Doorify Real Estate Podcast

Sidekick Is Launching on the Doorify Dashboard with Michael Martin and Eugene Pak

• Doorify MLS • Episode 94

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Every agent’s workflow is packed with tools, tabs, and to-dos. What if one assistant could pull it all together and actually help move things forward?

In this episode, I’m joined by Michael Martin and Eugene Pak from Sidekick, the AI-powered assistant that’s about to launch right inside the Doorify dashboard. 

Michael and Eugene break down how Sidekick was built, who it’s really for, and why it works better than a generic chatbot. We talk through how Sidekick interprets goals, pulls from MLS data, and helps with tasks like pricing conversations and follow-up messages.

We also talk about AI habits, market reports, and the challenge of helping agents ask better questions. And we preview what Doorify users can expect once Sidekick goes live in December.

Listen now to hear how Sidekick fits into your day and why it’s worth exploring when it hits the Doorify dashboard.

Specifically, this episode highlights the following themes: 

  • What Sidekick actually does and how agents are using it
  • Why MLS data feels different when used in conversation
  • How AI can reduce busywork without removing the human side of real estate

Links from this episode:

1ae5b43598204883b524f061d4880e6d10fca88c (for podfollow.com)

Allan Nielsen [00:00:08]:
Well, hello everybody and welcome to the Doorify podcast. We're here today with the Sidekick with Michael and Eugene. So welcome, guys.

Michael Martin  [00:00:18]:
Thank you. Thanks for having us.

Eugene Pak [00:00:19]:
Thank you.

Allan Nielsen [00:00:20]:
And welcome to you as well, Matt, of course. So we're excited to have you guys on here. We are not that far from launching Sidekick on our dashboard and we can't wait for that to happen. But let's start out the right way here and maybe give you guys a chance to tell us about yourselves. So Michael or Eugene? I don't know. Who wants to go first?

Michael Martin  [00:00:42]:
Sure, yeah. So I'm Michael Martin. I'm the co founder and CEO of Sidekick. Been working within the real estate technology space now for about six years, and we officially launched Sidekick this year. I'm excited to share more about it.

Eugene Pak [00:00:54]:
My name is Eugene Pak again and I'm the partnerships lead. I've been a realtor for over 25 years. I'm also, as I say, a recovering manager. So very familiar with this space. And you know, again, why I love working with Michael and the rest of the team, especially on this product, is, you know, what I always really enjoy, or what we actually really enjoy doing, is really trying to help agents and brokers, you know, to do their business better. And we feel like we've really stepped into something where we're like, this would be a great way to do it.

Allan Nielsen [00:01:21]:
Matt, we didn't introduce ourselves at the beginning either. Do you want to.

Matt Fowler [00:01:24]:
Hey, everybody, again. Matt Fowler, CEO at Dori. And I wanted to kick us off if I could. Eugene, we got to go to dinner a few months ago and we're about to launch, as Allan just said, the Sidekick tile on the dashboard. So Dorify users will be able to come and try Sidekick and sign up for Sidekick actually through the dashboard. We make it really easy for you to do that. We've worked with these guys to optimize the data in the back end. So you can trust that it's described on the website there, Michael, as a virtual assistant.

Matt Fowler [00:01:55]:
An AI virtual assistant. So I'll let you guys describe that. But so from a tile on the dashboard to where it began in the very beginning, you said it just came out this year. Tell us the origin story and then we'll get to that tile on the dashboard and what that virtual assistant thing means.

Michael Martin  [00:02:14]:
Sure. I'll try to keep the origin story relatively short. It's always probably the most interesting person to the person telling it. But in 2019, my co founder and I started a tech enabled real estate brokerage in The Bay area, called Avenue 8, which scaled to at its peak, about 500 agents across LA, San Francisco, NY and in Florida. We had been building technology for our agents along the way. We actually were very early playing with versions of what became ChatGPT. It didn't have a name back then, it was a developer sandbox. And you pick a model called DaVinci and you would control the heat and the temperature.

