Solving for Mission-Aligned Outcomes with AI-Driven Grant Management Tool Eddi

Transcript

Show ID (00:04):

The power of data is undeniable and, unharnessed, it's nothing but chaos.

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The amount of data was crazy.

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Can I trust it?

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You will waste money.

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Held together with duct tape.

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Doomed to failure.

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This season, we're solving problems in real time to reveal the art of the possible, making data your ally, using it to lead with confidence and clarity, helping communities and people thrive. This is Data-Driven Leadership, a show by Resultant.

Jess Carter (00:31):

Hey everyone. Welcome back to Data-Driven Leadership. Today, I'm really excited for you guys to hear from Jeremy Bhatia. He's the CEO and founder of Eddi, an AI-powered platform that's transforming how public sector organizations manage grants and do so many other things. Jeremy is a really interesting guy and I really appreciate, he kind of embodies something I find really important. If you've read much of Patrick Lencioni, or some people pronounce it Lencioni, this concept of hungry, humble, smart. He sort of broke the pattern of where he'd been and what he'd been doing and jumped full force into building his own company. And there isn't any, there isn't a list of things that scare me as much as something like that is a big, brave thing to do in my opinion.

(01:20):

And so I have a lot of respect for him. They're not even a year in and he was willing to jump on the podcast and talk to us about what he's seen, why they exist, exactly what he's hoping for. And what I really appreciate about that is his openness to talk about how his platform is leveraging AI and even understanding and addressing with empathy the concerns people might have about that. 

(01:42):

I think that one of my favorite things that he talked through, or something you'll notice as a thread throughout this conversation, is his passion for outcomes. And again, they've only been around for just under a year and he is thinking long-term about the product he's created, the ways that they're building it, how people are leveraging it, how to make it more versatile for entities well beyond education. But he's also focused on what are those outcomes and how is it helping them get there? This is somebody that I will keep an eye on throughout my career. I think he's doing some really big, beautiful things and I really hope you enjoy this episode as much as I enjoyed making it.

(02:23):

Jeremy, welcome.

Jeremy Bhatia (02:24):

Thanks for having me, Jess. Appreciate it.

Jess Carter (02:26):

Yeah, well, I'm so grateful that you're the CEO and founder because you have to be good at telling this story and I want to get into it, but I feel like there's a lot to uncover for context. So when you explain Eddi to, maybe, Grandma, let's start there. What is this? What is this thing you do?

Jeremy Bhatia (02:46):

Yeah, that's a hard one. So explaining to Grandma, I think what Eddi fundamentally is doing is really helping leaders make better decisions using data and financial information more regularly. And so what that looks like is we bring in both financial information from their accounting system—so you can imagine all these organizations have dollars coming in from a lot of different places, and so we help make sense of that, make it easy to share—and then we also bring in performance and impact data into that same system so that in one place they can tell the story of their own organization, whether that's for grants, whether that's for the state, whether that's for their own internal use to make sure that they're making as good of a decision as possible. We really make it easy in one environment to do that.

Jess Carter (03:33):

You have just summarized the reason why enterprise data warehouses and AI analytics teams and dashboards exist. So what was the market calling for that led you to build Eddi specifically?

Jeremy Bhatia (03:46):

Yeah, it's kind of been years in the making. This company, to be honest, early in my career I was doing finance and data analytics and things like that for a network of schools and working for some really amazing and intelligent people. And one of the things I realized is actually, all organizations should have people like this doing this all the time. And it really supports the end goal, which is in education to drive student outcomes. Our goal is for the students. So that's kind of like where this all stems from, but what we have seen over the past several years, and specifically in education, but also broadly in government-funded organizations, is that the pandemic created this huge influx of money into the different spaces. And fundamentally, organizations were in a tough time, but they really struggled to spend all of the money. I was shocked as to why were there still organizations figuring out how they could spend this money.

(04:41):

And there was a few reasons why. One was due to compliance, one was due to really not knowing where to spend the money or having the data or the financial intelligence to do so. But then I started digging into the problem and really saw, actually, as you start to peel back the layers, this is a problem that's happening all the time, not just a pandemic problem. And we saw with many of the organizations I was working with my previous company that making really good decisions with your budgeting with your dollars was hard because there's so many different information sources. There's really so many different places that you could spend these dollars that it's a hard process to make. And it's really only been possible because of AI.

