Promoting the sustainable adoption of rooftop solar across Singapore, Solar AI Technologies is transforming the renewable landscape, envisaging mass adoption of green energy amongst every household. Bolong Chew, the Co-founder and CEO of Solar AI Technologies, sheds light on the future of solar, while discussing the process and integration of their solar-as-a-service venture model.
With years of experience moulding new concepts into lucrative businesses, venture builder Bolong Chew went on to establish his own Singapore-based CleanTech startup, Solar AI Technologies. The former business contractor has aided multiple startups including Homage and Hype with scaling and expanding their solutions across the Southeast Asian region. Adding to his extensive portfolio, Bolong was formally employed as a consultant for Fortune 500, eventually embarking on his technopreneurial journey with ENGIE FACTORY, building sustainable solutions for the future.
Solar AI Technologies builds on ENGIE FACTORY’s vision to drive a zero-carbon economy through partnerships with entrepreneurs and startups. Incubated and seed-funded by ENGIE‘s venture arm, Solar AI Technologies integrates satellite imagery and advanced analytics to deliver its solar-as-a-service model with zero upfront costs. The startup educates and shifts consumers’ perceptions of rooftop solar to effectively tackle climate change and build a sustainable future.
Breaking barriers in the adoption of solar panels across Singapore, Bolong is committed to bringing his venture to the forefront of renewable energy in Southeast Asia and beyond, offering a cost effective, accessible, and transparent process for their customers. The Co-founder shares more on their rooftop solar deployment model, potential market expansion opportunities, and the future of solar.
Could you start off by telling us about yourself and your background prior to co-founding Solar AI Technologies?
I started working on Solar AI Technologies around two years ago. I first started out in management consulting, and thereafter went on to work with a venture builder, Hype, based in Hong Kong, [where] we worked on helping their market expansion. I spent a bit of time in Malaysia with a healthcare startup called Homage.
Generally speaking, as I was growing up, poverty alleviation and equal opportunity were really important causes to me, and about five years ago, I started to realise that climate change was actually real, and that most of those negative impacts ultimately affect people at the bottom of the pyramid. I shifted my focus to how I can try and chip away the problem of climate change.
Fast forward to three years ago, ENGIE FACTORY got in touch and I joined them full-time. They are the venture arm of ENGIE, a Fortune 500 French listed company. Traditionally, they started out in oil and gas, and in the last decade divested all their own gas assets and boosted their focus on energy efficiency.
Can you tell us more about the founding story of Solar AI?
I joined ENGIE Factory as an Entrepreneur in Residence, as the venture arm was looking to build new CleanTech ventures to tackle different problem areas they observed across the space with renewables, smart city, mobility, and energy efficiency being the four pillars I [assisted them] with venture building.
I started working on a couple of problem statements for rooftop solar, and around four months in, we built our first site and brought on our first data scientist. It was in the midst of the Covid-19 lockdown in 2020, and Ariel Fabiano was the data scientist I connected with through LinkedIn.
At the time, he was back in Argentina, and borders were closed and we never met him face to face but despite that we connected really well through Google Hangouts.
Initially I was supposed to build and validate the ventures, and once things start running, we would recruit founders in and then let them take it forward. [Since I had worked closely in the project], instead of hiring a founder, I proposed to convert my role into a full-time founder, and I started building on the business thereon.
What is your vision and mission for Solar AI?
Within the ClimateTech space, there are several solutions which are already matured, with solar and wind as the two prime examples, and the cost of these renewables is significantly lower than traditional fossil fuels. It poses the question- if that is the case, then we should adopt and expand them as dominant energy sources within the industry, but rooftop solar penetration is still less than 1% at this point in time globally.
A big part of the reason is lack of trust and awareness of how these technologies work. So, starting out, that was a major part of what we were working on. In terms of building trust and awareness, we started to put out a lot of content with about 15,000 monthly readers across Southeast Asia.
Being the first of its kind in Singapore, the founder emphasised breaking the high-cost stigma around renewable energies, launching a zero upfront cost rooftop solar-as-a-service model for its residential customers, calling it their “rent-to-own model.”
“It’s where we deploy and maintain the solar systems, and in return charge them a flat monthly fee, which is [significantly] lower than their electricity bill savings,” added Bolong.
What services and solutions does Solar AI offer?
While we have evolved [gradually] as a business, we initially started out as an aggregator- a close comparison service for customers looking for rooftop solar options. We advise them to get the best and we try to educate them as much as possible.
Our customer segment is rather unique in a way as we target residential customers and homeowners. While many have [sufficient] capital in their banks they may still not move ahead as they do not have fundamental trust in it.
That is why we launched this as a tester in the market, as adoption is several times higher than if we proposed a direct purchase. That reflects a sense of confidence and validation, which not only addresses the issue of high upfront cost, but also whether it’s a viable risk-free solar [solution] over the long term.
We want to try and use rooftop solar as a way to democratise clean energy access beyond Singapore.
What is the role of artificial intelligence in your product offerings?