Michael Martin  [00:02:47]:
And I remember we were copying and pasting, listing descriptions into a text box and asking questions, questions about the information and you just really couldn't use it at all. In early 23, when ChatGPT first became commercialized, it was no surprise to us that it was sweeping the real estate industry by storm in November of 23 when GPTs became a thing which was effectively like a off the shelf front end that you could at the time connect like one API to or upload like one document to. And it could kind of perform these sequential assistant type actions. And so that very day we told the team, hey, stop what you're doing, we connected a GPT front end to our data fabric and called it Sidekick as the industry's first AI assistant. And really it was just for our agents and the feedback was really positive. And then we started to get meetings with various MLSs, with various brokerages, and we grew a lot of conviction that we could continue the mission of Avenue 8, but through a different means, which is really about building Sidekick to be a system of action that can help agents and brokerages just work faster, grow the business without necessarily having to replace a bunch of infrastructure, replace a bunch of tools. And so we made the decision last year to focus the business entirely on Sidekick. And it took time to take it from an internal only GPT front end experiment to a productionized product that we felt confident putting in the hands of the market earlier this year.

Michael Martin  [00:04:19]:
And since that time it's just been an incredible learning curve of how people are using Sidekick, how they want to be using Sidekick, how they think about their relationship with technology today, how they think about the role of MLS data. It's just been really fascinating. Of course, this is all happening at a time when every other word out of everyone's mouth and Inman or elsewhere is AI. And so I think it's such an important time to have a point of view and to take leadership and I mean, obviously hats off to Dorifi for being amongst the leading voices and sort of innovation within the industry.

Allan Nielsen [00:04:53]:
That is such an interesting story there, Michael. So with what you set out to create and the problems you set out to solve to where you are today, you were mentioning that you got a lot of feedback from agents and have interacted with them. So from where you started out with to where you are now, what changed? What has changed?

Michael Martin  [00:05:15]:
I'll give you a really real example. When we were a brokerage, we would publish market reports for our agents to send to their clients. And to do this efficiently, we created a front end visualization layer in Looker, which is a tool that Google offers. We designed it and then we had, you know, fields that were connected to a data API that could populate and refresh every month and we could define the zones. And so, for example, in Los Angeles there were probably about 30 to 40 neighborhoods that our agents cared about having market reports for. So we had a very kind of efficient system for them to toggle and generate these reports, you know, ad hoc. When we connected a GPT front end, it was now really great to say, hey, pull up the most recent West Hollywood market report and Psychic could generate that asset, pull all the data out. You could go a little bit deeper on some of the questions like how have days on market changed this month versus last month? What we realized was that the fidelity, the idea of being able to talk with your MLS data was hugely attractive.

Michael Martin  [00:06:18]:
When, when people were like what Sidekick? We said at the time, like the product has, I think, evolved. At the time it was really, imagine if ChatGPT could talk to the MLS and then do stuff that's like real estate specific. And within one second customers understood what Sidekick was. What we realized was that they didn't want to ask a question like that. They wanted to ask a question like how have three bed condos, enormous triangle south of Sunset, traded the last six months versus similar type of inventory in the Hollywood Hills. It was these very flexible, unpredictable time frames and zones and metrics. And that also oftentimes they might be asking a data question, but it might be the wrong data question. The data question is always in pursuit of something else.

Michael Martin  [00:07:04]:
You're never just pulling data for data's sake. You're pulling that question about Norman Triangle because you're working with a buyer and that buyer needs to get confidence that they can afford a condo in normal Triangle. And maybe they can't and maybe you need to give them a different area to consider. And so Psychic then evolved to being more of a reasoning partner where instead of asking Psychic a question like give me these stats, you're sort of giving it a goal based on the context of a scenario. Hey, I'm working with a buyer. This is her concern. I need some data to support a position that I think is right. But you let me know based on the market trend if that actually is the right position and let Sidekick find the number right or series of numbers that might be relevant.

Michael Martin  [00:07:47]:
And so to get from that initial step to where we are today, the amount of work we did on our data pipelines and also on the relationship between the AI and our data just became like an absolute focus of the company. And, you know, at the time, MLS data was really the only data in question. But the flexibility we've built around how we can query information is kind of data set agnostic in a sense.

Matt Fowler [00:08:17]:
Michael, I have a question for you real quick. About. Are you Gene or whoever? Like, I. I'm hearing you describe this and the interaction talking to your mls. I've experienced that. Clearly it's really compelling that you're able to just have a conversation with the data. Have you found it? Have you found agents have difficulty making that leap? Like, they haven't thought of MLS in that way. And I mean, some of it is, is you have to know that the tool can do these things to be able to access that.

