Dec. 4, 2025
AI is REVOLUTIONIZING Clean Energy Permits
#EP322
Today on the Clean Power Hour, we dive into one of the biggest bottlenecks strangling the clean energy transition: the slow, messy world of zoning, permitting, and early-stage development. Our guests are Julia Wu and Anuj Saigal, co-founders of Spark AI, a Y Combinator-backed platform that has ingested and summarized nearly every zoning code, ordinance, and permitting pathway in the United States.
Julia Wu is CEO and co-founder of Spark AI. She's a software engineer who cut her teeth at Microsoft, Apple (working on Siri), and Brex before founding Spark. Her background spans AI, natural language processing, and financial technology.
Anuj Saigal is the co-founder of Spark AI and a veteran of the energy sector since 2010. He worked at the Department of Energy Loan Programs Office during the Obama administration, then moved through SunEdison, EVgo, and Natto (a video AI telematics company). Anuj has been a developer of utility-scale and distributed generation projects and understands the pain points of permitting and interconnection firsthand.
Key Discussion Points:
- Spark AI uses large language models to aggregate zoning regulations, permitting requirements, local sentiment, and news articles for every jurisdiction in America
- Three core use cases: early-stage greenfield site selection, monitoring projects in development for regulatory changes, and rapid acquisition due diligence
- Standard Solar (Brookfield-owned) reduced acquisition diligence from months to one week, cutting time by 75%
- Dynamic Energy accelerated site screening while improving community sentiment risk assessment
- UGE used Spark to assess a massive portfolio acquisition, determining which parcels had viable zoning paths
- The platform is LLM-neutral, easily swapping between OpenAI, Anthropic, DeepSeek, and other models to optimize for accuracy and speed
- Unlike off-the-shelf AI tools, Spark delivers consistency, accuracy, and citations developers need for trust-but-verify workflows
Connect with Anuj and Julia
Julia Wu: https://www.linkedin.com/in/wu-julia/
Anuj Saigal: https://www.linkedin.com/in/anujsaigal/
Spark AI Website: https://www.sparkhq.ai/
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You got to see it to believe it, right? I hate to say that, but you just have to, you know, the standard off the shelf stuff just not going to work for use a little bit of kind of industry terminology here. It's like, deterministic versus probabilistic. You know, off the shelf AI is not it's going to give you different answers. Certain times it's going to hallucinate. I'm sure that's a word that a lot of people are tuning into nowadays.
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So we are, we are developers who are building for project developers, and that means that we're pulling the right information to reach the right conclusions and putting it in a format and a workflow that is genuinely going to speed up work.
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Are you speeding the energy transition? Here at the Clean Power Hour, our host Tim Montague, bring you the best in solar, batteries and clean technologies every week. Want to go deeper into decarbonization.
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Today on the Clean Power Hour, we're diving into one of the biggest bottlenecks in the clean energy transition, the slow, messy and unpredictable world of zoning, permitting and early stage development. My guests today are Julia Wu and Anu Saigal, the co founders of Spark. AI a Y Combinator backed AI native platform that is rewriting how developers identify sites, assess project risk and move solar and storage projects through local jurisdictions.
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Spark has ingested and summarized nearly every relevant zoning code, ordinance and permitting pathway in the United States and their AI continuously rereads and updates that information multiple times a week. What used to take a developer a week of tedious document hunting now takes about 10 seconds, we're talking about the future of AI, insight, selection, data rooms, local politics and clean energy development at scale, and how an AI first approach can unlock gigawatts of projects that would otherwise die in permitting purgatory. Strap in. This is a look at how AI is accelerating the energy transition where it matters most, the front end of development. Welcome to the show.
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Thank you for having
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us. Yeah, good to be here. Thanks.
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Great to see you again, Anuj, and to meet you.
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Julia, you know, it's heady days. AI has exploded onto the scene, literally and figuratively, and most energy professionals are aware that they can leverage these tools, both in your just daily workflow, helping you process information more quickly. But now we have this advent of AI, first platforms like spark that are truly making it faster, cheaper, smarter, to develop solar, wind, battery projects.
