Tech that Tastes Good
Galley co-founder Ian Christopher and CTO Matthew Ferguson on why generic enterprise software keeps failing kitchens, and what has to be true of the data underneath before any of it works.
About this session
Most foodservice software starts at the interface and works down. This session starts at the bottom - with the structured food data everything else depends on - and works up to what the team calls a culinary operating system.
Galley co-founder Ian Christopher and CTO Matthew Ferguson walk through why the industry needs specialization rather than another generic enterprise platform, how a system learns the language of a kitchen well enough to catch a unit error, and the product test they hold themselves to: simple, lovable, and complete.
Key takeaways
- Why industry specialization beats generic enterprise software once you get into real kitchen workflows.
- The building-block view: structured food data at the foundation, a culinary operating system on top.
- What it means for software to understand the language of a kitchen - and to gently flag a pound of salt that should have been a teaspoon.
- How dietary and sourcing constraints get modeled rather than bolted on.
- Why sub-recipes matter: thawing, brining, and marinating are steps with their own 24-hour clocks.
- The internal test for finished software: simple, lovable, and complete.
Transcript
Full transcript
auto-generated from the recording · click to expand0:02Now that I've invited two of my good friends, our beloved co-founder, fearless Leader of Galley, Mr. Ian Christopher, and also Mr. Matthew Ferguson, our Chief Technology Officer, who's been behind the helm putting all of these cool toys and features together. So, I wanna start off the conversation by letting these two gentlemen introduce themselves. And honestly, I'm gonna hand it over and let them do a bulk of the talking 'cause they're the subject matter experts here.
0:31But we're gonna talk a little bit about what the food service landscape looks like. What are the different solutions that are out there? What's the different types of pain and things that you're feeling that we can help solve for? Largely part of the dream that Ian and his brother-in-Law, Benji, have put together and brought in experts like Matt Ferguson and others to help us bring to life.
0:52So Ian, why don't you kick us off. Tell us a little bit about your background and how we got Here. Thanks for thanks for having me, gunner. It's always fun to make the webinar. So yeah, my background is it really, it started in agriculture. That's how I got closer to food in general. Well, I guess that's kind of a reverse journey.
1:10I didn't really have an opportunity to not be close to food growing up. Grew up the son of a caterer, so catering was our family business. And back then felt a little bit more like forced labor. I was stirring sauces and rolling pasta. We had hours into the night but loved it and loved just what the power of food was to bring people together.
1:29And so knew I always wanted to do something with food. it was kind of a glutton for punishment and hard work. So I went a little bit further up into just sort of the value chain of food and went into the production side in agriculture. So, spent about 10 years playing in the dirt doing commercial, large scale organic or agriculture, and then figured out that there was a way to get out of that and and get back into, really bringing efficiency to the f food service industry in just a completely new and exciting way.
2:02And that was by partnering with with my brother-in-Law and co-founder Benji to to start galley. And really with this vision of empowering, culinary operators to make more profitable decisions and ultimately lead better lives as a result. And that's only possible because we've been able to partner with just great people and specifically Matt Ferguson here, our beloved CTO and and gentleman who's, who's helped make this vision a reality Boy background. tying it into food for me has, certainly been a lifetime of just being a good amateur chef around the house.
2:40And learning. I built my first hand-built pizza oven, brick pizza oven 10 or so years ago to use that. and then have built a Brazilian oven as well, or an Argentinian grill beside our house. But it all comes back to, working in food service as a, at a, as a person in college, getting through school and, living that life for a couple of years to pay the bills and get through school and then start a career, do the tour of software engineering in many different industries.
3:13And then finally hearing story from Benji and Ian about what they were trying to solve and realizing the incredible impact it could have. And understanding, I think, from the team that this had not been done. I, we thought properly yet, and lots of pieces out there and how we could fit it all together. And there was this massive opportunity just became super exciting to me. It felt like one of those, wow, nobody's done this correctly yet.
3:38We can do this different. We have the power to do this. And it was just great timing. So I'm super excited to be here with this vision from the team. Yeah. Awesome. Well thanks for all the background and likewise, my history and food service has kind of brought me into this funny world of technology and data, but I love the fact that you're, like the engineer's engineer, you're building pizza ovens and, like really digging into the mechanics of what it takes to get food out.