Jess Carter (05:21):

As somebody who's been in public sector for a decade now, I was really up close and personal during COVID, and I think it was so well intended to see those dollars come in. It was like every state, every municipality, every county, whatever region is going to have different needs during COVID. Some are more advanced than others. Some of them still need broadband. Some of them don't even have a hospital system nearby. And so they just knew that they'd have major needs and there was this assurance that we would fund them, but that did create this gap of like who's going to dictate where those go and how we use them though, and please let that be led by data, which sometimes in some cases it couldn't have been. 

(06:01):

So to your point, sometimes there was I think a pause in some of those decisions because it was like, how far back do we have to stop to try and catch up with the needs of today and meet those needs? And public sector, we're so focused on fulfilling the data requirements and reports, the federal reports and state reports that are required of us at the time that to your point, having sophisticated understanding of how to ask for funding from grantors as a grantee, how to report in a way that will probably secure more funding if you wanted it. Those require some iterations. There's some growing pain around some of that. Right? And so it sounds like Eddi is here. Eddi exists, and it's been around for how long?

Jeremy Bhatia (06:45):

Just over a year now. Yeah.

Jess Carter (06:46):

Just over a year. Oh my gosh.

Jeremy Bhatia (06:49):

Yeah. Still young.

Jess Carter (06:49):

Okay. Talk me through, really break down for me exactly what it does. Are there modules, are there components? How, if I was a prospect, what would you tell me about implementing Eddi for me?

Jeremy Bhatia (07:02):

Sure. Yeah. So there's a few parts of implementation, but one of the key parts that we really advocate for is that we make it easy to integrate financial data and student performance data or whatever that impact data may be for your organization in one place. And then what you have on top of that are two things. One is an AI-supported editor and has a chat that can work alongside you. Think of it as a more user-friendly ChatGPT canvas where you can actually focus on different sections. You can actually edit the words as you're going in a way that really is helpful for long 30-page proposals that you need to complete.

Jess Carter (07:35):

Sure.

Jeremy Bhatia (07:35):

So that's one part of the product. But we think it's really critical in order to have really good AI performance that your data is in a very, very clean place. And so the way that we do that is we have a dynamic table, which is like Airtable, but we've added a little bit more into it where you can essentially upload data and then create a relational database in a really user-friendly fashion in one place. So you can do budgeting with this, you can upload data, you can create, we've seen people streamline things like summer payroll and things that they're used to doing in 50 spreadsheets they're now doing in one central database.

Jess Carter (08:11):

I see.

Jeremy Bhatia (08:11):

And then the beautiful part about that, too, is once you have that data there, now the AI can use it and your results are multiple times better than what you'd get in just a transactional ChatGPT conversation. You're actually bringing in all this rich data with your task at hand, and you have this nice mixture of a documenter that you can pull in really clean data into.

Jess Carter (08:32):

That is super cool. Okay, so one year in, do you have any lessons? If you could go back and start it over again a year ago, is there anything that you would've done differently, either with the product itself or just with how you've built the company in the last year? I'm curious about your reflections.

Jeremy Bhatia (08:51):

Yeah, I'm typically of the mindset that everything that happened in the past leads you to the current moment, and that's the beauty of the past. All the failure is the learnings. And so I generally don't like to go through this exercise because it's like I wouldn't want anything else than what I'm currently in right now, frankly. However, in the process of learning, and I wouldn't have made any different decisions, here are the things that I've learned that I'll apply to the future. 

So the main things are, one is how critical it is to have really, really solid implementation with these organizations. And that happens honestly way before the first time you meet them, even. What we realized is who needs to be in those conversations is really important, both the roles, but then the archetype of the person themselves is really critical as well. And so that could be: Are they a data-friendly culture? Are they open to trying a new solution? Do they want to, do they really feel like the pain enough that they want to try it? What we've seen is that that pain has to be felt by the whole organization, not just one department. Oh, we all see this as a big problem and we need to solve it together.