Matt Fowler [00:08:49]:
Your smile.

Eugene Pak [00:08:50]:
And Eugene, what?

Matt Fowler [00:08:51]:
Have you found that?

Eugene Pak [00:08:52]:
I mean, yeah, I mean, like I said, I've been a realtor for over 25 years. This is like, really familiar to me. But, you know, mls, I think, used to be looked at as, you know, it's just a place where I, like, I fill in a series of boxes and then it just like, they don't think of it as data. Like, I think this is the other thing that I think is also changing, by the way, is how people think about information as data. Right? Because that's essentially what we're trying to do, is access data and make meaning of it. But essentially it was just like filling in a bunch of boxes. And they were mindless. And so, you know, telling people that, hey, you no longer have to, by the way, you don't have to just fill in boxes.

Eugene Pak [00:09:29]:
You can think like. And this is the. So in some ways we're all, I don't want to say going backwards, but we're kind of putting it back on them to saying, look, you're not being led by the nose here. Like, you could actually do stuff like that. Maybe those questions that are bugging you in the back of your brain, do ask. Just ask. Like, this is how we've been telling people like Michael. Right.

Eugene Pak [00:09:48]:
Like when we first started, we would do these like, demos and of Course, Michael and I, you know, we're much more engaged within. And everybody's like, wowed. And then what happens is, like, we've done. And then they go off on their own. They're like, I don't even know where to start. And so, yeah, that's what I'm talking about.

Matt Fowler [00:10:02]:
That leap. I mean, because you put it in front of somebody, a lot of people are just going to sit and stare at it.

Eugene Pak [00:10:08]:
Yeah, no, we have to retrace our steps a bit. Like, we've made a lot of assumptions. So, for example, like, even asking about an assistant, right? We were just saying, it's an assistant assistant. And then I realized as I was doing demos, I was talking. And then when I met with some of your agents, you know, doing pop tech, I realized, like, not everybody even understands how to use an assistant. So there's. So it's sort of. And this is, by the way, this is the same thinking process, like, where we're trying to tell people, just ask a question.

Eugene Pak [00:10:32]:
Or like Michael had mentioned earlier, a goal. Like, where are you trying to go? So either the beginning or the end point, right? And then let sidekick, you know, and this is the general concept of the end. Let. Let it help you get to where you're going or where you think you're going. Because here's the thing, like, he was mentioned earlier, like, maybe the goal is incorrect. Maybe it's not about, like, comparing data. Maybe it's actually just thinking about schools, let's say, right? Maybe that's really where this person wants to go. So it's like more about parallel, you know, like, it's not about the actual end.

Eugene Pak [00:11:03]:
It's just sort of the P is kind of how we're looking at it. So even for us, as we're going through this, right, Michael, like, we're kind of like, rethinking this as we go because we realized we made a lot of assumptions on our part. And what we just tell people, like, just ask a question or just say something to it, and then you start iterating. It's just like a conversation. It's, again, I hate to say them, but it's like a conversation like, hey, Matt, how you doing? Great. And I want to get to know you better. Like, my goal is to get to know you better, like, where are you from? Where are you? Whatever. But, you know, it's just a series of questions, and then it starts getting richer, and then I can trace, like, oh, wait, Matt talked about, you know, he's actually originally from, like, Huntsville Like, I actually wanted to know something about Huntsville, right.

Eugene Pak [00:11:41]:
So the path is still the same. I'm getting to know you, but it's.

Matt Fowler [00:11:44]:
Taking a step like use chat all the time. And there's a teaching method or aspect rather, you have to kind of set it up, telling what author is your favorite author and you want to come across sounding in some way, is that something you'd recommend with people starting to use Psychic.

