SPACSphere Cites $16M–$20M in 2026 Recurring Revenue

Datta said going public is intended to support machine-learning hiring, computing capacity and acquisitions of data assets and teams.

Rhea-AI Filing Summary

SPACSphere Acquisition Corp. and Mobilewalla Holdco, Inc. discussed their proposed $250 million business combination. SPACSphere Chairman and CEO Bala Padmakumar said Mobilewalla’s 2026 ARR was forecast at $16 million to $20 million in the S-4. He described a close before year-end as an “outside chance,” with early next year as the alternative.

Mobilewalla CEO Anindya Datta said its platform receives about 50 terabytes of data per day and compresses stored data to 17 petabytes, with monthly Amazon storage spending of $150,000. He said going public is intended to support machine-learning hiring, computing capacity, and acquisitions of data assets and teams.

Filing Explained

Although Padmakumar says the deal “closed” quickly, this filing still calls the combination proposed: the S-4 contains a preliminary proxy, a definitive proxy and shareholder vote remain ahead, and regulatory approvals are closing conditions; no completed change for common holders is reported.

Key Figures

Proposed business combination: $250 million SPAC funds: $172 million 2026 ARR forecast: $16 million to $20 million +3 more

6 metrics

Proposed business combination $250 million Amount cited for the proposed combination

SPAC funds $172 million Amount Padmakumar said was in SPACSphere

2026 ARR forecast $16 million to $20 million Mobilewalla forecast cited by Padmakumar

Daily data inflow About 50 terabytes per day Mobilewalla data platform, as described by Datta

Compressed data volume 17 petabytes Volume Datta said Mobilewalla compresses its data to

Monthly Amazon storage spending $150,000 per month Amount Datta said Mobilewalla spends

Key Terms

Agentic AI, ARR, data lake, de-SPAC, +1 more

5 terms

Agentic AI technical

"Agentic AI systems"

Agentic AI refers to computer systems that can make their own decisions and take actions without needing someone to tell them what to do each time. It's like giving a robot a degree of independence to solve problems or achieve goals on its own, which matters because it could change how we work and interact with technology in everyday life.

ARR financial

"an ARR of between $16 and $20 million for 2026"

ARR, or Annual Recurring Revenue, is the predictable income a business expects to earn each year from ongoing customer subscriptions or contracts. It’s like a steady paycheck that shows the company's ability to generate consistent revenue over time, helping investors assess its stability and growth potential. ARR provides a clear picture of how well a company is performing in building long-term customer relationships.

data lake technical

"the output of the data platform is a very large data lake"

A data lake is a large, centralized storage system that holds raw digital information in its original form — documents, spreadsheets, sensor logs, images, and more — rather than forcing everything into a neat structure first. For investors, a well-run data lake is like a company’s research library: it can speed product development, improve forecasting and risk detection, and support better decisions, but it also requires disciplined management to avoid becoming disorganized and costly.

de-SPAC financial

"the de-SPAC and going public"

A de-spac occurs when a company that was created through a special type of public listing, called a SPAC, officially becomes a regular publicly traded company. This process is similar to a startup moving out of its temporary workspace into a permanent office, allowing investors to see the company's true value and operations. For investors, de-spacs are important because they mark the transition to a more established company, often leading to clearer financial information and investment opportunities.

longitudinal behavioral asset technical

"build this very, very long longitudinal behavioral asset"

FAQ

AI-generated questions and answers. How Rhea-AI works. Not financial advice.

What amount was cited for the SPACSphere and Mobilewalla combination?

The proposed business combination was described as $250 million.

What ARR is Mobilewalla forecasting for 2026?

SPACSphere Chairman and CEO Bala Padmakumar said Mobilewalla’s S-4 forecast ARR of $16 million to $20 million for 2026.

How many machine-learning engineers does Mobilewalla plan to hire?

Datta said going public should enable Mobilewalla to hire 15 machine-learning engineers; he said each would probably cost $500,000 to $700,000 a year.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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Available on EDGAR 09/24/2026 - 05:24 PM Accepted by SEC EDGAR 09/24/2026 - 05:21 PM Learn about SEC filing dates

Filed by SPACSphere Acquisition Corp. pursuant to Rule 425

under the Securities Act of 1933, as amended,

and deemed filed pursuant to Rule 14a-12

under the Securities Exchange Act of 1934, as amended

Subject Company: SPACSphere Acquisition Corp. (File No. 001-43093)

The following is a podcast transcript where Anindya Datta, the Chief Executive Officer of Mobilewalla Holdco, Inc. (“Mobilewalla”), and Bala Padmakumar, the Chairman and Chief Executive Officer of SPACSphere Acquisition Corp. (“SPACSphere”), appeared as guests on an episode of SPACInsider, a podcast hosted by Nick Clayton, for a discussion about the proposed business combination between Mobilewalla and SPACSphere.