(09:56):

So when we validated that, everything goes better. And then also we are much more sophisticated at asking what we need, when we need it, what our preferences for receiving it, all of those things. So that helps a lot. And then I would say all software companies are really only as good as their people. And one of the things that I've gotten a lot better at is definitely figuring out both who is the right fit for the company, but who is the company a right fit for as well, the kind of the mutual relationship there. And that's been one of the bigger things that I feel like we're able to find the right people for Eddi that will thrive in this culture where we are so impact-driven that you really, really have to care about what could happen as a result of Eddi and that be one of your motivating factors.

Jess Carter (10:38):

Yeah, you're doing something really innovative in a space where I think there wasn't space for it a few years ago. I think because of COVID, there was some funding to look at efficiencies that we can gain in some of these ways. And I say Six Sigma black belt, which I realize is not sexy, but it's real. And it was really valuable to me to go through that process, this concept of you don't lean anything out, you don't lean a process out, you don't reduce waste until it works, you know, have a good process. And so to your point about finding the right fit for Eddi, part of this is what is the health of your data? What is the openness of your culture to leveraging AI as a tool? Those things are foundational for an implementation of this kind? That just, that makes sense innately to me. How mature are they? So I imagine there's probably sizes of organizations or entities where if maybe they're too big or, especially, I'm thinking about too small, where it's like, hey, at some point it's going to be the right fit to say we can lean this out. You are mature enough to know that this makes sense to apply to this organization. Their maturity around AI, their interests, their openness, the stars kind of align. And it's just interesting.

Jeremy Bhatia (11:50):

It's actually interesting. We are working in some of our organizations that I wouldn't say have their data in a good place at all, but are really reaping tremendous value from Eddi solely because they are not satisfied with their existing processes and what they're currently doing. To the extent that they're so dissatisfied with th'em that they're motivated to do something better. And so in a place where they have student outcomes that are very below, working in tough circumstances, and improvement is the only option, which is awesome. And I think that's, I would say all of the things don't need to be true of data in a good place, motivated, open. I think, honestly, what I've seen is motivation across the organization, a really good group of people that are motivated is the most important thing.

Jess Carter (12:38):

Okay, that's cool, too, because what you're telling me is, it's not just a optimizer. It actually can help you get to a point in which your processes require that optimization. It can actually be a catalyst for the right kinds of changes. Okay. That's so cool. And it's amazing you've already seen that in a year. That's so encouraging and incredible. Now I imagine, I mean one of the things I was going to ask you is there have been grants around for a long time, grant management systems, grantors and grantees. What do you think about why this is such a game-changer for public sector organizations? I assume not just education, I assume you're looking at how to use Eddi in more broad ways. Okay.

Jeremy Bhatia (13:22):

Yeah, definitely. Our conclusion early on was that this needed to be a full life cycle product that you could, from the time you're thinking about getting money to the time that you're then reporting on it or understanding what the effect of it, it needed to cover that whole spectrum. And another part, too, is that a lot of the existing solutions are built with a very, very rigid business logic where it's like, it will solve this exact hierarchy of needs. It's basically like business logic built on top of a very rigid backend.

(13:52):

And so from the technology perspective, we kind of said, actually, what you need is the opposite. You need concepts that will work in different contexts, whereas many states are open to spending a year or a year–and–a–half for a custom build where it's like, actually that part shouldn't take a year. That part should take a month. That part should be quick. That was one of our big insights was it needed to work across the whole life cycle and have a very flexible business logic. That's where we've had a more generalized app logic that can be applied to a lot of different business cases. And so that was really critical. And then the other part on grants is that we realized that actually grants are a portion of the puzzle and that we really quickly wanted to move outside of grants into the general fund or whatever that may be in that organization. And so, once again, that was critical that we built not too much embedded business logic that we could then move into a less restrictive funding source and still provide value there.

Jess Carter (14:49):

I worked in nonprofits for a minute and there was, I helped with some of the grant writing and the reporting. And you're right, a lot of the grant systems start with when you think you're going to go, when you've already decided that you're going after this grant. And so it's like, well, we've lost all of that knowledge and value of what's everything we thought about going after? Why didn't we go after those things? Now we're going after this. How did that go? How did our reporting go? Did we get an additional year of granting or how many additional years of add-on were we able to receive? And most importantly, what was the value proposition? Right?

