of optical data that's on a shelf tag, identifying every product, every hole that's sort of in, in the space.

So you know, we've really had the chance to kind of put this head to head against everything in, in sort of industry sort of out there.

And I think what further emphasizes this point is, you know, we've, we've stepped into stores that have been around for decades, right?

And by stepping into these stores and reducing out of stocks by like 60%, reducing price errors by 90%, you know, having this level of impact for stores that have tried, you know, all of these different sort of technologies, there's really, really deep benefit there.

And you know, we've been able to demonstrate proven results, not just help you identify the out of stocks and the pricing errors, but act on them.

So verify that action's really being taken.

And not only that action's being taken, but what does this mean as a benefit to the business from things like a margin standpoint?

Brad, there's a couple of things I want to call out that you just said there that I think are really important for our audience to make sure that they hear.

One is that you're basically saying that from an accuracy perspective, robotics are the best solution in your mind.

And also from a capital cost standpoint, Also one of the most cost effective solutions as well.

But the other piece of it that you said that I want you to talk about more because this was the unlock for me when I think a mutual friend of ours opened my eyes to this when he said the beauty of the robot too is that unlike other systems, it goes into a store and it does it the same way every single time.

There's no human intervention that mucks with the system design of that data capture.

Can you explain what that means?

Absolutely.

So one of the beauties of leveraging sort of an autonomous, autonomous mobile robot platform is it can keep up with things like changes in sort of the store layout or store environment.

So as an aisle or move or whether what products are on a shelf may change, these are dynamic sort of changes that this type of technology keeps up with.

It's much just like a self driving sort of car on the road.

So that's one of the biggest benefits for this type of environment.

The other is you're largely managing sort of a single device versus thousands of cameras.

In addition, it's not sort of planogram dependent.

I think we all recognize that planogram level, sort of execution accuracy in the industry can vary immensely.

So you're kind of starting off on the wrong foot if you're depending on sort of a product location model that doesn't actually represent reality, which many of the other technologies we talked about, you know, do depend on.

On that sort of model, we're actually analyzing what we see and certainly we'll ingest a planogram or authorized products list to know what we should expect to be in store.

But we're really sensing, you know, what's actually happening on, on physical store shelves.

Right.

Which is very different than like say the model where the employees are using their device to take a picture and capture this.

Because then the employees are doing it differently every store, every hour of the day, depending on who the employee is.

And there's a turnover in retail as well.

So it's a very key point.

But you know, with all that said too, it's not, it's.

Is it all about robotics?

Because like the robotics can also work in concert with other technologies or other systems that you've already talked about.

So.

So how do you think about that question?

Absolutely.

So when we brought TALLY technology to market, you know, we're very focused on sort of fast moving consumer goods stores sort of initially, but we were getting a lot of outreach from environments like sporting goods or environments that had packaged goods, plus sort of soft lines that may have been leveraging rfid.

So over the last couple years, we have announced a version of Tally that's either RFID only or computer vision plus RFID as well.

When you think about the core TALLY platform today, our goal is also to make a lot of these systems better.

So when you think about your average grocery environment that has a computer assisted ordering system or computer generated ordering system, this stream of data is only going to make those types of systems sort of much better.

But continuing on this journey, one of the things that Simbi is actually announcing today is sort of this concept of a multimodal store intelligence platform.

And what that means is we're moving beyond just the physical robot, but also including a fixed sensor called Talispot, which is currently in field trials at a number of large retailers.

Although the robot sort of covers the full store today, what Tally Spot really opens us up to do is capture a couple select areas within a store at sort of a greater frequency.

So those are often areas like your rotisserie chicken hot hole that you might have a lot of high churns sort of throughout the day.

It might be areas like high shrink sections that you might have in particular environments where you might actually not want to do restocking activity until a few units come off the shelf.

Right.

Other areas are like bottom basket detection, so it's easy to miss.

And of course when a card is is going out out if there's something on the bottom.

So this really all ties back to Simbi's vision of providing perpetual instrumentation really across the environment.

Well, Brad, I'm sure that a lot of our listeners have seen who've been following Omnich for a while.

They've seen our video down at Schnooks with Tally roaming the aisles, being touted as like another associate friend of the team working in that store.

But I love for you to share a little bit about what you've just been talking about.