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So please tell us a little bit about yourselves. How did you guys get connected, and what was the spark for Spark. Ai, I love it. I love it.
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Very good. Thank you.
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Thank you, Tim for having us on the show. Spark is AI, workflow software for solar storage and data center developers, and the way that we work is we use llms and state of the art technology to aggregate zoning regulations, permitting requirements, local sentiment, news articles, and we've done this for every jurisdiction in America, so that developers of these energy infrastructure projects can determine fatal flaws, identify risks and make decisions on investments in seconds instead of weeks. And prior to starting this company, I was a software engineer. I went to school for computer science, and back then, I was learning about more traditional natural language processing and software engineering, but I fell in love with it, and decided, even before I graduated, that I would learn from the best in technology and then start my own tech company. And to zoom out a little bit, I grew up very internationally, between Brazil and Asia. And these are places with tremendous natural resources, tremendous need for energy and power, and also some of the biggest pollutants and the biggest i. Causers of climate impact. And so that is that was an extremely relevant environment to grow up in. And then when I graduated from college, I was working at Apple as a software engineer on Siri, and Apple, they already took clean power consumption very seriously. And 24/7 clean energy, and I realized how much power is required to build the future. I was also following diesel crises in Brazil from afar, and reading about the energy transition, reading about renewal renewables, and working on side projects related to that. And I also went on to work at brex, which is a financial technology unicorn building, agentic finance for the door dashes and coin basis of the world. It's a total rocket ship.
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And I got to see how this company went from early stages to billions in revenue. And with all of these experiences, I then sought out to build my own tech company, and I wanted it to be at the intersection of bleeding edge technology, because that's where my bread and butter is.
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I'm an engineer at heart and impact civilization infrastructure. Those are all very important to me as a person, and we started having conversations with developers in utility scale, solar and storage, and we would keep hearing challenges like interconnection and permitting.
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Interconnection and permitting, that was always the set of challenges. And teaming up with a new with a background in this space, and having been there in the early days of Sun Edison and the loan programs office, is what led to spark becoming a
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Very good. Anuj, introduce yourself.
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reality.
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Yeah, great. I've been in the energy, transportation infrastructure business for a long time.
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Started my career in finance, but basically since 2010 I've been working in energy transportation infrastructure, what I call energy and transportation infrastructure.
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For me, that's the catch all phrase for a lot of the things we're talking about today. So I started off during the Department of Energy, loan programs office during the Obama administration, then had a smattering of different roles, but once again, always in energy and transportation. So Sun Edison, evigo nado, which is a video AI telematics company, and really sort of sought to make my impact on energy and transportation, typically putting clean electrons into the ground, or, you know, producing clean electrons and putting those into cars or or people's homes. I took a kind of a side step, or a brief departure from energy in 2020, when I started my own company, and that company had nothing to do with the topics we're going to talk about today. But the reason why it's relevant is one of my investors, Y Combinator, is also an investor in Spark, so through that network, is how I met spark and Julia, and was intrigued by what was cooking here and and the way I can explain is this, I've been a developer. I've worked on utility scale projects. I've been I've worked on behind the meter projects and a lot of other things. And there was a lot of parts about that rule that I liked. I really liked being a developer. I like boots on the ground. I like doing these things. There were elements with respect to interconnection permitting that were really hard. It was really hard to go through a zoning ordinance. It was really hard to research what is the true path to permitting at a jurisdiction I had never been in, and, you know, long and short of it is, AI is really good at these things. AI is a large language model to kind of paraphrase, and large language models are good at guess language. So as Julia mentioned, there was a key thing that she mentioned her introduction is, we've gone through nearly every jurisdiction in America and have read through over a million documents that are relevant to this permitting thing, the meeting minutes, the local news articles, all these things. No human could possibly do that.
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And we do it not only once. We do it multiple times a week. So it was these things that I was like, whoa. Like, that is that is the thing that is the thing that hits on some of the things I did not like to do as a developer and to ease those processes. And when I heard about that, I said, Oh, this is, this is this is going to be money, and I'm excited about it.
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So that's a bit about me. I'm based in Los Angeles. I've got two kids and and all that sort of thing.