The transcript was generated using automated transcription tools and while effort has been made to provide an accurate transcription, there may be typographical mistakes, inaudible statements, errors, inaccuracies or omissions in the transcript. Neither Mobilewalla nor SPACSphere believe that these are material.

Mobilewalla Podcast Interview by SPACInsider

Podcast Episode Transcript

September 24, 2026

PRESENTATION

Nick Clayton

Hello and welcome to another SPACInsider podcast, where we bring an independent eye in interviewing the targets of SPAC transactions and their SPAC partners. Agentic AI is here and working in a number of industries, but the question of which model can win is increasingly defined by who has the data to back it up. I’m Nick Clayton, and this week, I speak with Anindya Datta, CEO of Mobilewalla, and Bala Padmakumar, CEO and Chairman of SPACSphere Acquisition Corp. The two announced a $250 million combination in June. Anindya explains how Mobilewalla’s long tail of data forms the backbone of its own AI offerings, and how it has innovated internally to give it an edge. Bala gets into why he sees Mobilewalla as standing above the noise in the AI space and how SPACSphere concluded it was ready for the public markets. Take a listen.

And so, Anindya, Mobilewalla was founded in 2012. I imagine so much has changed in the time in between. Do you have a quick version of the story of how Mobilewalla has changed, and how it’s modified its approach and solving problems for clients with all that technological change over that time?

Anindya Datta

Yes, super interesting questions, and takes me back in time. So to understand how Mobilewalla started, I’ll take you back to my background. I started my career as a practicing computer scientist, as a sort of young faculty member at Georgia Tech, in computer science.

And my computer science work and research was in AI. When I started my career in the late 90s, early 2000s, AI was not sexy at all. In fact, listeners who were in the same boat as me would empathize with me that people would make fun of AI — other computer science groups would say that nothing is ever going to come of AI, you guys are gonna build toy systems forever. That situation has now changed.

My work was always at the intersection of AI and data — the impact of data on AI. And to tell you how Mobilewalla started, let me just give this sort of preamble: all AI is basically, regardless of what flavor of AI — these days we talk of generative AI, which is really a tiny, tiny part of AI — all AI, machine learning, neural networks, any flavor, is basically a marriage between a technique and data. You have a technique, you have an algorithm, and you train it on some data, and this data is really a manifestation of some period of history — rainfall in Georgia, or mango production in the Philippines, or whatever. You take this data that manifests history over a period of time, and you apply this technique or algorithm on it, and what it does is that it finds patterns in the data that repeat. And then once it has found those, it basically looks for those patterns in ongoing stuff, and it predicts, like, next year mango production is going to be good or bad, or next week how much rainfall is going to be there in Atlanta. All AI, all prediction, at a very abstract level, is a marriage between algorithm and data.

My work was focused on, sort of, the interrelationship between these two. When you build an AI model, you build it by applying an algorithm on data and what are the relative impacts of data and algorithm on that model — which is more important. In the field of AI for 40 years, every practitioner, every researcher chased the algorithm, because it was believed that better algorithms are going to lead to better predictions — the reason that predictions were not good was thought to be that the techniques being applied were not powerful. And you’ll see that one of my theses was that perhaps that was not the case. Perhaps data is more important than what we believed.

So now, the consensus in every major AI lab, whether you go to MIT, or Stanford, or Google Lab, or Anthropic Lab, is that data is the dominant partner. The reason that a simple image recognition algorithm — you can feed it a picture of a cat, and it’ll tell you it’s a cat — is not because innately the algorithm is very superior. Very simple algorithms are going to do this. It’s because you’ve trained it on a trillion cat pictures. So a lot of my thesis was that data is really important to predictive science.

So, Mobilewalla was founded on that basis. My bet was simple — the bet Mobilewalla was making was simple. If better data beats better algorithms, let’s build a data asset first — build a solid, solid, solid data asset — and build the kind of data that nobody can catch up to. Now of course data could mean a lot of things. All my work, for a long time we were looking at consumer data — so that’s the kind of data we’re looking at, how people behave in different places. We just said that let’s build an asset that captures consumer behavior over a long time.

And right when we were starting — 2013, 2014 — mobile apps were getting really, really, really powerful. And mobile apps represented some of the most instrumented technology of all times, and there was a lot of data that was coming out of mobile apps. So basically we were lucky and we said: let’s go collect how people behave, and there was this highly instrumented entity that was capturing how people behave.

