Transcript
hey everyone and welcome to another episode of the shipping Excellence podcast today I have Jason mocked with me who was previously a director of product at Avant a Chicago based fintech and now who specializ is specializes in helping real estate companies scale and grow their businesses using technology and data welcome Jason thanks for having me uh good to see you yeah thanks for coming so Jason is going to cover some real world examples where he's used technology including AI to scale real estate businesses from Marketing sales and operations Jason can you tell us a little bit about what you've been doing
yeah sure so you know I took an interest in in really everything to do with AI you know back in the early days of chpt um it kind of started out with uh github's pilot and at that time you know had a team of Engineers and I I was really interested in I I was able to see just through chat gbt the the power of like what it could actually solve and do and uh I knew immediately it's like man we need to find ways to get our team using this and my mindset especially when you're dealing with engineering teams you know any incremental Improvement those are some of your most expensive resources right so if I can improve the velocity of my pod and get them solving problems faster through leveraging some of these tools I was like that's a that's an obvious win like how do we how do we do more of that
and so that's kind of where it started for me um this last year I had the opportunity to work for a couple Real Estate Investors um predominantly in the um wholesale space and you know that's largely a sort of like marketing and sales operation and um you know the the the thing I love about working with you know smaller teams and like startups is you have the ability to kind of like build create prototype iterate IDE on idea as quickly and uh it was kind of a a a ripe ground for me to to sort of uh play with some of these different tools and um you know my main focus was building out like the teken um data for uh powering a lot of our marketing efforts but throughout that you know you start to notice things like huh I think that could be a good use case for some of these new AI models that are coming out
and and the really fun thing for the last several years really has just been the speed at which there's been ad advances in the power of these models uh and it's like one week you might have an idea like a it's not quite there like literally the next month it's like oh you can solve that problem uh with a high degree of certainty now and so um you know I guess one example that that um that that I really enjoyed working on was uh in the real estate space there's this this concept of uh it's literally just called driving for dollars so the idea is you you drive around neighborhoods uh and you know literally some of these companies will pay people people to drive around and say yeah that house looks like a good fixer upper that house could be one and they just make a list um at the end of the day they submit their list and you know generally you're trying to reach out to the seller to see if they're interested in selling their house so you can buy it fix it up and and sell it
um so that's a real thing uh and that eventually evolved to what's known as like virtual drawing for dollars and so there's actually software out there with the full intent to you know hire virtual assistants to go through Google street view images and they're literally just clicking through looking at Google Street View images and they they just make note of the properties and so as I got involved with the business and I saw this uh my first thought was like why couldn't we just have a vision model to this seems way more efficient than you know this team of five virtual assistants we paying like $8 an hour whatever we were paying them um to go do this thing
and so uh my my first thought was like okay how do I go build this thing and create a prototype and you know I think the the combination I think as like the tech and the space has evolved in the last number of years the power of some of these like low code or like no code Solutions and then you pair that with um the apis available through a lot of these models allow you to really build and prototype an idea pretty quickly and so that's what I did I took um I decided to essentially take a list of properties all the same properties in a given area that that we were interested in the same type of map you would draw to like give to one of these uh uh virtual assistants to to drive through um we pulled all the addresses I fed those through Google Street View to get the images for each of the properties and then I fed those one by one through the model and gave it a essentially a custom prompt to identify you know what is the quality of the house is it a high-end new construction luxury home okay that's 100 I don't want that one does it look like the you know the roof is falling off of the house and and it it needs you know it needs some um uh you know it needs a rehabber to come in there and fix it up give that like you know a 20 right
so I came up with a rubric to have it score it for us on automated fashion um the nice thing is you can also iterate and test on these really quickly right so I would test variations of the the prompting the instruction that I I would give it to get better outputs and then you have this sort of like you know human in the loop aspect where you reinforce the learning I wasn't really doing reinforcement learning but I was able to iterate on the prompting to get better outputs and uh and that resulted in us you know running um you know 10 or 15,000 records to this that we then used
and this is kind of the Crux of it the reason we are interested in doing that is because uh in the context of marketing you need to make sure you're getting a good return on your spend and so Direct Mail is a is a big channel in the real estate sector where uh you know you're going to mass Market people that you think you have a good chance of maybe buying their house so you can renovate it um we were isolating segments that we knew weren't likely to respond and so I needed to find the needle and the Hy stack I needed another signal in the data to tell me that there might be a reason why they might want to sell and this was my signal