4:04So I think everybody here has that kind of unique understanding of, food and the love of food and hospitality. Ian, help us kind of understand like some more nuts and bolts around how you got here and, what's the landscape look like? I'm sure people out in the audience know this, but for me, it's always kind of fun to look at it squarely and just hear it from other perspectives.
4:25Like, what's going on when it comes to technology and food service? Yeah, I think the probably the answer is not a lot and all too much altogether at the same time. so I think, there's such a large spectrum of how people access technology and food service. like I mentioned growing up, the son of a caterer on the smaller SMB individual food operators, a lot of times's, just manual processes.
4:50And so I vividly remember my mom's recipe book storage area, and looking at it in just the thousands of yellow stickies that lined, essentially her recipe database, which was this closet in our house that housed this corpus of information. And then how she made that more operational for herself was converting those into handwritten notes on yellow legal pads. And so our house was just a smorgasbord of sticky notes and legal pads, and this was all of her food data that she used to run a small business.
5:23We have commercial operators that honestly aren't far off from that. lots of folks that we've run into and and get the opportunity and privilege to speak to are using very manual processes, so still in spreadsheets, still using pen and paper to run their daily operations. And honestly, we have so many pieces of technology in this space now that it's really difficult for operators to navigate sort of what the critical needs are and then how these things all fit together as well.
5:52So the landscape kind of looks like a patchwork quilt, if you will. there's, there's one system for recipes, there's another to manage nutrition and maybe even a clipboard floating around to handle inventory. So it's, it's an absolute mess. And we haven't done a good job of providing one single source of truth and one single operating system for culinary operators that really allows them to do what they're passionate about, which is make food and hopefully drive greater levels of profitability.
6:22Yeah. So as we think about the competitive landscape and all of the different point, what we call point solutions, meaning it solves a singular problem. So that could be a QuickBooks, it could be Excel for your recipes. Then you've got some sort of an ERP planning system if you're doing production, you've got LA Labor and workforce management, right? Probably using some version of Workday and all that other stuff.
6:47And I'm not saying we solve all of these problems into the dirt, but boy, wouldn't it be nice if we could collapse so much of that around like this core piece of information that runs our business. And I think it's recipes, but I feel like that's one of the commonalities that registers for everybody as you're trying to solve all these different problems, you could end up with all of these different solutions.
7:08Is that kind of a fair way to think about it? Yeah, absolutely. I think we, you name the solutions and then you have to think about the interconnectivity of all these things. we just, we call it digital duct tape, but the, these systems aren't designed, natively always to integrate with each other. So lots of times there's manual process of moving data outta one system into another, or transforming that data to actually get it into the next system.
7:30And it just ends up creating a lot of inaccuracy and a lot of headache for operators who, honestly, food service operators need great data right? In time. And so they need that information, they need it accessible, they need it organized, and they need to be able to trust it in order to have, a profitable outcome. And so, I think it's not only just the quantity of, point solutions and things that are flooded in our market, but even the way that those things need to interact with each other, that can bring greater levels of efficiency.
8:00And that's, that's why, to your next point, why we said there's gotta be a different way and there's gotta be a better entry point, and lots of these point solutions pick entry points to this problem set. But we chose the recipe as our way, our beachhead into this data set. And I think that's been one of the core differentiators of Galley, and it's served us really well, because that really resonates with the culinary operator.
8:25They understand the language of a recipe and then can derive other data from a really well structured recipe. So I think, to your point, yes, we did believe that the recipe was the right starting point, and and that's really been validated through our customer base and most everyone we speak to in the industry as well. Yeah. anything to add there? Fergie?
8:49For me, it brings this point about, it's a really, I think it's a really natural progression of software from point solutions that solve specific problems and, niche, we call 'em solutions, maybe or point solutions, but the data model that supports that one niche solution, isn't thinking broad enough yet, or is not flexible to do other things. And so it's only natural that once there's a collection of those niche and point solutions that you can see the bigger picture that you can then really envision, you know a next generation of software.
9:26And I think this happens in industry after industry whether it's accounting or, you know ERP or any almost in manufacturing of different sorts that telecommunications, you've seen this exact same evolution of point solutions turn into, Hey, let's, let's, let's bring all these together. And sometimes they'll start with bringing them together by wiring them up, but ultimately that isn't efficient.
9:56Whether it's done by, a single operator buying up solutions and then, reselling them under a different label or it's, an integrator wiring them together and selling them, or the end party, the customer wiring them together, that becomes inefficient and isn't, isn't the most elegant way to do it. So re-envisioning that data underneath is how you get to the next level and take the leap forward.