The thesis was to build this very, very long longitudinal behavioral asset. And the second part of the thesis is: it’s not just build a data asset first, but build an asset that nobody can copy. Because you can copy an algorithm, sort of, on a weekend. But you cannot copy time. When you capture data over 10 years — a decade of longitudinal behavioral data, the same mobile devices observed continuously across 40-plus countries — it is literally uncompressible. If a competitor started today with unlimited money, in 10 years they would still be 10 years behind, because we would have collected 10 years more data.

So that’s how Mobilewalla started, and that’s what we sort of started quietly building.

Now, the industry, of course, arrived at the same point. There’s the belief that data is the dominant partner in AI — there’s consensus on that now. But the exact answer to your question was that Mobilewalla remained sort of a little science experiment-y over some period of time, because we were collecting data without quite knowing all the cool stuff we’d eventually going to be able to do with it. But we had to generate revenue — I was lucky in the sense of having good investors, because I’d already made them money before, and they were tolerant of my science experiment-y kind of technique. Initially, Mobilewalla started by selling insights from the data that we were collecting. And it was pretty easy, because we were getting consumer data over long periods of time, we were figuring out where people visit, and what kind of people visit what locations, and so on. And we were selling insights like this primarily into the advertising ecosystem as we were building the underlying asset.

But eventually what happened was that we got to a point where we had enough history to be able to start making predictions. So that was the first kind of… and I wouldn’t really say it’s a pivot, because it was… the underlying thing didn’t change. We said that, okay, now instead of selling data, we can actually sell features — features is also data, but features is predictive data. For instance, we were selling data into telecom companies and telecom companies predict churn. And we figured out that something that is a very strong predictor of churn is something that we call carrier heterogeneity in households — meaning if you are an AT&T subscriber and you lived with all AT&T subscribers, you are less likely to leave AT&T than if you lived with non-AT&T subscribers in your household as well. So we saw that we could make these features from the data that we are collecting because we had enough history. We started making these features, we started selling these features. And then came the third incarnation, which is where we are now.

We said: instead of selling predictive features to people who are building AI systems, why don’t we rise up the value chain and build AI systems ourselves? And the recognition at that time was that the type of AI we all know — the Gen AI we all know, which is the foundational LLMs, like your GPT, like Claude — they operate on publicly available data. You cannot go and ask them questions that need proprietary data, which we had. So we said: let’s now rise up the value stack, and instead of selling the data that we are building, let’s build actual predictive systems that can operate on that data. And that brings us to today and vertical AI.

Nick Clayton

Yeah, and I want to get into some of the more of the details of that as well. I want to get Bala in here as well. Bala, with SPACSphere, it appears that your team were looking at a variety of industries with your initial target search. How did Mobilewalla and this opportunity around Agentic AI specifically come up in your process?

Bala Padmakumar

As you indicated, yes, we were looking at a variety of industries, though very clearly either directly AI or AI-adjacent. We were looking at Agentic AI systems. I had certainly seen a lot of opportunities in healthcare. We had seen a lot of opportunities in energy and infrastructure support — data centers, small modular reactors, cooling systems, battery systems that support the power architectures that are required for today’s data centers. Anindya and I were introduced in February of this year, about a couple of weeks after the IPO.

Why this deal came together so quickly was that not only did it check all the boxes that we were looking for — it was an Agentic AI system, it was a product that was launched, it was revenue-ready — they had a team that was highly credible, in both Anindya and his broader team with Jay and Laurie and Mahir and Chandra and the rest of the team. But what really made the entire deal come together so quickly was the support from his investors was there — his investors were not passive in the process, they were actively involved in the process and continue to support the company, not just through the de-SPAC, but post-de-SPAC. That was a significant difference from, frankly, every other SPAC process I’ve been involved with.

And secondarily, what Anindya has is marquee customers. His customer base are top-tier telecom industry customers in the United States and in places in the Far East. And most significantly, they had expressed an interest, potentially, in being part of the process too. So we had support from all of the interested parties Anindya was bringing to the table, which was fairly unique and gave us a degree of comfort that this is a deal that’s going to close, and not only close, but it’s also going to trade well post-de-SPAC, given the revenue opportunities.

We have come to the table and closed, frankly, much quicker than the average SPAC. We are expecting comments back from the SEC this week on our first round, and we are six months post-IPO. So, if things go well, there is an outside chance that we will actually close the deal before the end of the year. Otherwise, it’ll go to early next year. That is our hope right now.

Nick Clayton

Great, and I want to dive into the deal a bit as well, but I did want to bring things back to Mobilewalla for a bit, as you were talking about there, Anindya, about how really the data is the foundation of these AI companies that are now coming to the fore. Could you talk a little bit about how you’ve been able to form your own proprietary data set, and how are you able to continue to scale that as you go along?