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So just to set the table, Spark reads, parses and summarizes every zoning code ordinance and meeting note in the United States. It updates that database multiple times a week, and it's. So if I'm a developer, and I've identified parcels that I'm interested in potentially, and there's a myriad of ways that I might do that, but what is the accelerant for me as a developer? How am I leveraging Spark to go further, faster? And basically it's kind of a green, yellow, red. I want to get parcels out that are going to be difficult to develop or impossible to develop, and I want to focus on those parcels that are green and developable and potentially can turn into a project, because only a small fraction of parcels that go through initial permitting are going to get developed, and there's a continuum, and some will get developed more quickly than others based on what's going on at the local jurisdiction and in the local community, how much pushback there may be locally, and things like that. So how does this work?
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Yeah, I'll give an example from one of our customers, dynamic energy, which is representative of our customer base, including the best developers in the world. So when dynamic came to spark, they had their in house approach, like you said, Tim about site selection and assessment, but any strong developer with a strong pipeline is going to have a lot of parcels, and this is a process that can take hours or days to manually abrogate the zoning ordinance, read through the zoning ordinance, understand for this zoning district, what, what are the setbacks and the high restrictions can I even develop here? Do I need a conditional use permit, a special use permit? And then looking at the presence of moratoria, that is from another source of information, and then there's understanding community sentiment. What is the likelihood of this authority having jurisdiction of approving my project, both from the decision makers perspective, but also from what locals are saying? So that's searching the internet for what, for news articles, Facebook groups, on top of what we already talked about for zoning regulations and who the local authorities are, so it is a desktop research process that spark has turned from four To 10 hours down to minutes for every site, and this allows developers to screen faster identify the promising parcels, whether greenfielding or from an acquisition perspective, you're still looking at a portfolio of projects right which ones are likely to succeed based on zoning, land use, moratoria and sentiment, and, of course, nearby projects and competition.
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You want to add anything Anuj?
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Yeah, I'll try to keep mine short. There are, there are multiple use cases, but there's three main ones. And this is somewhat paraphrasing, but also building on top of what Julia said number one, early stage site selection, you don't the more sites you can look at swiftly, the more you're able to identify the good ones fast, the ones that have fatal flaws. So if you're doing that soup to nuts from a traditional desktop research point of view, that's hard. When you use Spark, you type in a geographic identifier, a parcel, a county, a town, whatever. You press enter, and all that information is at your fingertips, literally everything you might need. Number two is, you know, the second use case is, okay, I've put some capital down. I know where I want to build a project. Maybe it's in some stage of development.
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Hasn't reached NTP yet. But as you and I and everybody who's posting this, knows, projects don't take just a couple days.
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They take, potentially years to build. Stuff changes in those years, the zoning might change, the city council might change. A bunch of things that a developer cares about will likely change.
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How do you keep tabs on that?
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Particularly if you have 510, 15, 100 different sites depending on your small versus utility. Skilled developer, Spark is going to kind of alert you to those things, and we already know which is going to kind of change the game for you.
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Somebody said moratorium last night at the city council meeting. Well, you might want to know about that. So that's number two, and then number three is acquisitions. You and I both know that. Hey, you know capital is the I should say the industry is in an interesting environment right now. There's well capitalized players and there's less well capitalized players, that's one of the things that's driving just intense acquisition activity. So if you want to very quickly diligence, a data room of potential DG sites, I don't know by DG, I mean distributed generation sites to read through every single document. A data room takes time, and I'm sure people. And who are, who are listening now I've done that.
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What spark you can plow in the whole data room and, like, understand, you know, get a sense on the portfolio in, seriously, just like 510, minutes. So those are three areas where some of you might want to use Spark.
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So let's talk about green Fielding. You know, these llms. One of the answer engines that I use is perplexity. And I probably could put in a list of parcels to perplexity and say, Hey, give me the local sentiment on these parcels in X, Y or Z state, and it and it can go out there and look at what's going on in the interwebs and on social media and come back with some answers.
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But what is different, I guess, and how do you go deeper and give your customers a better answer or dashboard or user experience than just cludging together a workflow or somehow creating their own automation using some combination of llms and things that connect their data to llms. How is your platform creating a better experience for the end user.