and so by scoring them taking that full list filtering that down to call it you know a score of 30 or below that is the subset that I on a mail and ideally get a higher response so thereby my return on spend for that campaign should be higher and and as we found was higher so just to give you an example you know we would we would normally see maybe a tenth of a percent response rate on a campaign um and after we we ran a test of this we saw 3 or 4X uh the response rate relative to the population we're mailing at 10 basis points you're probably going to be break even on the campaign at three or 4X that you're going to be profitable and that becomes a viable solution for the for the business
and so um that's just like one example of you know I think the the the the ways I think about leveraging AI in the business is you find these sort of like micro optimizations that you can sort of double down on and and get uh I don't know if you'd call that an edge but it's it's the type of thing that not everybody's doing and if I can find ways to differentiate it myself leveraging some of these tools tools um I think there's a tremendous amount of power there and um and you just kind of you know take this laser focus on on those areas where you can get the most leverage
I think I would certainly call that an edge because to your point not all businesses are doing that right and so if you're you know your clients your businesses are employing some of these techniques they're more profitable right they're they're return on investment for you their spend is much more efficient right and so that gives them a lot more Runway to you know do additional marketing efforts or improve the service that they're providing or could find additional homes right so scale their business and so I think that certainly gives them an edge when they're trying to grow their business right any anytime you can make a business run more efficiently it frees up you know Capital whether that be know a person's time or or the the money that you have to spend in other areas of business so I'd call it an edge and I think that's really cool you know so take note real estate companies this you should be doing some form of this
U but I got a question so that makes sense for I think the would be the real estate you know company so like the institutional real estate investor right and then the you know the individual Real Estate Investors who maybe have you know larger portfolios or doing some kind of volume right so it's not like the you know probably not that effective for like the person who's buying one or two homes a year right that's right makes sense at scale what what about like and and or give me your thoughts do you think this could be applied to to people who are looking to buy their next home like is it could they and when I asked this it's not like they need a they would build a product to do this but the the Zillow of the world who are selling you know ad space and are platforms for people to sell homes is there some kind of application for this type of technology to help the consumer right there will be value add to their business
yeah I think it depends like my my particular use case was really geared towards kind of a a a niche right and that Niche is you know wholesale or Fix and Flip real estate um and and the thing that they're trying to solve for is like how do I improve uh my return on spend um yeah in the consumer space or like you know retail home buyer you know I think it's harder to say um you know this is the kind of thing I think makes sense that like you know uh at scale um when you're doing kind of a marketing effort um not sure how it apply um to like that that you know someone looking for a home let's say the other the other challenge too is you know things like you're you're only as good as the data that you have available to you right so even something like Google street view images uh if you notice you'll go in there it might be like from like 2019 and if you like drive by that today like the house might be knocked down right so you know there's there's this like recency um aspect of like for on a on an individual data or like home uh uh perspective you know it might not be the most recent data but at scale you're going to have a larger number of occurrences where where it kind of like fits that uh I think really well
yeah so I know another thing we talked about previously was um you how this applies to like sales and you know lead motivation scoring versus you know New Leads versus existing leads and I found that particularly interesting which is why I wanted to talk to you today here can you tell us a little bit about that
yeah so i' I've come across this problem a number of times um and especially like any sales organization I've seen a a lot with you know my clients now where the problem is kind of around how do I know which leads are the ones I should be spending my time on or or the the sales team should be spending their time on and there's always this kind of like push and pull with most organizations where there's obviously a big emphasis on marketing how do I get new leads in the system and how do I you know keep on moving but once you get in the system then the next question is like then what happens usually there's like a very you know there's a sales process and a sales cycle and there's like this early stage filtering like hot lead warm lead cold lead long-term followup Dead lead right
once it get pass there it's it's usually an abyss right it's like a black box and these leads no one knows where they go they're somewhere in the CRM and you know the the pattern I've seen is yeah there's um some instrumentation in place to do follow-ups you know the the the mo more sophisticated operators will have um really dialed in follow-up processes and segmentation and automated drip campaigns and automated EMA uh emails that go out and SMS and direct mail that might do a follow-up touch but in the vast majority of the cases um where that's not happening it's kind of the sales team or the lead manager's uh uh responsibility to you know randomly reach out or call them or text them