10:21Yeah. You mentioned different industries, and what I've noticed is that you've got this broad, let's use ERP for example, enterprise resource planning, these broad strokes at solving that problem of how to produce a widget, and then you back it into other industries, but you probably, like, maybe that was originally built for a healthcare or retail type environment, right. and then you hear things like, okay, we're gonna implement SAP and people go Uhoh.
10:48That can be a huge undertaking, right? I've been around it in my enterprise life before, but when you bring everything down into a particular industry, it requires that specialization. And I think the one thing that I had to wrap my brain around in my career is the fact that there is such a thing as food data. Ian, you kind of articulated this up top, I've got mom's cookbook and there's gonna be pieces of that over here, and then you gotta start assembling ingredients.
11:13You gotta start bringing in nutritional data and all that stuff. what happens to food data in a commercial kitchen today? Where is it, how do I find it? what do I do? I has really resonated with me is like, think about a cook or a chef, going to put together a complex dish and imagine all your ingredients just scattered in various rooms around your facility.
11:35That's what it's like to have, not have an understanding of your food data. So yes, you hope something is in your walk-in maybe one of the three things you need is there somehow an ingredient ended up in the parking lot and maybe something's in the bathroom down the hall. But these pieces of data are just floating around this ecosystem, this environment. and they need to be consolidated and optimized in order for an operator to truly benefit from having that data in the first place.
12:03Otherwise, it's, it's almost useless. And to your point, some people are, sometimes organizations are altogether unaware that they even have food data. what are we even talking about when we say food data? These are the recipes, the menus, the vendor items, your purchase orders. It's all of the data that allows you to operate and execute your operational plan daily. that we're talking about here.
12:27And it's just the majority of food service operators EA are either less aware of the critical importance of that data as it relates to their profitability and just how it can, wrangling that and managing it well can really lead to operational excellence as well. So yeah, I have a, an actual, case where I was talking with an operator, an owner of a business, and they're a, top tier producer of food here in the states that that literally has taken their accounting software because someone said, well, we can do it in the accounting software, right?
13:05And they have built their food data into accounting software. They've created bill of materials just like a manufacturer, and used those metaphors and extended it. And, now you can imagine the problems with upgrading their accounting software or, who are you gonna break the accountants? Or you can break the food guys, which system do you wanna break when we do the upgrade?
13:28And it's, it's now, of course, and they're a multimillion dollar, year business that made a decision like that because the tools weren't really appropriate for them yet, that they couldn't, they couldn't find them at the time. Yeah. And I think that story surfaces over and over and over and really leans into this vision of like, okay, how do we structure the data as a good foundation so that we can build the other parts and pieces that are required in order to have this really a culinary operating system is what we've been talking about over at least my tenure here at Galley.
14:06So how did we arrive at this? if you put culinary operating system up against what you had described Fergie, where maybe I'm pulling other parts and pieces and other pieces of technology. I think, Ian, you said digital duct tape, right? that's what everything feels like, but our vision here could really be to bring all that into one house. Is that kind of how this pans out?
14:31Absolutely. if you think about the, these building blocks at the bottom of this diagram, this slide where we have the structured food data at the bottom and what we call the COS at the top, the, that icing on the cake might be COS, but at the bottom we have this root level set of building blocks and recipes being one. And we think, the most, I don't know if I'll call it the most important, but it's certainly the the, a lot of things revolve around, like the recipe is where everything has to work to make the recipe work.
15:02You might be able to have vendors and vendor items and get some value, but those vendor and vendor items flowing into the ingredients and those ingredients rolling into the recipes is the relationship you're looking for being able to define what we call catalogs. Like this is what we sell here being, in a catalog defining the things, the products you sell which are how we, recipes being how we make it, but catalog things being what we actually sell from those things.
15:35And being able to understand this differentiation between a recipe and a product is starts to be this subtle difference that we've, started to unweave from this model. it's, it's a complicated ecosystem of data. And and from a, as a data guy, as a software guy, trying to model it properly, is quite a challenge. And having, now that we're into this many years, it's, the model is not the model we created when we started.
16:04I think the vision's the same, but we've even learned a ton about working with our, working with customers directly to say, ah, that there's a new layer we didn't, that we now understand that is gonna make this better. And another layer, and we keep, we keep pulling, the parts of the onion back every year and seeing new ways to better, best describe this data.