Jeremy Bhatia (15:22):

Exactly.

Jess Carter (15:22):

Did it do what we thought it was going to do? And I think some grantors are open to, no, we tried something and it didn't work, and here's what we learned from it. Not everybody is. So part of me too, the challenge of the grant piece is, you're looking at, and you guys sounds like realize this really quickly. You're looking at a piece of a puzzle, that grant piece, I love that you were thinking about it from cradle to grave and this quick realization of that's only one piece of the puzzle. We can't make the impact we want to make if we only look at that component.

Jeremy Bhatia (15:53):

Correct.

Jess Carter (15:54):

So leveraging the rest of the data and being more open and versatile about what they bring in and what Eddi can then be leveraged for is like anyone using an AI tool realizes it can do more with more. And so if you can actually make it more broad, how quickly did you figure that out?

Jeremy Bhatia (16:09):

Honestly, from the beginning was very, very much we saw if we could solve the grant problem, we could solve the all dollar problem because the grants are harder.

(16:20):

That was one of the big realizations early on in talking to these folks. And a lot of the people that are funded by grants in these organizations are also funded by general funds or a portion of that as well. And so that was a key realization. One of the things you said that really resonated with me, too, is when organizations are applying or are grant funded, the reality is is that they could do so much better if there was this feedback loop of like, you gave me this money, here's what we did with it, or here's what we didn't do with it. And the Eddi story, one of the things that I saw really close up over the past several years is some nonprofits really fade away that had really awesome missions, honestly, things that I think should still be in existence, but just because they didn't have this feedback loop of saying you were giving the incredible funders that you would die for to have on your donor list, right?

Jess Carter (17:10):

Yeah.

Jeremy Bhatia (17:10):

But they weren't able to tell this story of, you gave us this money, here are our impact metrics. Here's what we shifted here was this hypothesis we wanted to test, which is very much like a for-profit-driven way of thinking it, right? 

Jess Carter (17:21):

I have some acquaintances and some friends that work in this world and there's this sort of heartbreak over not wanting it to be a transactional relationship. This is an organism and grantors are innately tied to their grantees and they impact each other in all these ways. And there's learnings and how often that goes undiscovered or unreflected on, and the retrospectives don't occur because the grantees are nervous. There is a power dynamic there of, well, we want more money. And they don't realize that sometimes if they were just really honest about what worked and what didn't, listing the failures of how the dollars went is not innately a bad thing. It actually, for the right grantor, can convey so much confidence that you are being thoughtful, honest, partnership-oriented. And so I love hearing you say that. And I think to your point, I would hope that that then spreads to the whole organization.

(18:16):

So as they're using it far beyond grants, as you said that it's like, okay, I think we keep saying that the openness concept, that there's this opportunity and environment that Eddi can play well in to say, here's all the data. What could we learn? How do we think about it? And that's where I was going to head next is, it is AI. There's going to be these fears about how much power do we give it, what insights is it going to have about what we organization, and can I shape those? How much can I shape those? How much are, is it going to dictate decisions that we should make that I'm not sure are right? We see AI making up lots of things in the world. It's built to do that. And so how do you counter any one of those fears or concerns people may have?

Jeremy Bhatia (19:01):

Yeah, I mean, it's a very valid fear. I wouldn't just put it to the side. I think one of our goals in building Eddi the way we have is that we do try and mitigate the volume of those types of insights being revealed. Even when you do ask analytical questions, it'll say, what we recommend is that you'd actually do a longitudinal study. Here's a data you're missing. So we do point out those flaws still in the analysis that we're completing, which is important, but also I think structuring your data in a way that at least it's more usable than AI. Whereas I think the common behavior now is I'll throw a spreadsheet into this thing for one time and get one insight, but I won't keep adding to it or relating it to other data that's happening, so I get the whole picture. And that's where we really feel like a responsibility to create an environment where that is possible, where people are just in their silos throwing random data into a base model, but they are able to unlock more of an organizational knowledge that they can then use within these tools. We're probably not in the business of getting to causation where we can say, you did X, here's Y, and this is the direct link. Correlation is attainable. I think getting to correlation is very attainable, and we do try and just say, this should be a tool, one of many tools. You can have what the story is. You can either use it, don't use it, but the idea is it should be at least a point of your decision-making process. It doesn't need to be the whole decision-making process.