00:17:17.319 --> 00:19:47.099
Great question. It boils down to three elements, consistency, accuracy and form factor. There's a lot more that we could talk about, but I would say these three or what we hear from our customers over and over, and when we tried to build a perplexity wrapper, which we did, by the way, we tried to build a perplexity wrapper a chat PD rapper. We Yeah, we did that. We tried that, but it wasn't at the level of quality and accuracy and consistency that a developer needs. So what do I mean by consistency? A an answering engine is good at scraping and getting you the URLs, and it's good at predicting the next token is good at summarizing some high level information. But is it going to know that for a developer, when I'm asking about the zoning requirements, whether I need a conditional use permit or a special use permit, the answer is not going to be as deep for this particular HJ and going deep into their zoning ordinances of 1000 pages to get you exactly what you need and for that answer to be consistent across different parcels, and every time that you query that is One and on the accuracy perspective, Spark is huge on evaluations and citations. We take state of the art models, whether it's perplexities, models open AI, anthropic. We'll go for whatever is the best at this given moment, and we will have evaluations for their accuracy. We will have classifiers that are trained and coached so that we are able to identify the right information and classify whether a piece of information is worthy or not. On top of that, everything you see on Spark has a citation, either to the URL or even deeper, to the exact passage of information so that developers can do what they love to do, which is trust but verify and finally, finally, form factor, because it's all about workflows. We want to be the place where work gets done.
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That means your previous searches, your portfolios, your what you want to share with your team, what you want that to look like. We want to do that with the developers workflow and needs in mind. And that's where the software, UI, UX comes in.
00:20:05.359 --> 00:20:27.399
Yeah, you know, Tim, the only thing I would, I would add to that, I think that's a great background, is you got to see it to believe it, right? I hate to say that, but you just have to, you, know, the standard off the shelf stuff just not going to work for use a little bit of kind of industry terminology here. It's like deterministic versus probabilistic. You know, off the shelf AI is not it's going to give you different answers.
00:20:25.420 --> 00:20:45.839
Certain times going to hallucinate. I'm sure that's a word that a lot of people are tuning into nowadays. So, we are. We are developers who are building for project developers, and that means that we're pulling the right information to reach the right conclusions and putting it in a format and a workflow that is genuinely going to speed up work. The Clean
00:20:45.839 --> 00:21:02.899
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00:21:46.859 --> 00:22:35.759
There's new tools or new families of tools coming online every day. You know, chat GPT just released 5.1 and there's some delta. And I found, you know, three months ago, I used to use Claude a lot, and now I use chat GPT more because I like how it operates better. I like the responses. It just feels more intelligent. It feels more human. And like, ultimately, we are building, we're all building a small army of super intelligent colleagues that are not quite human level yet. And it could be two years, it could be 10 years, it could be 20 years before their human level.
00:22:36.180 --> 00:23:17.740
But if you're not leveraging this small army of you know of assistance, you're missing out on something. And there's this thing called agentic AI, which is still very much a black box, and it's a double edge, because if, if it works, it can speed things up, but it also makes a lot of mistakes or breaks. And so where are you guys on the agentic path? Or, if not, like, what, where are you leaning in to the best of breed technologies to build a better platform for developers.
00:23:18.819 --> 00:23:29.319
Yeah, today, agents are used in our product and in our product development process.
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So from a product perspective, we are using browser agents that are able to access information that vanilla scraping might not be able to do, and we're constantly experimenting with how far can agents go in terms of doing the work that a human might be doing, whether it's reading, aggregating information, or putting together the report in a form factor, in a structure that humans are expected to do. So we're constantly experimenting with that, and being close to having our ears to the ground by being a Silicon Valley based company, we're always sometimes first to get access to these bleeding edge models, and then on the keeping up with all of this, we built sparks architecture so that it's easy to hot swap or try out different models. So whether it's GPG 5.1 or Claude sonnet 4.5 or deep seeks latest models, whatever those might be, our architecture is built so that we can easily swap out models and try it, run them by our evaluation systems and see how the quality is, and if it's not up to par, sometimes you get more you get more accuracy, but that compromises on speed, so we're constantly iterating on that and applying agents to get that information, produce output, and obviously, in our day to day building of these products. We are using all the development software development agents as well,
00:25:18.579 --> 00:25:50.160
you know. We we want the foundational models to get better. We we get better when the foundational models get better, we actually don't view it as a threat. There's too much specific coaching and workflow and real product work that goes into create, creation of Spark, so that the underlying, foundational model of which you spoke about some Tim and so Julia, we love it. We want those to get better. That is a good thing for us. That is a good thing for the world, and it is a good thing for the ultimate quality and value that that our customers will see.