16:26And that's, I think, created that flexible model that we're in and making sure these building blocks if we need a new building block, we can add it for, I think is an important part of the concept here of as we have these building blocks, then we can put the building blocks together to create what we call like an invoice manager, production, execution, production planning, menu engineering.
16:48But they're all the value added applications that sit on top that create the, the incredible economies of scale for our operators. Yeah, I love that. So you've got this very somewhat rigid approach to building technology with intention and making sure that the data model supports it, but in that you've got this incredible flexibility so that you can continue to bring on parts and pieces, different use cases, and not break the whole thing, right?
17:14Kind of like your story about the financial system getting, reverse engineered into a recipe management system, you're gonna break one side of your universe or the other. And it sounds like galley's been really intentional about not doing that, leaning into what we call that recipe first approach. Is that a fair summation? It started with the recipe for sure. And that, and it, that maintains the, I think that maintains the integrity of everything we do.
17:41If you want cost a recipe, if you wanna scale a recipe, if you want to know the nutritional of a recipe it requires almost all the components. And then recipes are then used in when we, when we're thinking of menus or products that are gonna be in a catalog. And so recipes are then used, to be the what we produce.
18:06So we'd like to think of this as like just simple, lovable, and complete. So when we produce software, how do we know we're done? We think of it as, is it simple, lovable, and complete? That's our step one. When we produce software, can we make it such that, this, the first version of everything has these components. And, I think we've been working with and we're gonna, show some of the first cuts of the, this next generation of software that we're producing.
18:36And our goal is that it literally is simple, lovable, and complete. Like, it's the beginning. It's truly usable software day one, and we're listening to our customers, tier one major customers about, and really standing side by side with them in the trenches and saying, what, how would you build this? And sometimes in, in many cases, spending a year making sure that we're building the right data model to support these things with that recipe first approach we're thinking about machine learning from day one about what is it that we're, how we're gonna capture data, how we're gonna capture insights so that when they're operating the the system that we can actually saying, Hey, you're probably meant to use this unit and not that unit.
19:24And then when we make that recommendation, are we listening to what their answer is? that helps to learn, make the system a learning system and then make sure everything's accessible. Make sure we have an open API. So maybe you do want, a partner to use the system for some other purpose or for displays or something that we don't do yet. make sure that you can report on the data and make sure there's data warehousing available.
19:50I think this is an area, just data warehousing alone is an area that, in the point, solutions are typically not able to be done just from a standpoint of affordability. data warehouses are expensive to build and maintain, but the ability to look back on data and see the decisions you made over time truly separates you as an enterprise and an enterprise approach to what you're doing.
20:13To say, I can make anything into time series data. Like, I can turn my cost of a recipe week by week over 52 weeks over the next last few years into data. If I just snapshot the, what it was or what the nutritional values of that recipe was over time, the choices that go into that recipe over time those are all become data warehouse and analytics that we can use later.
20:39So, this kind of just, we'll stay here for two seconds and then I wanna move on because you mentioned machine learning, which smells a little bit like artificial intelligence, so I wanna unpack that a little bit. Sure. and this isn't to say galley solves all of these end points right on the outside of the circle, but it serves as that central repository for the recipes and the data and the features and the things that you can do around it.
21:05Because one of the, one of the questions and one of the conversations I had early on with Ian and Benji when as I came into this was, what's the evolution of the data? Like, what can we do now that we've optimized that data? And, there was this really enlightening approach and conversation I had with Benji around like, okay, well, the data at its core doesn't have any smarts to it, right?
21:28You need to organize it and optimize it, and then you can get to this top tier of insights, intelligent information machine learning and artificial intelligence, maybe END. What kind of thoughts or reactions do you have to this staged approach? how does it influence the way that we're making decisions around the technology? Yeah, I think that this is an evolutionary journey just as it's mapped out here of, progression of sort of what you're able to do with various, abilities to manage your data at different levels.
21:56And to me, I kind of map it back to our, at a first primitive level, like our understanding of the language of a kitchen. if we kind, we're kind of thinking back actually on that last side, what makes us simple and lovable is, I think it's this notion that we really do understand the language of the kitchen. And when we say, we talk about ingredients and recipes and menus, those are, those are the containers for a lot of other data, but those are the containers that culinary operators know how to interact with and they're familiar with.