Michael Martin  [00:12:04]:
I always anchor it to. And I think this is an important point because I think a lot of the evaluation of AI generally, certainly Sidekick, is sort of the comparison point is against all the software that's ever existed before it. Right. And most software historically were, you know, databases that had a front end and some sort of, like, predetermined set of things you could do. And it was really just around, you know, surfacing information from a database based off of the filtering or sort of workflows that were built into a system. Straightforward query, straightforward query, single path of action. Now, with AI, you're building something that is inherently probabilistic but can be made, can be harnessed to be very specific and valuable if you build the right application layer around it and if you build the right kind of context, training and engineering behind it. And so I was speaking on a panel this weekend at Haas School over at Berkeley, and the question became AI solucinate, how can you build trust at scale right.

Michael Martin  [00:13:10]:
When building an AI product? And I sort of said, I think we need to re anchor the question. Imagine computers didn't exist and you had a company of 10,000 employees. How do you build trust and work at scale in a company with 10,000 people and no computers? And the answer is, you can't. There's going to be tons of human error if you choose never to check the work, then those errors will go unchecked. And so if you compare that to imagine now you're working not only with people with computers, but with Sidekick, your error rate is actually going to go significantly down and your output level is going to go significantly up because AI is acting as, you know, albeit imperfect. It's actually like a substantial check and bAllance to the risk of human error. And so if a user is saying to me or to one of us, I don't know how to. I don't know what to ask it.

Michael Martin  [00:14:01]:
I say to them, imagine I show up on your office. It's my first day on the job. You just hired me to be your assistant. Me, Michael, for $15 an hour. And I'm like, great, let's get Started, you don't give me access to your calendar, I don't have a login to your CRM, I don't have credentials with the mls, I don't know anything about your files in dot loop. And you tell me, I don't know what to ask you. I would say, well, I don't know. What do you need help with? You know, have you thought about your business deeply enough? Do you know how to unblock yourself? And if you ask me to help you without giving me access to any of those panes of glass of information to look through, I'm not going to do a very good job.

Michael Martin  [00:14:36]:
I think what we're finding with Sidekick is that through these integrations with the MLS data, with some of the applications that are ubiquitous in the industry about different workflows, there's a gravitational pull we're feeling around. People want to be able to get answers quickly and get work done in a conversational type of way. That's all we care about, is that the user experience is simple. And I think that as More people use ChatGPT and other tools like it, there'll be a natural learning curve that gets overcome. But we're really early innings still. I mean there's. And we could talk at length about all the research that's come out around how are people using AI? How are people in real estate using AI? How are top producers vs not top producers using AI in real estate? I mean, there's. You're starting to see some really interesting segmentation emerge.

Allan Nielsen [00:15:26]:
We're so curious about to see how, how we will see agent engage with this.

Matt Fowler [00:15:31]:
Right.

Allan Nielsen [00:15:31]:
Because we are so used to. And Matt and I talk about this all the time where, you know, it used to be that an agent. Well, of course they need one of the platforms with a cma, with a search, with listing, ad, edit. We just assume that's what every licensed Realtor would need. Right, but who says that? Right. And that's what we are about to find out. And certainly from a dorify point of view, which is one of the reasons why we're excited about engaging with you guys, is that we're about to find out a little more granularly, what are the needs of the agents out there, Right. What do they actually need to do the business? So very, very, very interesting, Michael, I know you touched on this, but every time we talk about AI, hallucination comes up.

Allan Nielsen [00:16:12]:
So for our subscribers that are very curious about this, how can they feel safe that when they ask for CMA that the suggestions that they get. And all of that stuff is actually that they can trust that.

Michael Martin  [00:16:28]:
Sure. So we've built Sidekick to basically separate kind of what the AI is doing from like, kind of pulling and analyzing the data on its own, if that makes sense. Like, it's not as if in Sidekick there is a connection of the Dori data feed to ChatGPT and we're just letting you know, ChatGPT kind of work. Its wonder what's actually happening within Sidekick. And this is primarily to solve two things. One risk of hallucination and then also the sort of flexible querying we were talking about earlier is the AI is really analyzing the intent of the prompt, and not every prompt requires information from the mls. I've seen people use Sidekick to look up like cookie recipes, which I was thrilled about. And I was like, people are moving their, you know, they can lift and shift their generic ChatGPT workflow into Sidekick because it also has the real estate, you know, value out of that, probably.

Allan Nielsen [00:17:25]:
For an open house. Cookies for an open house.