Anindya Datta

Great question. So if you look at the Mobilewalla product architecture, at a high level, Mobilewalla products look like this: we have multiple products, but at the base of all our products is this thing that we call the Mobilewalla Data Platform. And on top of the platform, we have products in verticals — right now we’re in three verticals: telecom, consumer lending, and consumer data solutions. Each of these verticals has multiple products. But really, the question you’re asking has to do with the base. All of these basically rest on this base called the Mobilewalla Data Platform.

You can think of the Mobilewalla Data Platform as a machine that ingests commodity data from a variety of sources — the mobile device ecosystem, SDK providers, the RTB ecosystem, commonly available household data sets. Basically, all commodity data that anybody with money can buy. And then we run it through a proprietary computational process to produce the eventual artifacts that we sell. And right now, the easiest way to understand — I’ll give you both sides and the middle.

First, talking about what we produce. You can think of the Mobilewalla Data Platform, the output of the data platform is a very large data lake, and the data lake has thousands of attributes. Each of those attributes you can think of as a predictor of something. These attributes are now consumed by us in building our applications, but for a long time, and even now to some extent, they’re consumed by enterprises that are looking to do certain things — telecom companies looking to predict churn, emerging market lenders that are looking to lend to someone with absolutely no credit footprint, so they need to assess their risk, and so on and so forth. So, so…

And the left side of the platform is the data that we buy, which is then transformed into these features or these predictive data items. The data that we buy is all commodity, but what’s interesting to mention is that we ingest it in large quantities — we get about 50 terabytes a day of data flowing into the Mobilewalla ecosystem. And this data, the SDK stuff, the RTB stuff, is very noisy — it’s full of fraud. So this middle layer, the compute process does really two things. Well, three things, but the third thing is actually the algorithms to produce the eventual data items. If you leave that aside, it has two sort of key steps. One is, it defrauds. Because there is so much bad stuff in it — you might get a record that says the device is in this location, but in the majority of the cases, that location value is fraudulent. People just put it in. So we do a lot of denoising of the data, and there’s a lot of proprietary AI in that as well. So that’s one thing we do. We do a lot of denoising.

The other thing we do that’s basically a competitive moat for us. As I told you, we are getting 50 terabytes of data a day, so you can imagine, once you get this over now almost 11 years, the underlying raw data tonnage becomes very large. So if you were to multiply 50 terabytes a day by 365 days a year, by 11, 12 years, you’ll see that you exceed — it’s almost exabyte scale, you exceed 500 petabytes. And we need to store it, because at the end it’s this history that allows us to do all the cool stuff that we eventually do, to find patterns. But the economics of storage gets very complex. We use very cheap storage on commercial cloud. Even on that, 500 petabytes of data is going to cost you many millions of dollars a month — like 6, 7, 8 millions of dollars a month. That just is not practical for any startup or really any company to be able to pay that. So that’s one of the base impediments to building what we build.

So what we did was: a few years ago, one of my PhD students designed a new class of compression techniques. We all know compression — we use things like zip, GZIP, to send files and pictures, to compress. A typical zip will give you 2-to-1 compression at best. We are getting 10-to-1 to 25-to-1 compression, taking advantage of the type of data we are getting, the structure and semantics and all that. That gives you tremendous leverage — instead of 536 petabytes of data, we are compressing it down to 17 petabytes, and instead of paying $6 million a month, we are spending $150,000 a month on Amazon. So that’s a very key moat. You know, even if you give somebody 10 years, 20 years to collect data, just keeping it around becomes economically very complex.

So that’s what we do. We buy tons of commodity consumer data — literally that anybody can buy — but we buy that in large quantities. We process it such that we reduce noise from it — and I think we do that reduction better than anybody else — and then we can bring it down to manageable size so the economics work to process it. And then we run our proprietary techniques to produce the features that I spoke about. And the way we keep it fresh is because we are continuously getting new data.

The other thing I’ll end by saying is that there is also a limitation of the type of data that we get. There are other types of data that we do not get that we would like to. So one of the reasons for joining hands with Bala and going through the SPAC process, and not a classic fundraise — which is what I’ve always done privately in my career building companies — is that we would like to acquire companies that have very unique data sets, and we know many exist. So one of the things that I’m looking forward to is bringing in new kinds of data sets that are not purchasable, because it’s unique these companies — both augmenting and enriching the semantics of the data that we have, and the semantics of the history that we have, such that we can impart even more power to the top-level vertical engines that we are building.

Nick Clayton

Yeah, and so talking about arriving at the deal — what was the main factor that made you decide that now was the time for Mobilewalla to go public, and that the SPAC option was going to be the right route for you?