22:27So I think, taking that recipe first approach that we, that we claim to is also, it's, it's also characterized by understanding ingredients and menus in these venues, these places in a kitchen operation that that map to a language model that operators really understand and therefore can interact with. Well, when you're, when you're defining the language of anything, you're getting ready to optimize that language.
22:52I think we've all been now sort of been enlightened to what AI is and how it works. And so, first and foremost, we knew we would have to define a language before we move into, what I don't, I won't steal all the ML and AI fire here, I want to pass it back to Fergie. But before we knew that data was truly ready to be optimized in some really incredible ways that bring all new levels of efficiency and insights to the operator themselves.
23:17And so, again, I think it's this, it's this scaled journey that happens through a baseline understanding of the language of a kitchen that allows us then to leverage, higher and greater insights and optimization. as people progress down this journey of not only capturing that data and then interacting with it and then allowing, ML and AI to optimize it ultimately.
23:41So I'll let, I'll let Fergie give his perspective here on where the food language is today and how we're, we're sort of setting the stage for great things in AI and Ml. I think there's an interesting like, analogy and, 'cause I'm sure there's lots of people on the call that aren't maybe familiar with, maybe, some, they all understand the words, ML and ai, but, let's just take a really abstract example for real quick.
24:07And there's some words from that abstract example, like facial recognition. Like, how do I recognize my face versus gunner's face versus, in when you're, if someone was scanning our face and it's through the facets, like, there's the eyes, the nose, the distance between the eyes, the, all these things, there's all these measurements that are made and off of these facets of a face, and we understand a face and the ears and the hairline and all of that makes a very distinct caricature that we can then describe the human face or a dog's face or a cat's face, and you can pick them, you can with high confidence, identify that face over time.
24:46Well, we can do the same thing by describing a language and the characteristics and aspects of food data and, what is a recipe, but a set of characteristics and what is a particular ingredient, but a set of characteristics when it's used. So, the use of salt is gonna be a certain pattern in your organization. And when it's part of a recipe an Asian recipe versus a Mexican or Italian dish has a certain set of characteristics.
25:17And when you're adding savory and salty things, these are all, highly abstract ideas, but we're building a language model ultimately that can understand, and help you keep your food data aligned and help you make decisions and help operators improve data quality over time. So that if someone's making a recipe change, ultimately is what we'd like to see is, it's, we're not just cleaning and helping you massage data when you bring in your data the first time, but as you're making updates to the data, as you are making changes, or anybody in your system is making changes, you could be correcting them, or the system could be gently nudging them.
25:58Hmm, that sounds like you were using the wrong units there. You didn't mean one pound of salt, did you? I think you meant one teaspoon, because those kinda weird, that would get caught one way or another, but wouldn't try catch that first, immediately. And the system can, can be, that's where we, that's where we're heading, is the ability to use the data structure that we've, that we have and normalize and teach AI about the characteristics of recipes, ingredients, the units, the pack sizes, such that when you're doing receiving, when you are performing cycle counts, we understand what's normal, what's not normal, and we can make sure that the operators are not accidentally doing something that then has to be corrected tomorrow because we caught it.
26:52Just simple things like that to reduce mistakes, and learn from how things have been done in the past is, is gonna take us, is where we're going. And I think it's gonna take the food service industry, a long ways. I love that in the spirit of trying to prompt the operator around certain things that we know, I don't know that this is proper ai but it is like the next evolution as we think about these core components of a menu planner and a production planner.
27:27And I think other point solutions that have been built on the backs of financial planning or traditional ERP or whatever struggled to get here, right, in a very useful way, because that wasn't the original use case. That base data, that knowledge of food, hospitality and food service operation isn't there. I think they can get there eventually, but, we're there now, we're ready to go with these like really robust comprehensive features as simple and lovable as they are in this early iteration of it.
27:59Tell us what we built with menu planner and then we will slide into production planning a little bit. we've been so fortunate over the years to have people, love the applications we've been building around recipes and the building blocks and, receiving and cycle counts and, perpetual inventory. But we really raised the bar in the last year and went after some things that hadn't been done, we think at all in the industry, or certainly not done what we considered maybe, right.
28:28And menu planner is one of 'em. using all these building blocks, we were brought an opportunity to work with our, some of our existing customers, large operators, to invent how to, how to build a menu plan looking at, a 45 day period or a 30 day period, or even a 90 day period, whatever, and say, we need to build a plan for say a school to hit a budget to have, lunch and dinner or breakfast, lunch and dinner.