Like can you dive into a few other examples that might not be on people's radar yet of.

Of like some of the implementations that you've put into place in some, some retailers around the country?

Yeah, absolutely.

Some of the other retailers that have shared publicly sort of our work include folks like Spartan Ash.

Okay.

Where they reduced their out of stocks in stores by about 60%.

They were also able to reduce backroom, controllable sort of inventory about 80% in their initial field trials.

This data was also highlighted at Grocery Shop about a year back.

In addition, we've done further expansion work with Wakefern.

You know, Largest cooperative grocer in the U.S.

some of the highest volume stores in the country.

They have grocery stores, you know, doing 2 to 3 million a week.

I mean it's just wild.

You know we've helped them reduce out of stocks in a number of their environments by about 50% and you know, the ability to repurpose labor of about 50 hours of manual scan time to sort of more important tasks.

Certainly BJ's wholesale is, has been an incredible partner as well.

It was a different environment for us stepping from grocery.

Yeah, right.

Saw a significant improvement in sort of their restocking activities.

Understanding what's up in the steel, where it's at in the steel when you think about your sort of back sock or excess inventory and then given the size of their boxes, leveraging our up to date product location and availability information to massively decrease their sort of pick times in stores.

And you know they're spread across 20 states.

And so with functionality like our virtual tour capabilities, you know, their operational leaders can step into sort of any of their clubs any day and sort of walk their environment.

So those are some of the examples certainly you know, the schnooks example, well, where they've not only reduced their out of stocks but leverage this data actually heavily to build the initial business case for their ESLs and have now moved on to leveraging this data set to drive greater transparency and accountability with their merchant partners as well as build monetization strategies around it.

So you can see how sort of the value case for this level of technology just continues to build.

Brad, I have to ask some pretty remarkable statistics and examples that you just threw at us, but is there something that you feel like is the most maybe consistent theme that you're hearing from some of these retailers that you deployed with?

What is it that's really the linchpin for them?

Is there one thing or maybe a hierarchy of things that they're saying like okay, we were maybe this is a big investment for us, but this is why it's make it makes sense and this is why we're going to continue to work with you.

Yeah, I think there have been a number of factors today, but I think it's come down to just this rationalization of the value of perpetually instrumenting the store and what that means to their, their business.

I would say there have been lots of pillars, right.

People have seen the out of stock or OSA benefit, the price and promotional benefit, the labor benefit.

But as time goes on, what they've continued to see is how does this Data tie back to retail media?

How does this data tie back to online grocery and other omnichannel sort of initiatives?

And they're really starting to see this as a platform technology that's helping them evolve their efficiency and effectiveness.

And I think any retailer today is thinking about how they leverage automation and AI to improve their operating model.

Yeah, that was going to be my question too.

You know, similar to Ann, it was going to be like, I can't.

I mean, the reason we're so big on robotics is we can't think of another technology that creates so many different value streams.

And so to your point, just so everyone gets that it really isn't about one, it's probably, what I'm reading between the lines here is it's probably retailer dependent to decide which one is the most important to them, but really it is, yes to all of them.

And therefore it is a platform play here that gives you the data to understand what's happening in your store.

Is that.

That's right, Brad?

Absolutely.

And in further that point, we've seen different executive champions across these businesses carry this technology forward.

I mean, out of the gate.

Most people assume it's a CIO or CTO taking technology forward.

But we've had a lot of, you know, head of store champions, we've had chief supply chain officer champions, we've even had head of finance see the cross functional value of this technology and really champion it to take it forward.

When you got the head of finance singing your praise so you know you're doing something well.

Well, that, that's a great segue too, because that gets me to my next question that we wanted to ask you, which is, you know, what are some of the.

Because we've seen a lot over the course of the last 10 years or so, we've seen, we've seen some starts and stops as it comes to robotics and robotics deployments.

And what I always say to them is, like whenever I hear those is I think those executives that are stopping just haven't figured out how to deploy them correctly to get the value.

So, so what are some best practices that you can share for maybe those retailers that are thinking about it that haven't quite deployed them successfully to this point.

What are some best practices you can share to ensure that those deployments go well?

Yeah, Chris, I think with your first point, I would state that maybe not all shelf intelligence technologies or partners are created equal.