Michael Martin  [00:17:27]:
Yeah, it could have been, could have been for an open house. Would have been a better problem to said this is for an open house. But if the nature of the question the person's asking involves that data Sidekicks, the AI within Sidekick is operating as like a software engineer. It starts to write its own SQL code and pass that kind of complex query to something called the psychic query engine, which takes that and then runs it against our database. And so all of the analysis and logic and pulling information has nothing to do with AI at all. And so the answer is returned back to the AI as a last step on the chain. At that point, the model is doing what it does best, which is presenting the information in interesting formats or drawing, you know, comparisons or conclusions based off of guidance and prompting, you know, from that information. And so I think compared to what's happening today, where you have a number of agents just asking ChatGPT questions about the market and it's scraping a number of portal sites and random brokerage sites and giving these answers.

Michael Martin  [00:18:32]:
Like, I would be very concerned about that, but I think the way that we've structured it does a tremendous amount to mitigate. To mitigate that.

Eugene Pak [00:18:39]:
Yeah.

Allan Nielsen [00:18:39]:
Michael.

Eugene Pak [00:18:39]:
Tad, to your point, we feel very.

Allan Nielsen [00:18:41]:
Confident in, in that, you know, the data that is being utilized from that is the authorized dorify listing data.

Eugene Pak [00:18:48]:
Right.

Allan Nielsen [00:18:49]:
Eugene, you were.

Eugene Pak [00:18:50]:
I was gonna say Allan. So to your point, like, you know, I, I don't know, we've heard this a number time and time again. You know, you have Realtors are so scared about giving away the data and yet what do they do? They actually go to your MLS or whatever and they dump everything into a spreadsheet and then they take said spreadsheet and then they feed it to their chatgpt. Right. And so, you know how I explain to people because they're worried about this. Whereas, you know, we were very cognizant of this right from the get go because again, we were a brokerage before, so we understood the, you know, the concerns and the culture around it. And so we were like, look, we just took that same application and we just applied. We're just, I mean, we're a software company, but we still have that, you know, Michael introduced us.

Eugene Pak [00:19:28]:
Right. But we still have that part of our DNA. And so we understood about like, look, we have to be, we have to act with integrity. We understand the concerns of our potential user group and such like that. And so we have to make it so that not only do they trust the AI to come out with the right answer, but they have to trust us to be good actors. Right. And so, you know, we've gone through this with obviously with you guys showing our, you know, our infrastructure and how we, you know, you know, protect the data and so on and so forth. I think that's.

Matt Fowler [00:19:56]:
And we're obviously comfortable with all that. Yeah, we use, we're obviously comfortable with that, Eugene. And for our users, we, Allan and I consider our roles as something like curating where we go through and look at legitimate companies and you know, look at where they've installed before. Talk to those MLS directors and CEOs there, do some kind of deep dive interviews with everybody who's actually using it. We do a lot of work before we invite somebody to the dashboard and we really are excited about using it. And I want to, about seeing people using it. As Allan was saying, maybe in our last couple of questions, like to hear where it's up right now for our subscribers. And also maybe you were talking a minute ago, Michael, about how people approach the software incorrectly with the bat, with wrong assumptions and not even approaching it like you would assist that.

Matt Fowler [00:20:57]:
What are some success cases, some use cases that you think people can actually understand in their own teams and businesses.

Allan Nielsen [00:21:07]:
Yeah.

Michael Martin  [00:21:07]:
So Sidekick is available now in several markets throughout California and Florida and Michigan and obviously partnering with you all will also be launching soon and some other states surrounding the Carolinas. So it's been really exciting. The second question you had was around, I wouldn't say that there's anything such as an incorrect approach I mean, it's always, it's our problem to solve, right? You can't ever blame the user for not figuring out your software. You have to meet the person where they're at. I think that's really the theme of the company is how do we always meet our customers when they're using the product where they're at. We recently launched texting with Sidekick. So instead of a ui, you can send Sidekick a text message because agents live in their texts. You know, instead of building like a bunch of, you know, email workflows, we have a Gmail integration, right? And people live in their inbox.