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Yeah. So let's talk about some of the practicalities of using the platform. Whether I've got a portfolio of Greenfield parcels or a portfolio of, you know, pre NTP sites that I'm evaluating, you end up with, you know, a bunch of different types of information, and it's not easy sometimes to parse through all that information as the end user. But tell us a little bit about how does a user interface.
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Is it like a plug in to a GIS platform, and literally, you're you're giving some kind of a green, yellow, red to a layout of parcels and suggesting that a developer focus on x, y, but not Z, or some other interface.
00:26:47.399 --> 00:26:53.359
Yeah.
00:26:53.719 --> 00:27:02.959
Your intuition is right. Tim, there are different ways to import information.
00:26:56.598 --> 00:27:13.778
There are geographic identifiers for individual parcels, and there is a document input for data roots, so for individual parcels or batches of parcels.
00:27:14.019 --> 00:27:54.739
You can enter an assessor parcel number on Spark. You can enter the name of an HJ and we're also incorporating support for different types of formats, whether it's KMZ or coordinates and addresses, so there's a geographic identifier for individual parcels or sets of parcels, and then to your point about pre MTP under development assets, these are usually in the form of a data room. A data room a fat binder of files, as I like to call it, across land. PPAs, environmental interconnection.
00:27:54.918 --> 00:28:23.858
These are all PDFs that are either scanned or perfectly formatted, and we allow our users to upload entire data rooms with hundreds of files scanned, handwritten or regular PDF files and help them extract key insights about where those projects are, what the risks are, payment terms, milestones and the parties involved.
00:28:26.740 --> 00:28:30.879
Are there some success stories you guys could put your finger on?
00:28:33.339 --> 00:28:35.819
Yeah, I didn't get started then. Anuj, please chat.
00:28:35.819 --> 00:28:37.980
That sounds good. Thank you.
00:28:38.399 --> 00:31:02.119
We've recently published a number of case studies with our customers, and there are more in the works. I would love to talk first about standard solar. They are one of the best and largest developers of distributed assets in the country, owned by Brookfield and standard solar, when they came to us, they had a lot of projects that they wanted to acquire, and with Spark, they were able to take a process That would take months and a lot of consulting fees and external lawyers to to process because it is a deal that they are diligencing. And with Spark, they were able to reduce that process from months to just a week and not have to throw bodies at the problem. And as a result, standard solar is one of our most prolific users, with weekly usage, and otherwise I mentioned earlier is Dynamic Energy, where we became a solution for early stage diligence and interpreting sentiment, community engagement and the key factors for getting projects approved from a permitting and community buy. In perspective, a third success story is Uge. They were acquiring this massive portfolio from Oya, and in order to determine which projects and which assets were going to see the light of day, they had to figure out which ones were actually in a zoning district that has a path to getting permitted. And so from a land use regulation perspective, Spark helped them assess those parcels, and there are a lot more stories. And right now, because of safe harboring and because of everything that's happening with data centers, speed is everything, speed to power and the silent killer of all these projects on top of. So pure power is permitting and development feasibility. So these are some examples of how we were able to work with great developers.
00:31:03.680 --> 00:31:04.220
Thank you.
00:31:04.700 --> 00:31:28.720
Yeah, you know what I would add is we'll hope, hopefully, we can link to some of these in the show notes if there are. And you know, there's some more sure, you know, not only qualitative but quantitative elements of these things. So you know, every person who's interested should look at the numbers and see if it fits what they what they might need. But look, you know what we touched on earlier, there's three main use cases.