Jess Carter (20:26):

Yeah, yeah. Let's talk for a second about outcomes. What can you share so early? Can you quantify efficiencies that Eddi has delivered in the past, or are you getting closer to that? What does that look like?

Jeremy Bhatia (20:39):

Yeah, I mean, I think we're getting closer to the big goal for us. This over the next three to four months is getting to a place, and we're now in this really critical period over the summer where a lot of our admins are starting to ask those questions of how did the last year's grant impact this student population as they rebudget them? So we're in that phase right now where these next three months are really critical for us. So our summer is not a summer break at all. I would say the intensity picks up for us a little bit, which we love. So we're a little bit early on. The here was the investment, here's the outcome. We launched in November, realistically, or in October, sorry. And we are now in a place where several of our customers are starting to ask those questions and their environments are set up to enable those questions to be being asked.

Jess Carter (21:22):

Awesome.

Jeremy Bhatia (21:23):

And then I think the efficiencies we have seen to date have been on the speed of grant applications. So we've been able to cut down some grant applications where it's like, oh, the deadline's Friday and typically wouldn't have applied for this. We're able to get it done in that week, essentially.

Jess Carter (21:38):

That's huge. That's the number of times in a grant-based world that people don't respond to grants because they just found out about them. It's substantial. So if you can reduce that and help them to say, we can, I mean, that's dynamic. And does Eddi help match grantees and grantors?

Jeremy Bhatia (21:55):

Yes. Yeah. So what we're doing on that side right now is all the states where we're working in, you have their state education site indexed, so it's updating on a regular basis. Basically, we're both looking at the actual opportunities and then also all the files that are related to them. And our AI tool processes those files and converts them into a really easy way to understand the requirements, all the required attachments, it becomes a to-do list. And then each of those documents have all of the required sections built into them as well. Basically, go into a document, see the application immediately, and click different data elements to complete that application on the platform.

(22:33):

So that's how we help on the scraping. And then we also do that for grants.gov, all the federal grants, the foundation grants right now we're mostly doing with people. Mostly because those foundation sites, we found the way you apply for them to be so diverse. And so we're still working on that in the background, but most of our priority has candidly been on, okay, we can really match you with unlimited grants through just these two combinations of things. But we really wanted to make sure that the application and management process are really, really high quality because that's where a lot of the effort's spent. You spend a few hours per week searching, you spend the rest of your week applying and managing. And so that's where we wanted to really focus our efforts.

Jess Carter (23:13):

Two terms that you don't have to define these, we can work together on this, but I want to make sure people, just as we're on a data-driven leadership podcast, we talk about them: scraping. Do you want to take that one or do you want me to?

Jeremy Bhatia (23:24):

I can take it, yeah.

Jess Carter (23:25):

Okay.

Jeremy Bhatia (23:26):

Yeah. So I think especially actually right now, I don't know, the prevalence of scraping is increasing with tools like Relevance that are AI agents, but fundamentally, yeah, scraping is going into a website, understanding its structure, and then knowing which elements to pull on a regular basis. And usually you do it on an automated job where every day you go and check to see if things have changed, and then you put it into a readable database that can then be used in your application.

Jess Carter (23:52):

So interesting fact, I worked in two different states for their workforce agencies and from the Great Depression, there is a law that states must have a job board. And so every state has, like Indiana has Indiana Career Connect, and it will shock you. I know. I'm just ready to see your jaw on the floor at that. It doesn't make a lot of fiscal sense for Indiana to try and maintain its own job board by manually posting jobs. And so they scrape. So just so people understand, a use case is like, you go out and grab all of the potential grants that are posted on certain key websites, I imagine, and kind of pull those together in your database that's valuable to your product. In Indiana, they'd go to all the other major job boards to say, okay, can we scrape the jobs that are posted and see if there are new jobs or what that's indicative of our hot jobs or whatever. Now everyone knows what scraping is in two different examples. The other thing you said is, you talked about how your tool might recommend a longitudinal review or analysis. If one of your clients said, what's that? How would you describe that?