so the question then becomes how do I prioritize if you have you know a team of five and you have you know thousands or tens of thousands of leads in the system uh how do you actually like sift through the noise and and again the same idea is like how do I find the signal because at the end of the day you want output you want results you want revenue and you don't want to waste time
and so the same sort of aha moment came like hey like how can I take all this data uh and and and do something with it using these models to provide that signal and I think that's one of the like really powerful things with um these language models is you have this ability to take unstructured data and transform it into structured data that then can tell you something you can act on
and so the the idea that I had was what if we were just able to suck in all of the account notes for all of the leads in the entire CRM and then what if I could just feed that into a model and the same concept as this um this Vision model use case was I'll just have it score on the particular characteristics of the conversation did it check all the boxes of things that we were looking for did the lead have motivation and can I tell tell that from the notes did they articulate a price they're looking for do they have a timeline that they're on and through sort of the combination of those elements I can then have it provide a score provide a summary of all those elements and I can actually have it extract the information from uh from that so I can get the most relevant pieces of information that I that I need to know to make a decision
and at the end of that provide a score that I can then essentially feed back to the team and then once you have that you essentially have your Forest ranked list this is the Thousand leads in your system ranked by motivation score however you would adapt that for your particular business and it's like start here and work your way down and it takes sort of the guesswork of who to follow up with or you know uh it becomes less process based like okay we're going to follow up in 30 days it's like no these guys were ready to go next week they're at the top of the list you need to work your way from the top down and you know again the the the mindset there being is like how do I how do I take a vast amount of data pair that down extract the essence of the most important things and then use that to really focus the team and the the efforts and energy
you know what's interesting about that is I think this is the common misconception is like people think the data needs to be structured and we went from you know relational database systems you know to decade you know 15 years ago two decades ago to no SQL databases so non- relation to what we have now and you know like non relational databases were still structured data right you had Json blobs and docents like you still had some some kind of containerization and structure to them but now you can take literally just just any kind of data just notes you know pictures sound you can take that data and run it through some kind of agentic workflow process and derive meaningful value from it
and so it sounds like that's what you guys did you know you've got the you got sales reps on calls talking to prospects and Le and they're collecting all this information and even even like that human part of the process can be errone they don't necessarily write those notes down uh or maybe they forget a note to include but it sounds like you took the notes and some of the additional information that's a part of that CRM to rank these leads by the ones that need to be touched first and and and most often um you know I could see a world where you take sales calls and feed that into that process too or or you know through a different agent right different yeah agent but for the overall workflow and get additional uh meaning meaning out of it
absolutely and and we also did that so oh you did do that okay good to know good to know yeah like big picture is yeah I think you you know AI in my mind is is it's not a silver bullet right and it's not it's uh it is kind of a Swiss army knife but it it's just like a tool in the toolkit and I think if you spend enough time with it you start to understand the capabilities and the gap you start to understand which models apply to which use cases and which ones are stronger in different domains right I tend to gravitate towards open AI model Suite um there's you know anthropic claw uh some people like that for coding more there's all these sort of different flavors that you can um interact with and I think just spending some time with it you can then start to understand what are the capabilities and then it's like when you come across a problem whether it's a you know business pro problem or operational problem or whatever it might be you can then sort of like huh I think I think I could do something with that here how might I do that
and and you now have sort of the freedom to experiment with these things and I I think the the other thing I would add to that is the AI is also sort of like your you know pocket engineer or like your pocket data analyst or and you might not be familiar with a particular platform but you can rely it to like get to the thing that you're trying to do faster and get to An Answer faster and so it helps not only on the solutioning but also like through the iteration of trying to solution it and actually code the thing or you know uh whatever the the application might be
yeah are you finding the the ideas for these applications are coming from your head coming from the business owner head is it a combination of discussions you know with you know people in the business and yourself uh and your expertise with technology how are you seeing how are you finding discovering the ideas and the applications of the this technology