Anindya Datta

Yeah, so for the first time, I’m going to give you somewhat of a hand-wavy answer, okay? I did not know, Nick, that I wanted to go public. In fact, going public was the farthest from my mind. If you look at my background, I’ve always raised money from pretty Tier 1 Venture Capitalists and Private Equity funds — I’ve sold a company to another large company and so on. But at Mobilewalla, it’s the first company where I have not raised a lot of money. It was consciously done — to be able to do things at modest budgets. And it took us some time, but we are at a point where I believe that we are at the inflection point where we can scale very fast.

And I truly believe that the next — I’m going to use a cliché phrase — the next trillion-dollar opportunity in AI is not in horizontal AI. The foundational LLMs are there — there is no play in building — and there are plenty of open-weight models that are catching up to them. You probably saw Nvidia acquiring Hugging Face for $13 billion a couple of days ago. Foundational LLMs, in many ways, that’s not the opportunity anymore. The opportunity is much like what happened in the software business — if you look at how the software business developed, the first companies that got big were the foundational tech companies: database systems, messaging systems, Sybase, Oracle, Tibco, MQ. But then, companies realized that, hey, I cannot just buy Oracle licenses and Tibco licenses and build a payroll system — it just doesn’t work that way. So then came the next wave of companies that got much bigger — like SAP, like PeopleSoft, Siebel, now Salesforce — that built basically these vertical enterprise stacks on top of these foundational tech companies, that allowed companies to solve operational problems. I think AI is going through the same arc as well. Your Anthropic and your OpenAI are the Oracles and Sybases and Tibcos of AI — they’re setting up the foundational tech without which nothing is going to happen, but companies still need to use these to solve company problems. And I think we are at the vanguard — so you can think of us like the SAP of AI, or the PeopleSoft of AI. We are building enterprise stacks with proprietary data on top of these foundational LLMs, and we are at a point, Nick, where I feel we have proven out a couple of key theses: that proprietary data can do things that horizontal engines cannot do, and you need specialized computer science in the stacks as well.

And now is the time to scale. So how does one scale? I knew I wanted to raise capital. In software systems, it’s pretty much a technology build. Unfortunately, in AI, it’s largely a data build — you need domain expertise, you need data, and the cash requirements to grow are a lot more. So eventually, going public offered us a faster avenue to scale. I want to scale 3X to 4X a year over the next couple of years, and it’s not possible to do that without inorganic means. And I was convinced by one of our investors that this is a way we should look seriously. So we looked at it, and quite honestly, I was talking to SPACs and looking at RTOs. But for me, having come from, sort of, this Tier 1 VC ecosystem, the small-cap go-public ecosystem was a little weird, because the investors were of a different flavor and so on. Then I met Bala. To be honest, the reason I did SPACSphere was, we were already doing something with another SPAC earlier and walked away from that. I thought Bala was one of the first really knowledgeable, really solid SPAC CEOs I met, and felt that there could be a fantastic partnership here. And there were things — I mean, I knew how to build companies. And he knew how to navigate the go-public ecosystem. And I felt that that was, sort of, again using a cliche phrase, a marriage made in heaven. So, to grow fast, why go public? I told you. And why SPACSphere? Is basically, I met Bala and fell in love with it.

Nick Clayton

Right, and Bala, on your side of that, you and your team have done deals in really all the different corners of this space — software, hardware, and now coming into AI. I’m interested in how you see the market, and also doing a deal right now has got to be an interesting time just because, in terms of the valuation question, with these mega IPOs and all these other things swirling around? It seems like the landscape is constantly changing, but how did you approach these questions as you were engaging in your side of the marriage?

Bala Padmakumar

Absolutely. Among the biggest challenges that we perhaps had — which we needed to grapple with as a SPAC — was in fact valuation. To your point, some of the valuations we are seeing in the private world are just outlandish — completely outlandish. The most egregious one that I saw was a company that was generating $10 million in revenue that was valued at over a billion dollars. And of course, you’ve got all the other major ones — Cursor and all the other folks who are valued at 60, 70 times ARR.

The other issue that we obviously have in the SPAC world is that we can’t reduce valuations too much for two reasons. Number one, it’s driven by the SPAC size — we have $172 million in the SPAC, and the SPAC has to be a reasonable size of the surviving entity. And secondarily, given how SPACs have traded post-de-SPAC, we wanted to stay well away from the $50 million or so market cap that a SPAC can fall to, and then run into delisting territory, in the event that the stock does momentarily fall to some point.

So, in addition to that, given the new rules, we did not use any forecasting. All the valuation was done only on 2026 numbers.