29:01These groups, we can ultimately plan the a six week cycle or a or a one week period. So being able as a, as an organization, firstly define a template or a cycle that tells you, I have these operators, I have these locations different, and each one is gonna inherit a cycle so I can create a K through 12 fall cycle potentially, or, you can think of it or other iterations, maybe it's a, maybe it's a a hotel or maybe it's a, we have another folk people we work with that might be like food services at a mine.
29:38And they have three shifts every day that have to get fed and they feed, they have a certain type of things they like to serve, and they want to hit a budget and they want the local chef to have a lot of control over what ultimately gets planned. But at the corporate entity, they might want to put a cycle together to say, Hey, here's your basic template and here are the things that we as a corporate entity want to decide.
30:03And then you as the local chef can then work within some guidelines and be able to express exactly what you're gonna serve each day. But you have now a framework to work within, and you can manage some constraints or restrictions within, this corporate guidance. it'll tell you when you're out of bounds and when you're spending too much money on a, daily, weekly basis such that you're on budget but you're also hitting nutritional guidelines, 'cause a constraint might be that there, you can't have shellfish at this site or at this school or other constraints that you can imagine.
30:39Any dietary constraints. Kosher bacon kosher, yeah, kosher bacon. I, unfortunately probably never gonna happen, but, right. But the you can imagine all the constraints, whether they're all constraints your own custom categories. We have a customer who labels food as this is green and this is red, and this is yellow, because they want to establish a a mixture of what they call profiles, and so they have their own way of labeling things, and they wanna make sure that there's a mixture and that they're not serving too many of this color each week.
31:17This is, their garanimals approach to how I dress myself. But they serve food. And, that's an important part of their secret sauce. And we're not trying to steal their secret sauce. We're trying to say, Hey, we can help you make sure that you master your secret sauce on a daily basis. However you do that, we think everybody could benefit from that type of ability, and now you just go execute your secret sauce of how to do that.
31:42So the menu planner, we think is, is this next generation tooling that helps you stay on track on a budget, create events. You can define these events as a lunch service, but then you're serving, you have three options in the meal. we've created a new concept called a, what we call a collection. These are things we serve together. So how do you describe, and this is a common problem we've seen in many solutions, how do you describe a salad bar or an ice cream bar or a box lunch?
32:13These are all concepts of, I, they're not a recipe, but they're things I serve together. And so how do I put constraints that the salad bar needs to have three le, a lettuce option, and you, and the, and the chef needs to pick for, two two vegetables out of the many vegetables that could be served in the salad bar, and make sure that there's three dressing options out of the many different dressing options and recipes they have.
32:40So that's an interesting data modeling problem to make sure that they are, the company or the profile is being put together of how we describe a salad bar. And then letting the chef like have a lot of freedom to solve the problem. But put some guardrails around the problem. If you think about it's kinda like going and shopping for a car. A car company says that you can pick your automatic or your manual transmission, your engine size, color, interior, but you gotta go through a configurator in order to actually choose your car online.
33:10And then you can have it show up at your dealer. Well, we're building a configurator for menu planning, and we're helping the operator by creating the constraints around what those events might need. the impact that something like this has on an organization, and actually it's, it's customers as well. I think it's been really awesome, as in early, betas of rolling this out and not only knowing that, we've increased financial efficiency, the ability for these organizations that use it to be more profitable 'cause they're wasting less and they're planning more.
33:43But I think what's been the coolest thing for us, as, as the builders of this, is to hear that in multiple organizations, we've actually had the end user, the consumer of these goods, of the finished products that our customers are making, say we've noticed higher quality and consistency due to having better structured data. Well, I'm putting those words in their mouth.
34:05They don't know. It's, it's a result of better structured data. But that's the end results that we see that our customers get to experience, is literally imagine a university setting, which we've heard this multiple times of, folks patrons adopting galley and then having their students come and ask the question, what has changed in your culinary operations? that it has improved the quality of food and my overall dining experience.
34:31And so I just, I, that's been, I think the, the big aha moment was you don't even understand the results that well structured data can have, not only to your bottom line, but to overall to your customer experience as well. And so that's, that's been the validation that's just made this just all the more worthwhile to work on and to bring to market Menu planner has been like this kind holy grail thing that a lot of companies have been asking for.