yeah I think it depends uh what's the what's that like I can visualize as like bell curve there like the early adopter and then there's like everyone's doing it then it's like you know at the end of that nobody cares kind of thing the late adopters um yeah I think it just depends on where on that Spectrum you are like I happen to be like one of the earlier adopter um I I would say and and so I and I was just very curious on like exploring kind of the the boundaries of some of these things and so that in my experience generates a lot of ideas um what I've found so far is people then like react to those ideas and they're like oh that's that's great like how do I do that in my business or how how can we leverage that and that that starts um the conversation um I'm trying to think if I've if I've come across um some some other applications of uh ideas that people brought to me but you know at some people at some point people have lot of the same ideas and you'll see see some of the commonalities and um and that sort of points you in the right direction of like oh I should like um zero in on that a little bit more and and try to figure out you know how do I solve that problem
yeah 100% um okay so we covered sales and marketing and I want to I want to talk a little bit about how you've seen this this is where this is the space that I like to play in it's like operational efficiency which I guess you know technically the you know drive for dollars EX example is is operational efficiency But ultimately to drive sales um let's talk a little bit about like you know the operational efficiency you've made with like QA QA call monitoring
yeah yeah so bigger picture here I mean a lot of what people have talked about and I think what you see a lot of the headlines on is you know on the on the uh call center side right it's like chat Bots it's like AI like phone agents um and replacing kind of those uh those I don't want to call them lowlevel but like you know kind of like the entry level type rules of you know support specialist and how do we leverage basically how do you get information to the end user or customer faster uh and and the way I think about it is like upskilling the people that are taking those calls now to have focus on like more complex problems
and so you know I think on the operation side there's a ton of like already in production applications of this and like big companies have already uh repositioned a lot of their teams that are handling you know low-level questions like back in my credit card days um you'd be amazed at how many people just call in to like check their balance right or uh know when their next payment is it's like look we built an entire web UI just so you could find this out people are still calling in so how do you deflect that right because there's a there's a a a large cost to to support that
um but so taking that a step further even on like the operations side um call Quality Control is an important thing to a lot of businesses right like how do I uh you know how do I know that we're answering the phones the right way how do I know that we're answering questions the right way how do I know we're presenting the the company and the brand in the right way based on the way we as a brand we want to be you know representing ourselves and talking to to people um and the same goes true for on the sales side right on the sales process how do I know we're having powerful conversations with our prospects and increasing the likelihood that that's going to convert into a sale or into a transaction right
and what I've seen in a lot of businesses like that's like the unknown that you never really have a it's hard to hard to like fully grasp right like you train and you hire and you coach and you you do everything in your power to get the best results but it's hard to do that um sort of with any degree of certainty other than measuring the the outputs right but if you're not getting the outputs that you expecting what what where is the problem like how do you how do you pinpoint that
and so um again the the the we saw the same problem the question was like okay how do we fix this like how do we how do we answer that and um you know at the time yeah I think like one of the transcription models was uh just coming out and getting much better and and the costs were reducing I was like we could easily trans describe all of our calls same Concepts give it a rubric and the the things that we're looking for to extract it same idea again you're taking unstructured data you're taking call recordings of a conversation that you know can tend to go all over the place they could go on for you know dozens of minutes if not hours and the Manpower you would need to actually like manually review and score these is a lot you can't you can't get 100% coverage right
and um and then it's even it's even hard to cherry-pick the calls that you think you're going to get the most out of from like a um uh you know just a observation perspective like a coaching and training perspective and so that's that that was the Genesis of like okay how do we how do we get more out of this and so uh ended up standing up a a service to essentially take all the the call recordings which you can most cases like if you're using like a twilio or many of these these platforms you can connect direct to the API a lot of them are already transcribing it so maybe you don't even need that some of them are even maybe scoring it and there's there are a number of solutions out there um that would do a lot of this for you
but the idea was like take that transcription run it through our own internal sort of uh QA scoring rubric and then that would enable us to then a score it like was it a sales call was it a information gathering uh screening call was it a hey like uh you got the wrong number stop mailing me that type of call you could then sort of easily tag and um categorize them which is largely what some of these AI models were designed to do uh so it solves that problem and then you score it against your own sort of rubric on uh this is what a good call looks like this is what a bad call looks like
and then that becomes uh it it it takes thousands of calls and reduces it down to hey here's your top you know 10% of the best calls that you can use for training of like hey here's what a good call looks like here's the bottom 10% um that you can learn from and either do training on an individual level or or with a team to help them just improve their overall skill set what when they're on the phone with uh with with um prospects