I mean, we talked about this a little bit earlier, but I think there's some retailers that step down a path with a particular partner, maybe they were early in their journey or their technology development sort of life cycle.

And so that has put sort of a bad stamp on the space.

And it presents the exact question which you just raised is, you know, why is this, you know, why is this working for others?

I think internal alignment is one of the biggest pieces.

We do see that sometimes innovation teams can grab hold of this technology but not have the core support of really the rest of the capital committee or the rest of the leadership team.

And so as we talked about earlier, this technology really touches everyone in the business.

So making sure folks are aligned on what does the proof of technology phase look like or what is the proof of value or proof of economics?

We typically see like these one to two chapters sort of early in this sort of partnership journey.

And then once we've nailed sort of proof of value and proof of economics, it's, it's sort of really all about scaling, but making sure that communication and sort of alignment is key.

I'd say the other point that's really important is this is really like the country's first introduction to sort of robotics.

Right, right.

What most consumers know about robotics is kind of what they see in the movies.

Or you know, they might have a vacuum cleaner at home now if they live in Silicon Valley or these types of places, or they have a Tesla, they might be experimenting with you know, sort of self driving.

But it's, it's really important for retailers to I think both recognize which partners are making this technology really intuitive and in a way that can really kind of coexist with kind of shoppers and store teams.

So I wouldn't underestimate, I think the design component here is as well because it is so important.

I love that, Brad.

I think that's a great segue also again into our next question, which is about that I think the biggest concern people haven't seen robotics.

And when they hear the word robot, they get concerned that it's taking the place of human labor.

And what's it going to be like interacting with a robot in store versus what they're used to in their traditional shopping experience?

So I'd love to hear specifically like, what does this, what is this doing for the store associates in the retailers that have deployed these robots and what does that mean for kind of the bottom line of both, you know, employee happiness and satisfaction at work and then also what it's doing for retailers?

Absolutely.

So our goal with store teams and technology like Tally is to really be a power tool to them.

And most Store teams have really recognized this.

In fact, you know, we do a lot of store survey work, retailer survey work, NPS type work, and 90% of store teams today, actually, once they experience tally, actually don't want to do their jobs without tally sort of anymore because it's taking them in such a big step forward of, wait, I no longer have to look for problems.

This thing just tells me, and it helps me to really identify root cause.

So when the regional director sort of steps into store and is like, hey, what's going on with these shelf conditions?

They can sort of point and say, hey, look, this is a warehouse issue, or this is sort of a DSD issue, sort of, et cetera.

This level of sort of root cause and accountability up and down the chain has been sort of really empowering to these store teams.

Well, I, I know, Chris, you remember when we interviewed Kim Anderson at Schnooks, and we were asking her, because she's head of store operations there, she was like, we're like, what do you.

What do your teams think about this?

Do you remember what she said?

Yeah, she said something like, if I were to remove the robot, the first thing I'd have to.

They'd have to, like, I, I.

She said they'd rather.

I don't.

It was something crazy.

Right?

And like, she said, they'd rather take.

It out of store.

Yeah, they'd rather.

They'd rather see her leave than take the robot out of the store or something like that.

I was trying to see.

I don't.

She said it more colorfully than that.

I was trying to figure out what, what it was.

But, I mean, I think the whole purpose is exactly what Brad was saying is, like, it really has become a tool that can, like, do the stuff that the associates don't want to do.

And we saw firsthand when we were in Schnooks, like, then that associate who would be stocking shelves, who would be doing other things, is interacting with customers there and in, specifically around customers.

What.

What are customers doing when they see these robots in store?

Like, what's their response been?

And what have you heard from the retailers that you're working with about how they, how they work alongside people shopping in the stores?

I mean, we've had such a unique opportunity, and, I mean, you know, more than a half a billion consumers go into grocery stores every week to get, you know, milk, bread, juice, essentials.

So the fact that we've had this opportunity where they can kind of look over their shoulder and sort of seeing a robot has just been like so exciting.

And in fact, what we found is the majority of shoppers actually favor stores with robots because they clearly understand what it's doing.

Yeah.

And it's actually driving a better customer experience.

You know, obvious things that, that we've found being out in the field is, you know, kids certainly love tally.

We've actually found families with children spend a substantial more amount more time in stores.