We came up with an analysis that said that valuations in the private sector were around a low of about 20 to 25 to a high of about 60 to 70. And Anindya’s 2023 to 2025 numbers were about 14. And we set up a number at about 250, which basically set up a trailing valuation of about 18 to 20. And we have forecast in the S-4 that Mobilewalla is looking at an ARR of between $16 and $20 million for 2026, which will put the valuation somewhere between 12 and 15. So we felt that both those numbers were eminently defensible, and a significant discount to the numbers that we were seeing in the private sector. There were no really direct comps to what Anindya was doing in the public sector. So we kind of felt that, from that perspective, it was a defendable deal, it was an attractive deal.

In fact, one of the big pushbacks that I got from my board as to whether this was a deal that we should take was the fact that the revenue was on the light side. We had initially set a target of a revenue range — between $30 and $70 million would have been an ideal target. But given all the other positives that we talked about in terms of investor support, marquee customers, and customer engagement, we felt that that balanced out the issue of the revenue being lower than we would like.

Now, with respect to the other issue of the market segment — my previous SPACs were largely focused on the energy space, because that’s my core domain expertise. If you take away my SPAC and my public equity expertise, back in the day when I used to be a technologist and an engineer, I came up through the energy world — I came up through generation, through storage. And certainly given the issues that all the data centers are facing today, those are substantive issues — especially transient conditions that have got to be supported as data centers switch from one source of energy to another. We had looked at battery companies, we had looked at high-power supercapacitor companies, we had looked at, as I said healthcare, and we had seen multiple healthcare opportunities pre-de-SPAC — I mean, pre-IPO. And that was certainly a point of interest. So when Anindya came out, frankly, out of left field to us, it just clicked. We had an LOI in place 7 weeks after the IPO. We think that the deal just fell into place, and I think it’s moved along very well in the months since.

Anindya Datta

For people like me, the question is not: is AI going to be a bubble, or is AI going to work? I’ve spent my life on AI, and I think AI is absolutely going to work. The real question for me is where is the value of AI going to accrue next. That’s the question. And I firmly believe it is not going to accrue in horizontal foundational models anymore. That game is over. I truly think the reason AI is going to be big is because everyone, and because I work in always in selling things to companies, big, small, I think that AI is going to get pervasive in companies - people are going to do operations using AI - but for that to happen, more stuff needs to happen: proprietary data, domain expertise has to be developed, and the stack itself, because current technology for doing things like retrieval and orchestration is not going to work in vertical AI. And I truly think that we are at the absolute frontier, at the vanguard of that movement.

Nick Clayton

Right, and so Anindya, now that you’re moving ahead and becoming a public company is now very much on the horizon, what is the thing you’re most excited about being able to do as a public company to continue expanding Mobilewalla’s offerings and footprint?

Anindya Datta

Yeah, so here, Nick, I think very much like an engineer and technologist founder. So the thing I’m most excited about — the belief that drives me every day, which unfortunately is not immediately going to happen now — is that when enterprises use AI, which I’m going to call vertical AI, because telecom companies are going to ask telecom questions, and retail companies are going to ask retail questions, and insurance companies are going to ask insurance questions — the belief that drives me is that there are a couple of very nerdy reasons why you just cannot — let’s say you are a large insurance company, and you wanted to build an insurance AI application, where you can ask things like: when we wrote policies for these two people and they look exactly the same, why is this person turning out to be much riskier than others? The insurance company, of course, has proprietary data. You just cannot take a foundational LLM, like a GPT, or a Claude, or a Hugging Face model, just train it on the proprietary data and build it. There are some very nerdy reasons why that cannot happen. You need this other stack — other computational capabilities on top. And the belief that drives me is that we are one of the first to recognize that problem. It’s a hard computer science problem. And we are the first to actually have Gen 1 solutions, and hopefully we’ll have very robust solutions in the next year and a half. And in my mind, that is the trillion dollar play — when you launch that stack that anybody can take and build applications.

So what I look forward most to, Nick, is to build that stack. And the goal is that we should be able to launch that stack, and early thoughts are to do open source — I’m not sure about that yet.

And what the de-SPAC and going public should enable us to do is to put the pieces together to make that happen. It should enable us to hire the 15 incredible ML engineers that we need, each of whom are going to cost probably half a million to $700,000 a year. It’s going to allow us to put together enough compute capacity — either in the cloud, or, I don’t know, we are thinking of going away from commercial clouds now, but it’s still expensive. To be able to test out the thesis — because basically the biggest thesis we have to test out is that the stack works across domains. And in a bunch of domains there are public data available, so we would like to be able to get that data and run tests — that’s expensive. And all the while doing that, have the resources to be able to still grow the company through some organic sales — like signing up every major telecom company in the world for instance — but largely through inorganic means, acquiring interesting data and teams. So that’s what I look forward to, Nick — to build a stack, to build that fundamentally disruptive piece of technology, which I think, whether we do it or not, somebody’s gonna do it. And to have the resources to get there by growing the company during the time we are doing it, and putting the resources together to make that happen.