and so uh again that was the idea of like you know if I wanted to really tap into that and use the call data to inform uh training and the overall operations of either the sales team or either customer support team um you can leverage these models to to help provide that insight and so that was sort of the the application that we used
where do you so I saw a commercial I think on LinkedIn recently and it was of a you know AI callbot right so the AI was doing the conversation it was responding to the human um and and it was prompted or designed to you know collect information right through through a process right where do you think we are you know I guess in terms of number of months or years we got to talk of months because it's evolved so quickly where you don't need necessarily the solution of call monitoring because you can just automate the call itself
yeah yeah yeah totally fair point um yeah I mean at some point you could see sort of on the Spectrum like that goes away altogether and becomes irrelevant you know I think where we're at right now is that these AI agents the call agents in that are are are like now just now getting to the point like it the latency is low open AI just came out with a real-time API it's actually I demoed it and tested it it's actually really good um it feels more natural so I think there's kind of like two things that need to happen first of all I don't think it's going to happen overnight where like everything just becomes Ai call agents right I think there's um there's kind of like two Dimensions like one is like the level of complexity and Nuance of the call and the interaction at the end of the day we're still humans and we still need some element of human interaction uh through what whatever we're doing I I believe right like I don't think that that that doesn't go away if it does I think we have bigger problems on our on our hand
and so I think in a sales process or or whatever it may be or even just like Consulting and there there's still this element of like you're dealing with more complex problems um and and that you know that that needs the individual um where I think these AI agents fall fit in is kind of like the rudimentary kind of like everyday stuff of like you know I'm calling a restaurant because uh they don't have online booking and they only take phone calls but like you know at least I can do that with a AI agent actually I think Google launched that well they'll do that on your behalf which is pretty awesome um or or just even like the the stuff I talked about before where people are calling to check their balance like I don't know that I need to talk to a person to do that uh and you can solve that problem with an AI agent like the complexity of the problem is um you know reduced enough where that that fills in that purpose
and you know it's kind of like this transitional phase I think that we would be in where like that's that is the medium and that is like the interaction um but I I do think they are the the the agents are getting good uh and and frankly like I don't mind it and so I think that's the other dimension of this is sort of like the cultural adaptation or like exception of um of of using these and interfacing them with with them on a regular basis that's kind of like the new thing and I think that will transition right now it's kind of like a little weird and maybe off-putting at some point I think that perception changes so like oh yeah like that's just how it is right and you get more accustomed to um to talking to an AI and it's it's you wouldn't think twice about it
uh I came across an example recently I I like almost never have fast food but I I went through and and the drive-thru was Ai and it was human assisted Ai and I was like this makes a ton of sense like not to say like you want to replace all the you know people with you know fast food restaurant jobs but um it you know it takes the order reads through the screen like that that process is pretty basic the me the menu is pretty fixed it's like yes no add that take that off and I was like this is an amazing application I was amazed that the companies were already adopting at that level um and then you know something goes wrong there's someone there to like intervene and and course correct um I think you're going to see more and more of that embedded in different areas and at some point it's just going to be become broadly accepted um so I think we're we're there now already early adoption phase I think you'll see more and more of that and I think it'll just become status quo it's become normal
yeah I agree I um fun fact I worked this is you know not too long ago but long enough ago I I worked on automating a faster restaurant ordering system using AI is it really cool you might have built that one it was all secret you know it was really cool um and then you know you know even with the call automation using AI I still think it's a part of like an overall agentic workflow where you've got maybe one AI you know taking the call you've got another one listening in on the call to score it and then you still ultimately I don't think we're ever out of the world at least for the next decade where there's some human in the loop so ultimately you've got to have a human kind of looking at that cuz again humans own own businesses and humans want the businesses to operate well and to provide that service
so you know I think for for a while everyone was worried that all the software Engineers jobs are going to be replaced and I was like you know like I think I mean I use AI you know in encoding and I think you it's certainly a augmenting the work but I think there's still like really high level stuff that AI can't do right like there's it can do architecture even now but it can't design system to purpose fit the business problem so you still need humans for that