Michael Martin  [00:21:57]:
It's not just about sending emails through, through Gmail, it's about analyzing threads in your email and like coming up with recommended actions and messaging without having to rig up a bunch of complex, you know, systems underneath it. So I think that, you know, successful use cases, any sort of question about the market, I mean, you know, going from a blank sheet of paper to, you know, finding properties, analyzing them, creating presentations, you know, sharing out, I mean that, that entire process can, can, can be condensed 95%. And I found that for a lot of agents, having Sidekick role play with them is very useful. We had an example where an agent was nervous about having a price drop conversation with her seller. And so that was the answer to the question of like, who wants to volunteer? What are you working on? What do you need help with? And so I said, well, this is my situation. I got this property, it's been on the market longer than I would like. We need a price drop. Seller is very adamant, unrealistic.

Michael Martin  [00:22:58]:
And I got to talk to her today about it. And so we had Psychic pull up the listing. We asked it to give it some data driven arguments to support a price drop. We asked it to pretend to be the seller and to be unreasonable. And it kind of went back and forth. And you think about the what's happening there. It's the same as if you hired an assistant, a human assistant said, hey Michael, I'm nervous about this meeting coming up today. A good assistant's going to say, what are you nervous about? Let's go through it.

Matt Fowler [00:23:30]:
Fascinating. Something you didn't expect from so many.

Allan Nielsen [00:23:33]:
Use cases in this that we are not considering right off the bat. Right when we start hearing about something this absolutely fascinating.

Michael Martin  [00:23:40]:
Well, and here's the future, right? The future is you're setting it and forgetting it, right? That's really what we're building towards. We showed an example. We're working with a partner doing a really interesting Kind of CRM related integration where you know, say I think who were the most interesting leads that came in from Instagram last month and look them up online and based on what you find, find properties that they might like and write a three step personalized message and push it into the CRM and update the contacts with the additional metadata that we found Internet like their LinkedIn profile, their Instagram profile. But the what if was like well what if it just did this every time, right? What if was when a lead came in. Sidekick is ambiently running this process in the background and enriching your data and coming up with and you as the right. Because you would, if you hired an assistant, a human assistant and you always had to come to them, they would get fired. Right. A good assistant is anticipatory.

Michael Martin  [00:24:39]:
Good assistant is like hey, like I'm going to like, you know, you need this report every Monday, I'm going to get it for you every Monday. So that's really the next phase. What we're building is building sort of automations and alerts on top of the workflows that an agent, whatever the workflow is, could just set it and forget it and make it recurring or make it, you know. And I think that is really the future of how many AI systems are going to work where it's not really chatting like you might have the option to chat, but really it's just around I as an agent need to do these like hundred actions every week now Psychic can help me with 80 of these actions on its own. So how do I let Psychic know to do that and become a system of action? Maybe some of those actions live in involve, you know, opening dotloop or involve opening a CMA tool that dorify offers. And I think we're entering a world now where it's incumbent upon all of these application developers to become interoperable or.

Matt Fowler [00:25:43]:
So they can be powered by the.

Michael Martin  [00:25:44]:
Models, they can be managed by models so that people don't have to spend as much time managing it themselves.

Matt Fowler [00:25:52]:
Because that's the, that's not value added, it's not spending time on the relationships.

Michael Martin  [00:25:59]:
Well, right. People say real estate is a people business, right? It's a human, human business. Right. So sitting in front of a computer hunting and pecking in a drop down and filter is not getting rid of that aspect is putting the humanity back in the job.

Matt Fowler [00:26:11]:
Yeah, that was if you heard sky talk about this at Proptech last Wednesday. He was saying that that's the real value is that it lets you those kind of rote tasks that it's better at. It needs to go do those for you and remind you to go do them or just do them automatically so that you can be out meeting people and working on your network in ways that the bottom can't do that. It's far more value added behavior.

Michael Martin  [00:26:37]:
What's the like the famous quote where it's like I wanted, you know, AI to do my laundry and cooking so I could spend more time reading and writing and instead it's you know, doing the reading and writing and I'm, I'm having to, you know, still do the cooking and the cleaning.

Matt Fowler [00:26:51]:
Right, right.

Allan Nielsen [00:26:53]:
Yes.

Eugene Pak [00:26:54]:
Woo.

Matt Fowler [00:26:54]:
What a success. Well, so tell us really quickly on the way out a little bit about the, the plans for the product. So it's launching on the dashboard for a fixed rate per month and you can just sign up for that and sign up through Dorify. Are there add ons, things that you can add onto it cost more than the basic rate?