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Forward-Looking Statements

This communication includes “forward- looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended (the “Securities Act”) and Section 21E of the Securities Exchange Act of 1934, as amended (the “Exchange Act”). All statements, other than statements of present or historical fact included herein, regarding the proposed business combination, SSAC’s and Mobilewalla’s ability to consummate the transaction, the benefits of the transaction, SSAC’s and Mobilewalla’s future financial performance following the transaction, as well as SSAC’s and Mobilewalla’s strategy, future operations, financial position, estimated revenues and losses, projected costs, prospects, plans and objectives of management are forward-looking statements. When used herein, including any oral statements made in connection herewith, the words “could,” “should,” “will,” “may,” “believe,” “anticipate,” “intend,” “estimate,” “expect,” “project,” the negative of such terms and other similar expressions are intended to identify forward-looking statements, although not all forward-looking statements contain such identifying words.

These forward-looking statements are based on SSAC’s and Mobilewalla’s management’s current expectations and assumptions about future events and are based on currently available information as to the outcome and timing of future events. SSAC and Mobilewalla caution you that these forward-looking statements are subject to risks and uncertainties, most of which are difficult to predict and many of which are beyond the control of SSAC and Mobilewalla. These risks include, but are not limited to, (i) the risk that the proposed business combination may not be completed in a timely manner or at all, which may adversely affect the price of SSAC securities; (ii) the risk that the proposed business combination may not be completed by SSAC’s business combination deadline and the potential failure to obtain an extension of the business combination deadline if sought by SSAC; (iii) the failure to satisfy the conditions to the consummation of the proposed business combination, including the approval of the proposed business combination by SSAC’s shareholders and Mobilewalla’s stockholders, and the receipt of certain governmental and regulatory approvals; (iv) the effect of the announcement or pendency of the proposed business combination on Mobilewalla’s business relationships, performance, and business generally; (v) risks that the proposed business combination disrupts current plans of Mobilewalla and potential difficulties in Mobilewalla’s employee retention as a result of the proposed business combination; (vi) the outcome of any legal proceedings that may be instituted against SSAC or Mobilewalla related to the agreement and the proposed business combination; (vii) changes to the proposed structure of the business combination that may be required or appropriate as a result of applicable laws or regulations or as a condition to obtaining regulatory approval of the business combination (viii) the ability to maintain the listing of SSAC’s securities on the Nasdaq; (ix) the price of SSAC’s securities, including volatility resulting from changes in the competitive and highly regulated industries in which Mobilewalla plans to operate, variations in performance across competitors, changes in laws and regulations affecting Mobilewalla’s business and changes in the combined capital structure; (x) the ability to implement business plans, forecasts, and other expectations after the completion of the proposed business combination, and identify and realize additional opportunities; (xi) the enforceability of Mobilewalla’s intellectual property, and the potential infringement on the intellectual property rights of others, cybersecurity risks or potential breaches of data security; (xii) the risk that Mobilewalla may never achieve or sustain profitability; (xiii) changes in the competitive and regulated industries in which Mobilewalla operates, variations in operating performance across competitors, changes in laws and regulations affecting Mobilewalla’s business and changes in the combined capital structure; (xiv) the impact of the U.S.-Iran war and other geopolitical conflicts, and (xv) other risks and uncertainties related to the transaction set forth in the sections entitled “Risk Factors” and “Cautionary Note Regarding Forward-Looking Statements” in SSAC’s prospectus relating to its initial public offering (File No. 333-290414) declared effective by the U.S. Securities and Exchange Commission (the “SEC”) on January 30, 2026 and other documents filed, or to be filed with the SEC by SSAC, including the Registration Statement, SSAC’s periodic filings with the SEC, including SSAC’s Annual Report on Form 10-K filed with the SEC on March 27, 2026 and any subsequently filed Quarterly Report on Form 10-Q. SSAC’s SEC filings are available publicly on the SEC’s website at http://www.sec.gov.

The foregoing list of factors is not exhaustive. There may be additional risks that neither SSAC nor Mobilewalla presently know or that SSAC or Mobilewalla currently believe are immaterial that could also cause actual results to differ from those contained in the forward-looking statements. You should carefully consider the foregoing factors and the other risks and uncertainties that are described in SSAC’s proxy statement contained in the registration statement on Form S-4 initially filed with the SEC on August 12, 2026 (the “Registration Statement”), including those under “Risk Factors” therein, and other documents filed by SSAC from time to time with the SEC. These filings identify and address other important risks and uncertainties that could cause actual events and results to differ materially from those contained in the forward-looking statements. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and SSAC and Mobilewalla assume no obligation and, except as required by law, do not intend to update or revise these forward-looking statements, whether as a result of new information, future events, or otherwise. Neither SSAC nor Mobilewalla gives any assurance that either SSAC or Mobilewalla will achieve its expectations.