35:00Just put a point on it. our friend Marcus, chief customer officer came from industry trying to manipulate, different versions of spreadsheets and things in order to execute on this kind of stuff, and now it's something that's native in the technology and we're actively working with, some customers to get it rolled out. the other thing that we've been working on very collaboratively to produce is this concept of a production planner, which, producing goods isn't re revolutionary, but, building a system that can leverage where you manage your recipes and how you engage in all these different parts of food service.
35:39Maybe you're a caterer, maybe you're something a little non-traditional, right? Like our friends at Foxtrot in Chicago, right? They do a lot of really nice premium grab and go type items. Is it similar, right? We had to get the data right before we could really execute on this. Is that a fair way to look at that? And spoilers kids out in the audience, were gonna give you the opportunity to get a sneak peek at this year.
36:03But what's got you really excited about production planner? Fergie Production planner, I think for me was almost like an epiphany of what could be the smartest, most intelligent piece of the operating system. having all this great data and having the recipes as the cornerstone was, I think, necessary. But in the planning phase of what you're gonna do each week is, where the rubber, meets the road and turning all that into a, an executable plan.
36:36So, being able to look at all the recipes that are gonna be produced in a period of time, from many different menus potentially throughout the week and turning that into the most efficient plan possible, taking into consideration the all the data points that we had at our disposal that were defined in recipes and sub recipes and, the more engineered your recipes are to be following our data model of not just a recipe, we're, we're, we're all about reusable recipes, right?
37:07Reusable, mixes and spice pouch for this that can be used here and there. And, all these things are subres and this recursive data model of recipes, which can't be done unless really have this data model. We can dissect that, look through that entire plan of all the recipes you are planning to produce in your menus each week, and walk them backwards and say, what needs to be done first?
37:32How much, knowing the shelf life of what you're gonna produce, how long to chop onions last on the shelf? If we chop onions on Monday, and certainly we need chop onions every day, but what if we chop this many onions on Monday, and then we don't chop on this Tuesday because we can chop enough for Monday and Tuesday, but there's a two day shelf life, so let's chop them more on Wednesday.
37:51And then how much right? Do we need? Just that simple example. Take that into your most complicated dish that you wanna serve, and you might need to back up two weeks to make sure if you're, we have a customer that's like, Hey, we have this Turkey spread for Thanksgiving. We do at the school every year. We buy fresh Turkey, we marinade, we, well, we thaw.
38:12There's prep time for thawing. That's a sub recipe. We marinade. That's another sub recipe. These take, 24 hours, another 24 hours or more, so there's the brining, there's the, all of these things, and then there's the roasting, the cooling, da, and finally we get to slice some Turkey and put it on a sandwich. We're now able to back that up, right?
38:32And make sure that looks like a true two week processor, a week and a half process instead of, Hey, we need some Turkey on Friday. And in one person's head, they're like, wow, I guess I better start a week ago. or I better start that. And I have to be smart enough to keep that tracked while I'm helping with inventory, while I'm doing everything else.
38:52And that cognitive load is a lot of pain, right? And we talk a lot at Galley about, improving people's lives. And the epiphany for me was, man, could I reduce the stress by just saying, we've got that, we've got you. we're, we're understanding that pain point and we're putting it in data. We're putting it in process to make your life better so that you can go home at night, enjoy the kids, come back tomorrow, and the tasks are already laid out.
39:18That's, that's what it is for me. I love that. Well, as soon as we can bake in, solving for that late truck or, that last minute order that comes in, we'll have, world dominance clearly in our pre in our purview. But I love that we're thinking about it in this capacity. Questions, comments from the audience, please send 'em in the q and a box.
39:39We're about to wrap things up. Ian anything around menu planner, production planner you wanna finish out before we let people start making reservations? I think just my general excitement to see this, you know in the market we've just been talking to operators and like Fergie mentioned, just building this alongside some of just the most influential and high performing teams in the world. and it's just gonna be really cool to see, their efforts, our efforts made publicly available for other teams to truly optimize in a way that just hasn't been possible before.
40:11And for us as well, like Perge said, this is a, this is an evolutionary journey. So, we started four years ago, we knew that this was going to be a possibility, but the fact that we're here today and launching this feature to me is just it's a huge milestone in galley's history, but I also think in the industry at large being able to, manage this type of planning around production and menu cycles is gonna be a, an absolute game changer for the industry.
40:38So just really excited that this is, this is finally gonna be commercially available, Taking over the world one recipe at a time. I love it. so what we wanted to do we haven't socialized this broadly, but we put up a nice little reservation page in the spirit of, making a reservation at a restaurant. We want you guys to get on the list now.