00:31:25.539 --> 00:31:35.160
There's kind of early stage green Fielding, where spark can be helpful. There's, hey, we've got a project in development.
00:31:31.920 --> 00:31:38.639
How can we get alerted and keep keep tabs on how many things?
00:31:35.160 --> 00:32:09.139
And there's, there's examples of that. And then lastly, as Julia mentioned, lots of acquisition activity. That's what we're hearing every day from everybody. And you know, if you are not able to be a good partner in those acquisitions, meaning, get back to people quickly, that's that's a little more difficult. So we want to help people be good partners, be good potential acquirers, and act, act as fast as possible. So we've been able to aid some really great, well known partners in in that acquisition process. I think the one thing that she didn't mention that I would love to maybe let the cat out of the bag a little bit.
00:32:09.139 --> 00:32:17.259
You're gonna hear a lot about us on data center development. Now data center development and solar and storage development are not the same, but there's a lot of transferable skills.
00:32:17.319 --> 00:32:55.460
There are elements of the development process that are the same. So I think what you'll be hearing from us is as we partner with a lot of different organizations on data center development to help them understand what's going on, what the sentiment is like, what the local Chatter is about that development so that people can get those data centers developed faster. So that's what's to come. And I think there'll just be a tremendous amount of good work there, because, frankly, there has to be, I think you, Tim, and everybody who's probably listening in knows that energy consumption is increasing fast and and we're going to have to be part of that larger process.
00:32:55.940 --> 00:33:39.000
So one of my dreams is having tools that can assess where we are, and we could be a community or a group or class of professionals, but assess where we are and pursue different approaches and different scenarios. You know, you think about the AI data center build out, there are many constraints. There's the grid itself and its ability to deliver electricity to certain locations and and then there's, you know, where does, where does society need those data centers?
00:33:39.059 --> 00:35:32.880
And sometimes there's a disconnect between where the infrastructure is and where the need is. And then there's, there's this possibility that, well, we could get outside the box and develop data centers, you know, thinking 10 years into the future, and build them as micro grids, and put them exactly where we need them, or are going to need them. And I'm, I'm just curious, like, have you run into either you know this concept, I guess, at Spark, or in your travels, of people who are really, really leaning into the future. Because I do think that there's somewhat of a missed opportunity when it comes to, you know, we talk about super intelligence, and I, you know, it's, it's great to take the the existing workflow and accelerate it, and I get it, and I see that platforms like spark can do that, and I don't doubt that you're going to have tremendous success based on just what you've already built. But I also think about how dynamic the world is and the, you know, it's, it's no short order to, you know, 3x the grid in the US, when we electrify transportation, we continue to have this AI explosion. And meanwhile, it's like there are other external, external constraints. You know, we're creating a trade war with Asia right now, and that's just, like, really painful in so many ways, for the for the energy transition, there's a lot of friction there. So I'm just curious if that resonates at all, or what you have to say about that.
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Okay, this is, this is a very important topic, and what we've observed is people getting very creative about how to power these data centers as quickly as possible. The law. Long term vision of a more decentralized and self sufficient generation approach is is a future worth aspiring towards, and there is a lot of conversation in our communities about micro grids at the same time, and more current is various approaches of getting data centers turned on as quickly as possible. So what we have observed is, just to give some examples, is our customer base, some of the best developers of energy in the country, being really good at energy development. They know where power is. They're very skilled at solar, storage, even natural gas generation development. And then they will partner with data center developers to they will be responsible for the energy generation assets and then partner with data center developers to actually put the servers and the GPUs on site.
00:36:57.619 --> 00:37:59.599
What we've also seen is some more vertically integrated developers just going after where energy is more is more abundant and citing their projects that way. And we're also seeing really interesting applications of grid flexibility, whether it is demand response with data centers and utilities or using battery storage as a way to get data centers both turned on faster and helping them be good citizens with the grid. So we're I've been observing a lot of creativity is in the near term, how to get data centers powered as quickly as possible, while keeping in mind that we're going to need a lot more power, so something more decentralized could be in the cards.