Jeremy Bhatia (24:52):

Yeah, so a lot of the shortcomings in analysis is because you don't have sufficient data over time to understand any trends, that's too short of a time. So when you ask a question, did this investment in X improve outcomes for this group, the analysis doesn't have enough time to actually understand if it's happening, but longitudinal will just suggest that you want to do it over a longer period of time and have multiple iterations of whatever that experiment was. So rather than saying in one year, or it could be any period, or rather being one week, it needs to be over 10 weeks, or rather it being one year, it needs to be over five years. But that's one of the common shortcomings in statistical analysis is you're not able to see the trend emerge because you don't have sufficient data over time.

Jess Carter (25:36):

Yep. One of my hilarious ways I explained this to my 6-year-old was I was like, hey, I really enjoy gardening. And I was like, can you imagine if these books I have about gardening were only researched and published after one year of gardening? Maybe they just had a bad year. Maybe there wasn't enough rain. Maybe there was. So they're built after years and years and decades. That's why there's a zone for our state and a zone where we live is there's a century's worth of data about what works well here and what doesn't versus somebody who does one year, one season study and says, Nope, you can't grow, I dunno, butterfly weed in zone six. And it's like, no, actually you can. It just was a bad year.

(26:19):

So to your point, longitudinal. Where I get excited is that what you're starting to do, and this is my words, maybe not yours, but it's this transition in my opinion, from a one-time analysis of data, like a data asset to turning data into a product where it's a living, breathing thing. We're making decisions based off of it, but it does mean we're continuing to pipe data in and generate results. We're not doing it one time and then saying, we've done it, it's done. Here's the decisions. We're saying it's working this well. Here are the themes that are emerging, now these are shifting. And to do that in a nonprofit space or in anywhere where there's grants, how incredibly important that the grants are funding these things, let's make sure the grants are driving funds towards the right outcomes in a real way, not we got lucky this one season. And so this concept of what Eddi could look like in the next decade, that gets so exciting. Right?

Jeremy Bhatia (27:14):

Yeah, I know. Certainly, I'm excited about it. And this was maybe a point that I missed early in the podcast when you talked about the why was, one of the reasons why was the pandemic had this, what I thought would've been this beautiful opportunity to deploy a lot of money and then see what actually worked. The government, I think, it was 190 billion in the end. And then Stanford and Harvard did this big study, very reputable professors. Fundamentally, there wasn't a clear connection between what investment states were making and then the ability to maintain outcomes for a population. Out of 190 billion dollars. You think the one time you have a controlled time period with a lot of money being invested, you have scale across the entire United States. You have all of these elements that would lead to a great natural experiment that you don't need to do anything besides actually track and see what's happening.

(28:06):

But the result of that was there was no indication of what worked. And I think the problem is there's not enough tools. As a data analyst, you use Jupyter Notebook and things like that, but for the most part, these tools are missing in a world where, to me, the outcomes are way more important. You're talking about young people, you're talking about old people receiving health. All of these things are just so incredibly important that it feels like one of the most important problems that I've worked on in my life where it's like, can we help understand what's happening and actually what is driving citizen outcomes?

Jess Carter (28:39):

I think what you just said is just so important. Can we understand what's happening? And at least in nearish real time, because I think everything is getting faster in society, what I'm worried about is our ability to retain lessons and to understand how to appreciate that. And that can be a lot of wasted dollars. I think having more tools is part of the answer. I also think what we ask people to measure matters.

Jeremy Bhatia (29:01):

Yes, agreed.

Jess Carter (29:02):

And so for me, I was in a state agency with public health, monitoring the disease propagation throughout Indiana during the COVID, and there were so many unbelievable lessons we learned about how you measure that data, how you leverage it, that every state should have already learned and implemented by now. It didn't happen. 

And so I do think that there's something to be said for anyone who is in any leadership position of any company, what you measure matters. And I think stopping to think about what are the outcomes you really want and what are you measuring? And do those align at all? Because during COVID, the number one thing we should have been measuring is what are all of the ways that each of the states are maturing in their use of data to drive meaningful outcomes. And if we'd done that, oh my goodness, what a different world 2025 would be. And I'm very worried that we've not all learned the lessons that we should have from that experience.