We started out focused on AI and data science, since our initial offerings at that point in time was to identify the problem. One of the main issues within the industry is that the cost of sales is quite high, and solar companies were finding it difficult to acquire new customers because of their approach. You need first to find the right customers, educate them, then convert them.
So, we built a [number] of computer vision models, feeding in satellite imagery to identify the outlines of a building, and conduct some predictions on the material and the area available on the roof for solar deployment. We have a solar assessment tool on our website, [which allows you to] search any commercial properties in Singapore and see the building polygons, which contain the metadata of these. The main reason to build such a model was to make it easy for property owners to assess and move ahead with solar installations, giving them a good sense of how they could possibly benefit from solar energy.
Since then, our models are mature, and while still in our process, we are shifting away from that and [focusing on] building trust and awareness among our customers, and encouraging them to convert over to solar energy.
You mentioned that Solar AI developed an instant solar roof installation simulator and model, how does that work?
One can simply access the tool on our website, first enter your property address and give us a couple details and you will have an instant solar simulation on the size, the solar system of your property, and how much you will be able to save, and can instantly start assessing different offers available.
For customers interested, the next step is a digital survey. We allow customers a more self-serve [model], where the customer would take a picture of their metre box and upload their electricity bills, which typically a solar company does. Using all the given information, we are able to give them a quote, a revised price plan, which around 80% of the time [remains unchanged].
Finally, when customers feel they are ready to move ahead, we then schedule a site visit and use that as an opportunity to finalise everything and discuss how the panels are placed, and basically confirm the contract.
How does Solar AI intend to make this process more accessible and easy-to-implement for customers?
At this point in time, most of our tools are focused on first, streamlining the sales process, and keeping customers engaged to the point where they feel confident enough to move ahead. [Moreover], from the operational angle, these tools [allow] our contractor partners to be able to assess the details of different properties. We have digitised that process, and beyond the installation stages, a huge part of our work is around customer service and engagement.
Could you run us through Solar AI’s rent-to-own programme, and what is the process like?
The process is actually not quite different. Our customers, similarly, go through our solar system assessment.
Essentially, there are a couple criteria that we look at for shortlisted customers and whether or not they are applicable for the rent to own programme. Over time, we are looking to scale and try to streamline operations as we go along.
Similarly, without having to go down to the site, we can already provide them with a price step, with an even higher [accuracy rate] of up to 90%, and mostly do not have to bring any changes to the model. Once we confirm everything, the rest of the process is mostly online, such as signing contracts, and scheduling the installations. After the installations are turned on and [working] for a month, the customers will start paying a flat-monthly fee, which is [significantly] lower than their electricity bill savings.
How long does the process take normally?
To install a residential rooftop solar system, it can be rather quick. It can be wrapped up within three to seven days, and customers can instantly turn on the system and start using the solar energy.
There are some nuances around that, for example, in Singapore once you have a solar system installed, individuals are required to fill out a formal application to the local grid provider, Singapore Power. Due to their backlog, the process could take around a month after the solar panels are installed, which is often outside of our control.
Since its launch, what is the estimated impact of Solar AI’s rent-to-own initiative across Singapore?
Since the launch around two months ago, we have signed more than 20 contracts so far. For each of our customers, the average residential system size for the rental programme is around 14 kilowatt peak. As a high level comparison, it will generate about 1,400 kilowatts of electricity, which for most of our customers contributes to about 70% of their household energy consumption, where some customers could even generate up to 100% or more of what they consume.
Within those 20 projects, we have successfully installed around 10, generally due to the quick installation process.
What were some of the initial challenges and struggles endured when Solar AI was first starting out?
A lot of ClimateTech solutions are generally capital intensive, and solar is no different.
In the industry, you realise that when you have large-scale projects which are matured, and generate a grand amount of cash flow, banks [are willing to deploy more] capital. However, when you have new projects which are small scale, [similar to us], it gets quite difficult to find financing and investments into these segments.
We got lucky in that regard, and have three investors that we brought in. Cumulatively, they have earmarked around SG$4.5 million for deployment of capital expenditure for our solar projects as a pilot phase. We are currently using that capital to grow and scale our projects.
How has Solar AI addressed these issues within the industry and has since overcome those challenges?
A big part is really to build credibility over time. As before the rental models, we had been working on residential rooftop solar projects, and had more than 50 projects, which for us is a direct model proving to financiers that we are operational, and are able to build and run these projects, which effectively solve a bit of that equation.
The good thing is when working on the homeowner segment in Singapore, the overall credit ratings are quite good.
What does the future hold for Solar AI?
Our plan is ultimately to be a regional player and drive as much rooftop solar adoption as possible. In the coming 12 months, our intent is really to streamline operations within Singapore, and being able to really push market adoption, as the market leaders within the solar-as-a-service segment. Then, start to take these models into new markets.
We currently have sights on the Philippines and Malaysia, as the immediate lending markets [with hopes to], continue to move into new markets from there.
How can interested parties contact you for consultation and installation?
The best place to go to is our website, where everyone can try out our stimulators and instant solar assessments. Potential partners can also reach out to us via our website.
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