00:38:02.000 --> 00:38:33.120
Yeah, there's a couple things I'll hit on. Some are related. So we have seen that even traditional development has changed. What I mean by that is, like, what we're hearing from some fairly large partners, I can't quite name, but varied household names, is that data center development didn't necessarily used to have energy as a primary filter. Like, can we potentially feed this with energy? What is the, what is the or power this?
00:38:28.600 --> 00:39:34.320
We're now seeing that, so there's this sort of alteration or edit, if you will, to the way traditional development has happened, so that that has happened is happening and will continue to happen. So I think that's a really important point to note, that this connection between energy and data centers could not be tighter. And many of this all, you know, you know, all the smart folks have made adjustments to that development process to to make sure that they they get there. Two is, like, there's going to be, you know, I think your point was like, Hey, do we need to really think out of the box? Are there going to be some crazy technologies that we must employ to meet what is going to be incredible demand. And I think the answer is yes, both, there's going to be improvements to the way traditional development is done, incorporating technologies such as AI to get the development done faster. And there's going to be brand new technologies that come on board, I'm sure we've all heard about some of them to make sure we meet these crazy energy demands.
00:39:34.320 --> 00:40:22.540
But it doesn't mean that we let go of one or the other. I think we try to do both. So the answer is not the tyranny of the or, as a, you know, a boss of mine used to, used to said, it's, it's truly and and I think the last thing I'll say is, in any one of these paths that we follow, one that is, you know, an alteration to traditional development or some new technology that comes out of left field, dealing with local governments and populations and understanding sentiment at the local level will never change. You can't just pop something down anywhere. I don't care if it's West Texas or rural Ohio or Indiana or, you know, some urban area, you must, as a developer, have deep appreciation and understanding of that population and that local government, and I don't see that changing, in spite of, you know, these couple paths that we talked about.
00:40:23.460 --> 00:41:03.030
No, in fact, your platform would inform where they need to double down and do do a better job. I'm a huge fan of of a good ground game and being transparent with communities that are most impacted by energy projects. Hey, guys, are you a residential solar installer doing light commercial, but wanting to scale into large C&I solar? I'm Tim Montague. I've developed over 150 megawatts of commercials. Solar, and I've solved the problem that you're having you don't know what tools and technologies you need in order to successfully close 100 KW to megawatt scale projects.
00:40:59.204 --> 00:41:06.657
I've developed a commercial solar accelerator to help installers exactly like you.
00:41:06.723 --> 00:41:26.508
Just go to cleanpowerhour.com click on strategy and book a call today. It's totally free with no obligation. Thanks for being a listener. I really appreciate you listening to the pod, and I'm Tim Montague, let's grow solar and storage. Go to clean power hour and click strategy today. Thanks so much.
00:41:26.574 --> 00:41:34.620
Unfortunately, we have to wrap this up. Check out all of our content at cleanpowerhour.com.
00:41:30.597 --> 00:41:50.843
Please tell a friend about the show. That's the single most important thing you can do to help others find the show. Tell a friend and check us out on YouTube, on Spotify, on Apple, we're all we're everywhere. And connect with me on LinkedIn. I love hearing from my listeners.
00:41:50.909 --> 00:42:03.374
You can book a meeting with me if you have some interesting thing you want to talk about, or if you're looking for consulting services, either in C&I solar or in AI and solar, I'm your guy.
00:42:03.440 --> 00:42:06.540
Where can our listeners find you? Juliana news,
00:42:08.640 --> 00:42:24.945
our website is Sparkhq.ai, and there's a link to book meetings with us or demos with us. And yeah, our emails are also our first name at Sparkhq.ai and similar to
00:42:25.720 --> 00:42:29.920
we're all around the internet. We're all around the interweb, so LinkedIn, whatnot, we're not hard to find.
00:42:29.920 --> 00:42:42.360
But the one thing that Julia said, we are spark hq.ai, not to be confused with other versions of it. So if you are looking for us, type in Spark HQ, and you'll reach the right place.
00:42:42.720 --> 00:42:52.981
Thanks for that distinction. I'm Tim Montague, let's grow solar and storage. I want to thank Julia Wu and Anuj Saigal for coming on the show.
00:42:49.342 --> 00:42:54.711
Thank you so much, guys. Thank you so much. Take care. Bye.
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