Allan Nielsen [00:27:17]:
Yeah.

Michael Martin  [00:27:18]:
So all Dorify members will have access to Sidekick for $25 a month. That'll include integration with Dorify MLS, that'll include integration with Gmail and Google Calendar for a lot of really cool things you can do there. It'll also include a comps presentation, kind of, you know, presentation workflow.

Eugene Pak [00:27:36]:
Cma.

Michael Martin  [00:27:37]:
Yeah, workflow that also will include the ability for custom branding. So brokerage logos, team logos, contact information, photos, all that stuff can be kind of, you know, custom branded. We have a few different subscription plans for teams and for brokerages we have a teams plan that's up to 15 seats. We also have brokerage pricing and with that you get integrations into dotloop and so managing loops, uploading contracts, reviewing contracts, changing aspects about what's in the transaction management system can be fully managed through dotloop. And we also have an upcoming CRM rollout which that'll include which can psychic can kind of speak to a few of the, you know, large, you know, more industry leading CRMs. And the use cases there range from like data entry, really easy data entry to getting really interesting insights out of the information that exists in the database. And psychic potentially also enriching the information that you have existing in your database. So that's coming up and what's really exciting is that that'll be packaged through a native mobile app.

Michael Martin  [00:28:43]:
So we've just had like a thousand requests for a native app and so we're actually midstream and in the build of that right now, which will be really cool.

Allan Nielsen [00:28:51]:
Cool Cool.

Eugene Pak [00:28:53]:
Yeah. And then on the softer part. Oh, I'm sorry, I was just saying that.

Allan Nielsen [00:28:57]:
No, go ahead.

Eugene Pak [00:28:58]:
No, because since we're partnering with you guys, you know, obviously going back to our earlier point, like, how do we get people to use it and get. Get comfortable with it, Obviously we'll be doing, you know, webinars and such for your members and get people, like, very familiar hands on again. And for a lot of, you know, I've already done a couple of demos, frankly, actually, for some of your members when after Prop takes out, a lot of them are actually ChatGPT users. And so they found that the interface, like, it's a very smooth, easy sort of, you know, they're like, oh, this looks very comfortable and familiar. Obviously we have our own sort of additional items on there too, which they really appreciated, but at the end of the day, they were like, this looks so familiar and comfortable. And then we just went through and we started doing workflows. I did one for one of your, like a team earlier today and just sat there and I said, okay, I'm your assistant for five minutes. What are you working on? And so even though we're still testing all the data and everything, you know, it was still actually, he.

Eugene Pak [00:29:47]:
One of the people actually compared it. He just kind of looked into MLS and it's kind of compared and contrasted, and he's like, oh, you guys are actually. It actually matched. So that was a nice one because I, quite frankly, I wasn't really sure what was going to happen and, you know, because it wasn't scripted or anything like that, but it was really nice to be able to do the search, do the analysis. And, you know, they were like, oh, this is great. It makes my. It would make my life a lot easier. So it's really cool.

Eugene Pak [00:30:13]:
Right?

Allan Nielsen [00:30:14]:
And we'll. We'll have both a town hall coming up, and we'll have a product showcase coming up, probably beginning of December, and then we'll be all set for. For a good launch.

Eugene Pak [00:30:25]:
Great. Yeah, looking forward to it.

Allan Nielsen [00:30:27]:
Cool. Matt, do you have any other questions?

Matt Fowler [00:30:30]:
I don't think so. Thanks for coming on the call today, you guys.

Michael Martin  [00:30:33]:
Thanks for having us.

Eugene Pak [00:30:34]:
Yeah, like I said, it was great being part of Proptech South. Really. That was really nice. Nice format to kind of meet up a bunch of your members and such.

Matt Fowler [00:30:41]:
Watch your inbox this week for next year's date coming out soon.

Michael Martin  [00:30:45]:
All right.

Eugene Pak [00:30:45]:
Yes, Planet. Thank you.

Allan Nielsen [00:30:47]:
Very good. Well, thank you again and thank you to all our subscribers for joining us in the podcast. And we look forward to next time. Thank you.

Eugene Pak [00:31:00]:
Sam.

Contributor

Matt Fagioli

Host