so yeah it's it's just like a it's a lever right and it provides leverage right so with uh a little less effort you get way more output and that's kind of how I I think of it in the context of engineering and and code development or writing right like I I can uh I I've never personally loved writing extensively um but you can you can have an idea and you know use it to outline that and provide more structure and then fill in the detail and then you can kind of be the editor right so like these applications of like Aid driven content creation right it's just another lever where I can get more output faster with similar or even better results like I think some of these AI write better than I probably could so so it's uh it's super helpful
in the context of coding right it's like if I can if I can just like mitigate my my bug rate right my air rate Ju Just by a little bit even just by have it like seeing what I'm doing in the in the background and and calling that out the amount of time you save like how much time have you spent debugging code and it's like you're scratching your head trying to figure out what happened so like the these like optimizations of different areas where you can embed um these AI tools it's like a compounding effect and everything you're doing whether it's whether it's in the business and it's all the things we talked about it's like uh in optimization on the marketing side and optimization on the sales side then uh optimization on the operational side it's like the the multiplication of those optimizations across the business is massive and I think if you're if you're thinking about AI in that way and integrating into your business that way you can have much better results and returns and ultimately I think that's like what everyone's after right and so that's kind of how I think about injecting this into a given business
100% I mean for me it's the cognitive load that it reduces like as I'm there's so many things to do when you're running a business and like I can't keep it all in my head and and then you know when I need to do something very tactical like if I don't have to my brain doesn't have to work that hard to execute on the Tactical thing and a I can help me and it's you know at at a minimum as good as I would do on my own um you know ideally it's better because it's the machine doing it right um if it can do that then I'm 100% going I'm going to pay for that you know and I do pay for cursor I pay for GPT I pay for claw like I use Gemini 20 came out by the way have you have you got a chance to play with it I heard it's like amazing
I've heard good things I I've not for whatever reason though I was early stage I was a little turned off by Gemini Gemini because I I I played with it and I just like I was like I didn't quite get it uh the outputs I was looking for just wasn't quite comprehensive enough I did hear it the new one was good I have used it um you know what I love and I think that the edge that you know Google has is like I I don't know about you but for me I use almost all Google products and the fact that it's like natively already there and it's just like hey like use me like it's been kind of begging for adoption for some time it's always right there at the top of the bar it's like hey try this right now but it's actually getting really good and they've actually uh made leaps and bounds in terms of like how it interfaces with their existing things whether it's like Google Docs or uh I was recently doing some work in in collab which is their like python Jupiter notebook right and it's right there and you can interact with it and knows everything that's kind of the contextual stuff of what you're working on and so that's actually pretty big
because the world before which when I say world that was like you know last month but you'd have to like copy paste into like chaty BT or one of these other things and iterate there and you be back and forth so like where I see this is going is you'll you'll continue to have this like deeper integration into the workspaces the places you're already doing your work whether it's you know Google Docs Google Sheets things of that nature you mentioned cursor so like you have like the ID and it's everything that was sort of built with AI and binds in the in the actual um coding environment um you're starting to see that chaty BT is coming out with their they just had like the 12 days of uh of uh CH openi or whatever they called it um where they're they're trying to come out with features where it's more of that embedded uh experience right they're they're coming out with projects and you can like upload all the context of um whatever it is you're working on whether it's more of a you know uh writing type of application or if it's you know you're actually coding and I've seen people like upload entire uh code bases entire repositories into it and then they use it to like interact with the code and actually like re re-engineer features within it
so I think you're starting to see these more integrated experiences where you can do more with within each of them my mindset right now is I'm sort of agnostic to which one I'm using I think it's all about sort of the the use case and uh what wherever it is that I'm working and whatever works best with that that particular interaction
same I agreed I use them all except for Gemini which I am now going to start using and I never I never bought it for Google I use I'm a Google guy too I use Google products I never bought it for my Google workspace because you know bad taste the first time around they just they just missed and you know I think the the good thing is gole Google has all of the power of Google behind it and so when they saw what happened they were like oh it's time to play game and so they yeah game on exactly and so I you know I'm I'm excited I'm going to buy it for my our workspace now and you know hopefully I get to receive the same productivity gains that I get when I'm just you know when I was doing the copy and paste but now it's just kind of embedded so yeah
yeah no it's it's great so I'm I'm excited to see where um all of it kind of arrives and it it's been fun to watch it evolve yeah it has well Jason man this was awesome I really enjoyed the conversation we you talked about some like very tangible very real world examples of how people are using technology data and in particularly AI which is what this show is about and so thanks for coming it was absolute pleasure to have you yeah thanks for having me man this was awesome yeah man have a good one see you