Additional Information and Where to Find It

In connection with the proposed business combination between SSAC and Mobilewalla (the “Business Combination”), SSAC and Mobilewalla have jointly filed with the SEC a Registration Statement on Form S-4, which includes a preliminary prospectus and proxy statement of SSAC in connection with the Business Combination, referred to as a proxy statement/prospectus, and after the Registration Statement is declared effective, SSAC will mail a definitive proxy statement/prospectus relating to the Business Combination to its shareholders. This communication does not contain all the information that should be considered concerning the Business Combination and is not intended to form the basis of any investment decision or any other decision in respect of the Business Combination. SSAC may file other documents regarding the Business Combination with the SEC, and SSAC’s shareholders and other interested persons are advised to read, when available, the preliminary proxy statement/prospectus and the amendments thereto, the definitive proxy statement/prospectus and the other documents filed in connection with the Business Combination, as these materials will contain important information about Mobilewalla, SSAC and the Business Combination.

When available, the definitive proxy statement/prospectus and other relevant materials for the Business Combination will be mailed to shareholders of SSAC as of a record date to be established for voting on the Business Combination and the other matters to be voted upon at the meeting of SSAC’s shareholders to be held to approve the Business Combination and such other matters. Such shareholders will also be able to obtain copies of the preliminary proxy statement/prospectus, the definitive proxy statement/prospectus and other documents filed with the SEC, without charge, once available, at the SEC’s website at www.sec.gov, or by directing a request to SPACSphere Acquisition Corp., 8795 Folsom Blvd, Sacramento, California 95826, Attention: Soumen Das, Chief Financial Officer.

BEFORE MAKING ANY VOTING DECISION, INVESTORS AND SECURITY HOLDERS OF SSAC ARE URGED TO READ THE REGISTRATION STATEMENT, THE PROXY STATEMENT/PROSPECTUS AND AMENDMENTS THERETO, AND THE DEFINITIVE PROXY STATEMENT/PROSPECTUS IN CONNECTION WITH SSAC’S SOLICITATION OF PROXIES FOR ITS SHAREHOLDERS’ MEETING TO BE HELD TO APPROVE THE BUSINESS COMBINATION, AND ALL OTHER RELEVANT DOCUMENTS FILED OR THAT WILL BE FILED WITH THE SEC IN CONNECTION WITH THE BUSINESS COMBINATION AS THEY BECOME AVAILABLE BECAUSE THEY WILL CONTAIN IMPORTANT INFORMATION ABOUT SSAC, MOBILEWALLA AND THE BUSINESS COMBINATION.

INVESTMENT IN ANY SECURITIES DESCRIBED HEREIN HAS NOT BEEN APPROVED OR DISAPPROVED BY THE SEC OR ANY OTHER REGULATORY AUTHORITY, NOR HAS ANY AUTHORITY PASSED UPON OR ENDORSED THE MERITS OF THE BUSINESS COMBINATION OR THE ACCURACY OR ADEQUACY OF THE INFORMATION CONTAINED HEREIN. ANY REPRESENTATION TO THE CONTRARY IS A CRIMINAL OFFENSE.

Participants in the Solicitation

SSAC, Mobilewalla, and their respective directors, executive officers, other members of management, and employees, under SEC rules, may be deemed to be participants in the solicitation of proxies from SSAC’s shareholders in connection with the Business Combination. Information regarding the persons who may, under SEC rules, be deemed participants in the solicitation of SSAC’s shareholders in connection with the Business Combination, including the names of such persons and a description of their respective interests, is set forth in SSAC’s Annual Reports on Form 10-K, Quarterly Reports on Form 10-Q and Current Reports on Form 8-K. Additional information regarding the interests of those persons and other persons who may be deemed participants in the Business Combination may be obtained by reading the Registration Statement regarding the Business Combination when it becomes available. Shareholders will be able to obtain copies of the documents described in this paragraph that are filed with the SEC, once available, without charge at the SEC’s website at www.sec.gov, or by directing a request to SPACSphere Acquisition Corp., 8795 Folsom Blvd, Sacramento, California 95826, Attention: Soumen Das, Chief Financial Officer.

No Offer or Solicitation

This communication is not a proxy statement or solicitation of a proxy, consent or authorization with respect to any securities or in respect of the Business Combination and does not constitute an offer to sell or a solicitation of an offer to buy any securities of SSAC or Mobilewalla, nor shall there be any sale of any such securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of such state or jurisdiction. No offer of securities shall be made except by means of a prospectus meeting the requirements of the Securities Act.