Price.

Yeah.

Which is, which is sort of certainly interesting.

And yeah, so it's been a really positive sort of experience, you know, overall.

Yeah, I loved your point you made too, about the store director and the store visit to a given store too, because that can be such a unproductive exercise if people don't have the same data that they're all looking at and talking about together.

And so thinking about it in that way is just really powerful too.

BRAD just on that one really quick, I think it goes both ways.

In fact, you've spent enough time in stores that you often have a hint of when these activities are happening, whether you should know or not.

You know, having the ability to look at a store 24 by 7, 365 has been sort of really eye opening, you know, from a corporate perspective.

And you know, we like that we can provide that level of functionality where people in the home office can dial in anytime.

Yeah.

And it's, and it's valuable both before the visit and during the visit.

Right.

And then probably after the visit from an accountability perspective as well too.

Right.

So you get, you get all three of those things which you know, again, how stores are traditionally run, you just don't have that data clarity across all three of those steps.

So.

All right, Brad.

Well, I want to, I want to.

Ann and I wanted to put you on the spot here because you know, we like to make good on our claims.

BRAD we like to, we like to make good on our claims and we've said on record and I guess has now said 2024, but I guess 2025 is the year of the robot.

So what needs to happen for that prediction to come true?

What needs to happen for us to make good on that prediction?

Brad?

Absolutely, Chris.

I mean, certainly we're seeing increased narrative from all the retailers we're talking to about how they can leverage kind of automation and AI to improve their operating model and efficiency and sort of effectiveness with these store environments.

Like this is board level discussion that's happening.

I would say there's sort of this ChatGPT moment.

So what gives me, you know, continued confidence of this type of sort of automation becoming more ubiquitous.

You know, we actually signed more new logos in 2024 than we did almost in sort of all of our history.

And in fact, we continue to see a lot of tier one momentum.

So I think maybe a sub bullet to 2025 being the year of the robot is we'll probably see our first sizable sort of tier one grocery deployments of this technology as well.

So some of the largest players, obviously, given their position in the market, have taken a little bit of approach of sort of wait and see sort of up until now.

But there's very aggressive sort of initiatives, you know, certainly sort of across the board in a number of those different players.

What we're also seeing is this type of technology ending up in sort of formats adjacent to grocery.

So over the last 18 months, we went from grocery also into club, mass, alcohol, farm and home, doing more active work in hardware and home improvement and continual amount of sort of international interest.

Right.

If you look at environments like Canada, western Europe, sort of Australia, they look very much like the US today.

So we do believe strongly that this type of technology will become much more ubiquitous next year.

And Brad, where does the product, specifically, where does the Simbi product need to go next to keep evolving so that 2025 is the year of the robot and then we see more and more 26 and beyond?

Absolutely.

So we're always keeping pace with sort of innovation using the latest in sort of sensing and sort of compute technology.

But obviously we've done a lot of chain wide adoptions with where the product sits today.

So so much of our energy is focused more on additional value realization.

So how do we continue to take all the great data that we're deriving from these stores and more deeply fuse it with all the data that's in a retailer's back office?

In addition, some of the other examples we're focused on is how do you deploy, you know, foundationary models or large language models on top of this data to make this type of data more accessible to facet it in different ways.

You know, just being able to ask questions against the data, which, which stores are having the deepest challenges, which, which have the best restock rates, you know, which merchants or sort of categories are most problematic, these types of things.

We're also seeing a lot of advancements in sort of core computer vision that allows retailers to unlock some of the more challenging shelf situations at scale.

So some of those examples include things like plugs and spreads, so where you may be out of a specific flavor or sku of a product but you have another in that family adjacent to it, so you sort of face that over.

We see a lot of scenarios in stores where that original product that was out of stock stays out for a very long time and certainly cost the retailer both from sort of a top line and margin standpoint, even inclusive of switching and substitution.

So leveraging these types of advanced capabilities to detect some of these scenarios.

Got it.

So I want to ask you just to make sure, because I think you said a couple words there that I'm not sure audience is not familiar with.

Plugs and spreads.

So basically you're meaning the practice of the store manager, the store team, making sure that their planograms still look good and shoppable despite their inventory not being fully available on this shelf, which is a, which is a hot button issue right now, because I was talking to.