Jeremy Bhatia (30:02):

Yep, yep, yep. That's exactly right. Yeah. I think, at least in the education space, there's very defined or I think they are evolving to how we assess students, but there are very defined outcome metrics that everyone's focused on. So we, fortunately, what people measure has been predefined for us. Whether or not we agree with those measurements is another question, but understanding what to measure and whether or not it's indicative of future success is pretty important. And I think that's certainly being revisited education right now, unfortunately.

Jess Carter (30:35):

Yeah. Do you want to answer on a recorded podcast? Is attendance the right thing? I don't actually know. I have a six-year-old and a four-year-old who are just starting the school, and I'm like, that's so interesting that we went from COVID and I think we started to see outcomes were impacted by not being, so then there was this very strong reversal. I'm not Ned. I find that super interesting. I don't know. What do you think?

Jeremy Bhatia (31:00):

Yeah. Well, I've taken a lot of economics courses. I would say the rational actor would say that if someone deems that going to school is not worth going, it's likely for a reason. That's part of the equation, I think. I don't know. I have a two–and–a–half–year–old, too, and I honestly think some of her days where she learns the most is when she's not in school where she's doing all sorts of things. But I think obviously school has its value too. So I think there's really rigid attendance metrics now of, I think it's over ten days you're considered chronically absent or something, and that's become more and more rigid, opposed to understanding that there are potential ways to fill gaps at home or other experiences that matter just as much. But in a large system, you kind of have to create boundaries, right?

Jess Carter (31:48):

There's a supply and demand, and so if enough people start thinking that being in school isn't helpful, that can really harm the economy because we all count on school for our children so that we can work. And then you have, I think the other thing that I struggle with is the history of education and how it came to be this rigid protection of these certain hours. And anyway, that's for a different podcast, but I'm so excited and appreciative of the ways that you're using your leadership and your passion for data and creating something that the market needs that's going to drive. I mean, if you think about your impact, I just get excited when I think about Jeremy. That's not just a, you could just work on one grant with one entity and have this outcome, but the ability to help drive so many different entities to the funding they need to have the right outcomes they want. I mean, that is the dream, right, is to have a lasting impact like that. It's pretty cool.

Jeremy Bhatia (32:44):

Yeah, it's certainly my dream. People on our team, and then many of the people we work with thus far really get it. I think that's the biggest lesson from year one is definitely the amount of gratitude I have for all the people that have been in the Eddi mix of supporting in some fashion, receiving our product, using our product, giving us feedback. It really does take such a movement to drive innovation in the space where it's the propensity for any human is to do what they know. When you have the openness and you see so many people with this understanding that we could be doing better for our students, for our young people, we fundamentally can and we believe we can. Even people that are 30-year veterans in this space, they believe that still. And it's incredible, right? That's still a living, breathing thing in this world where so many people know there's a better way to do things and we can get there, and they're open to it, and it's cool.

Jess Carter (33:38):

Hear, hear. Okay. I reserve the right to invite you to come back after year two maybe and let us know how it's going.

Jeremy Bhatia (33:44):

That'd be awesome.

Jess Carter (33:45):

Yeah. Jeremy, if some of the listeners want to keep up with Eddi and follow you, we didn't cover how it's spelled. We can put stuff in the show notes, but how do they find you? What's the best way to find you?

Jeremy Bhatia (33:55):

Sure. Yeah. So LinkedIn, we're fairly active. We try and post a fair amount about the company, what's going on. We're Eddi, E-D-D-I, our URL is eddifi.ai because eddi.com was taken, but so eddifi.ai is E-D-D-I-F-I.ai. And then, yeah, if you're interested in my email is jeremy@eddifi.ai too.

Jess Carter (34:17):

Awesome. Thank you so much. Thank you guys for listening. I'm your host, Jess Carter, and don't forget to follow Data-Driven Leadership wherever you get your podcast, letting us know how these data topics are transforming your business. We can't wait for you to join us on the next episode.