41:03We have customers working with us collaboratively to put the final bake, pun intended on these features. and we wanna start inviting the rest of the world in. So we wanted folks who are spending time with us to learn and who have been to a webinar too enjoy our blogs and, the newsletters. Like we want you, our friends out in the food service world to get those first sneak peeks into this.
41:28So we invite you openly to get yourself on the list. and what'll happen is we will reach out to you directly and schedule some personalized demonstration of these platforms. You're not necessarily committing to both things, menu cycle or rather menu planner and production planner, but through the process, we just wanna learn more about, what are your needs and how can we offer up some solutions?
41:49And spots are going fast, man. So I did see one question out here that I think we can kind of answer. So there was a question about a feedback loop, and I think this is pointed more at the production planner for overproduction and food waste, something we've thought about. I think Matt, what what say you to tracking waste and overproduction? Yeah, it's something we're really keen on.
42:13It's not gonna be in the first version, but it will be coming. we just overhauled another data model of ours, which is our, what we call the perpetual inventory. And we just did a we really thought about how we're gonna do tracking waste as one of our key items that we want to add. and we wanna do it using perpetual, and I'm kind of getting into the weeds here.
42:37But we have a great data model in mind. This has been really thought through. and it's, it's gonna be in this next gen of these first of these pieces. So as you're producing and you either drop something on the floor or you overproduce and it's not used, then you can either track it as waste and we are working with partners like LeanPath as well to help make that possible.
43:03Ian I'll put you on the spot here a little bit. piggybacking off of that, tell us a little bit for the uninitiated about like the cool foods movement and what people are thinking about when it comes to waste tracking and understanding carbon footprints and stuff. There's initiatives out there, right? we're not solving for this completely yet, but we're working with those organizations, isn't that right?
43:24Yeah, absolutely. there's some great organizations that are just capturing all the metadata associated with food wastage in general. So understanding, CO2 contributions, things like that as well. So the beauty of galley, and I think our open data model is just our ability to really integrate with other systems readily and leverage that data. So we've got big plans like Fergie said, to leverage integration partners to I would say enhance our data set around food waste so that we can bring the most sort of impactful data set to our customers, ultimately because our customers are demanding it.
44:00But our customers are also very interested. there's a huge movement that's happening right now to just understand more granularly your food, and that entails, not only the nutritionals, but how it was produced and really the story behind why it was produced and the impact that it's having that data is not, only being demanded for compliance by operators that have maybe sustainability initiatives, but by the people actually consuming food.
44:27So the end consumer is really driving this and we're excited to make that information available in future iterations. Follow up question. So I hope that answers anonymous users question. Our friend Jason out in the audience here asks, what's the cost? I'm gonna give a kg answer, kind of depends on all the different things that you got going on with us. Probably gonna look at some things like overall spend with us and the locations, number of locations that you're producing out of.
44:56But, I'd be doing everybody a disservice if we gave you like a one size fits all answer. we're, we're solving some enterprise problems here, so that's part of that reservation system. We're definitely gonna take a look at everything and make sure that we're being reasonable with what we ask you to pay for these things. We do have standard pricing, but most of implementations take a discovery of scoping to understand the full configuration of the package and therefore the ultimate cost.
45:21So, yep, I think you nailed that. And then I saw a follow on with, maybe we can look at connecting our system. Absolutely. Talk about that during a discovery call. We have a number of ways to connect into other data models, other systems and things like that. So we always like to try and understand the use case around, what you're trying to connect and why, and then, and then we sort of solution that with you.
45:41So happy to have that conversation. I would encourage you to sign up for a reservation and and schedule a call and we can absolutely dive into that. And then also, just to put a little cherry on top, I'm sure this will get everybody's fingers moving quickly, but I went into the marketing closet and I found things like cookbooks, I've got aprons, I've got all sorts of fun things that we can give away for all of our friends that sign up for reservations today.
46:05So I'll give it a few days. We'll give it about, you know end of next week and then whoever registers for a reservation over the next seven days or so. I'm gonna send you some fun swag. Alright well we've got nine minutes to spare. Happy to keep the lines open if there's any lingering questions, but I'm also happy to send everyone back into the wild to go and feed the masses or whatever it is you do to save the world every day.
46:30Including my friends here. Thank you so much, Matt, Ian, and we'll look forward to the next one. Take care. Cheers everybody. Take care everyone. Bye.