I won't name the retailer, but it's one that's very near and dear to both Anna and I's heart, where they're actually potentially instructing their teams not to flex.

Flex is another word you hear for it versus plug and spread.

Flex their inventory to cover the outs.

Because, you know, generally speaking, that was the mark of a good store manager, was somebody that always kept their store looking beautiful.

And you're saying that may be a good thing, but you want the data to back up whether it is or is not.

Is that right?

So what we're actually saying is it's actually a bad thing, but we don't think when it's happening.

So what we've been able to prove in many cases is, you know, that particular skewer flavor isn't in that store environment.

And that traditional skewer flavor has a traditional sales velocity.

And when you start looking at that data, even inclusive of things like substitution and switching, you start to see that many plugs and spread scenarios are actually sort of detrimental to both top line and sort of bottom line.

So these are some of the things that this type of technology has uncovered.

You step into a retailer that's like, hey, we think we have a very efficient operation.

We're only a 2% out on shelf rate.

What's like, hey, let's double click on that.

Yeah, and, you know, see what's sort of really there.

And certainly, you know, we often find opportunities and it's, it's not the store team's fault, as you said, Chris, many times they're instructed to do this.

But what, where we're seeing the opportunity is let's get the right product back on shelf because it was supposed to be there from, you know, the original planogram design.

Right.

And I would imagine Brad, that that gets back to your earlier questions of understanding where the problem is.

Is it supply chain, like you didn't have the product to restock or you know, like what, what's the solution that we can look at when our regional manager is in store?

We could talk address this.

We can get through and like have some consistency in what we're looking at to try to get to the root of the problem.

Exactly.

And we see we actually have this internal forum at Simbi, we call it Skew stories.

But we get these incredible quotes from our customer success team, from our sales teams almost daily from stores all across the country and sort of globe with these types of scenarios.

And perhaps this is something we should find ways to share with the world because it helps to highlight just the type of shelf execution challenges retailers and brands deal with all the time.

I would say another big area of focus that we've seen with retailers really operationalizing this data themselves is a big focus on dsd.

So the direct to store delivery partners, milk, bread, soda, juice folks.

Because when a consumer comes into a store they don't.

They attribute that product to sort of the retailer.

Right.

100%.

Yeah, they have.

They would have never have any idea that that's how it gets.

They're not blaming Coke for not being there.

Yeah, right.

Yeah, yeah.

So this data is often used to optimize truck rolls into your point earlier, Chris, what does the shelf look like before they come in and what does it look like after?

Right.

Yeah, it's really, it's really interesting too.

Like when I think about this, like from having been a district manager, a store manager, like you know, what is the size of the out that I'm having to talk about too?

And like having that information before I make the decision on what to do, do I flex it, do I leave it open?

You know, that's just information that should be discussed which historically has not been.

And there's probably not one right answer for every situation to do the same thing either.

And that's the beauty and art of retail, which is why we need to bring data to it.

Yes.

And that's where we help them prioritize like in our mobile app directly, we will help them understand the cost of that out.

You know, what is the missed sales opportunity today, this week for not having that product on shelf as well as how is it relatively prioritized against all the other tasks that are out there?

Right, right.

Your point not all outs are created equal.

Right.

Yeah.

And how pissed are the customers that they're potentially looking at empty shelves in that section of the store too?

Which is a factor too, Right?

I mean, we hear that a lot.

Oh, man, this is great.

Yes.

Brad, thank you so much for taking the time with us today.

It was so insightful.

It was so fun to get to get properly schooled on robotics and what we can expect in the year ahead here.

I'm sure there's a lot of people who are going to be reaching out.

They want to get in touch with you.

What's the best way for them to do that?

Absolutely.

You know, first and foremost, I would encourage folks to hit our website, simirobotics.com you can reach out to me personally.

I'd love to hear from you.

It's just BradIMBY robotics.com or search for me on LinkedIn.

Brad BegoliaIMBI and would love to hear from you.

And Chris and Anne, thank you so much for, you know, the opportunity and dialogue to be here today.

Wonderful.

Well, thank you, Brad Begolia.

That wraps us up.

Brad Begolia of Simbi.

Thanks to everyone out there for listening in as well.

And on behalf of all of us at omnitalk, as always, be careful out there.