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September 22, 2021


Josh Kinley—Chief Financial Officer, BigBear.ai

Reggie Brothers—Chief Executive Officer, BigBear.ai

Brian Frutchey—Chief Technology Officer, BigBear.ai

Raluca Dinu—GigCapital4 Chief Executive Officer


Good morning, and welcome to BigBear.ai’s Analyst Day! I would now like to turn the call over to Josh Kinley, Chief Financial Officer of BigBear.ai.

Mr. Kinley, please go ahead.


Good morning, everybody and welcome to BigBear.ai’s Analyst Day.

As he mentioned, my name is Josh Kinley, and I am the Chief Financial Officer for BigBear.ai.

We really appreciate the opportunity to spend some time with you this morning and introduce the company.

As you know, we are going through the process of becoming a publicly traded company via a merger with GigCapital4. And that was announced on June 4th.

We filed our amended proxy statement on September 17th and we expect to complete the transaction and begin trading on the New York Stock Exchange in the fourth quarter of ’21.

Moving to our presentation, on pages 2 and 3 we have included the customary safe harbor disclaimers.

I am not going to review all of these disclaimers in detail, but I do request that you take some time to review these pages at your convenience. I would like to point out the disclosure on forward-looking statements on page 2.

This document includes forward-looking statements within the meaning of the safe-harbor provisions of the United States Private Securities Litigation Reform Act of 1995.

Forward-looking statements may be identified with the use of words such as forecasts, intend, seek, target, anticipate, believe, expect, estimate, plan, outlook, and project, and other similar expressions that predict other future similar events or trends that are not statements of historical matters.

Such forward-looking statements with respect to revenues, earnings, performance, strategies, prospects, and other aspects of business such as GigCapital4, BigBear.ai, or the combined company at the completion of the business combination are based on current expectations that are subject to risk and uncertainties.



A number of actions can cause actual results of outcomes to differ materially from the indicated and such forward-looking statements.

With that taken care of, it is my pleasure to turn the call over to BigBear.ai’s Chief Executive Officer, Dr. Reggie Brothers.


Thanks, Josh. We appreciate and thank you all for joining us this morning. It’s great to be here, and we’re excited about our progress toward the completion of our merger with GigCapital4.

Let me start by telling you about my background. So, I’ve been immersed in technology my entire career. I earned my PhD at MIT in optical communications. From there I joined MIT Lincoln Laboratory where I was an Assistant group leader and designed radar and advanced communications systems. I then left Lincoln to join Envoy Networks. Envoy was a 3G wireless start-up, which was later acquired by Texas Instruments. I then developed an Advanced Communication group at Draper Laboratory before joining DARPA. After that I then went to work for BAE Systems. And there I was a Technical Director and Technical Fellow and that’s before I got a call, a very special call, from the White House to interview to be the Deputy Assistant Secretary of Defense for Research. In this role, I had oversight of the department’s entire Science & Technology budget of about $24 Billion. I then joined Department of Homeland Security as the Under Secretary to lead Science & Technology for that department. Now, since leaving DHS, I was a Principal at the Chertoff Group. And there I advised commercial technology companies on doing business with the government, the CTO of Peraton, which is now a $7 Billion defense contractor, and in June of last year I joined BigBear.ai as CEO.

I have five patents, I sit on the Visiting Committee of sponsored research at MIT and am a distinguished Fellow at Georgetown University and was recently honored with the Washington 100 Award for being one of the most influential leaders in the government market.

Now, joining me today from BigBear.ai are:

Brian Frutchey, our Chief Technology Officer. He’s got greater than 25 years of technology experience focusing on Big Data analytics for the defense community.

You just heard from Josh Kinley, our Chief Financial Officer, who also has more than two decades of relevant experience and was co-founder and formerly the CFO of PCI.

We also have Sean Battle, our Chief Strategy Officer, who will facilitate our Q&A session after our prepared remarks.

In addition, we are happy to have Dr. Raluca Dinu here with us who is CEO and President of our SPAC partner GigCaptial4.

The Gig team have been great partners and Raluca will cover the transaction details later in the presentation.

So let’s turn to our mission: Our world— We can think of our world as a battleground of complex data – and the question is what do you need to win on this battleground? You need to be able to apply your data to make decisions that get you better outcomes. And you need to make these decisions faster and better than your competitors. In sum, organizations need to be able to operationalize AI capabilities at scale. That’s what we do, that’s what we do here at BigBear.ai.



Next chart.

So in many ways, our equity story is quite simple: Companies and organizations, they’re literally drowning in data.

We provide transformative software and technology that helps customers operationalize AI to make sense of that data, to make better decisions based on the data they’re accumulated. We operate in a massive and fast growing addressable public and private sector market. We have a track record of growing customer relationships with new products and solutions. We have positive and accelerating revenue, gross margins, EBITDA and free cash flow, that’s fueled by a robust $500 million backlog.

Now here’s some additional context for you:

According to the IDC, the amount of data created over the next 3 years will be more than the data created over the past 30.

Now, this is complex, high stakes data – information that, for a defense customer, could be adversarial in nature, or for commercial customer, could be critical to gaining or maintaining a competitive advantage or ensuring the continuity of business operations. And the rate at which data is moving, well that’s accelerating – with nearly 5 billion active net internet users connected worldwide. Additionally, the growth in commercial space and Low Earth Orbiting Satellite constellations, the internet of things, and 5G, these will increase exponentially not just the volume, but the variety of data.

This all leads to increased cyber vulnerabilities, the proliferation of sophisticated cyber threats and the need for the convergence of cyber and analytics to develop next generation tools. There’s a huge need for AI tools that can be applied at enterprise scale. But applying these technologies at scale, that requires more than just algorithm development. Organizations need tools, they need tools to deal with this data tidal wave that can be effectively and efficiently integrated into existing infrastructure.

So since 1999, BigBear has enabled our customers to analyze data – even if it’s imperfect – to make faster and make better decisions. We use our hybrid cloud integration model, and AI driven exploratory analytics and anticipatory intelligence.

So today, we are delivering a suite of AI-powered technology that optimizes human decision making and integrates easily into customers’ existing technology. Our end-to-end software and technology transform raw data into knowledge. Importantly, the AI tools we provide, they augment the human decision-making processes, they do not eliminate the human – they augment the human.

Our unique offerings work in realistic data environments – in other words, our solutions are built to perform in real-time environments in which there is often less than perfect and/or highly complex data sets. And we operate in an attractive and fast-growing market.

So the broad AI/ML market is projected to grow at a CAGR of approximately 40% over the next 5 years and reach about $310 billion by 2026. And, there is rapidly growing demand in both the public and private sector.



We have a proven land and expand strategy. This is a track record of growing customer engagements.

Because our flexible solutions are implemented within our customers’ current decision-making frameworks, we can efficiently drive expansion and extension of engagements.

In addition to established, profitable customers in defense and intelligence, where we have long-lasting engagements, we are expanding into the federal, state and local government segments, as well as the commercial sector. So there are vast opportunities across many industry verticals, where leadership needs tracking, optimization and predictive and prescriptive tools to help handle and harness these mountains of data.

And as noted above, we are different than many companies going through the SPAC process given our strong and growing revenue, margins, adjusted EBITDA and free cash flow, plus our large backlog. We have an incredible team of employees, and our ability to transform raw data into focused action, well that’s unparalleled. So we believe our offerings, coupled with rapidly growing demand for AI and ML services, makes us an attractive investment opportunity priced with significant, significant upside potential.

Let’s go to the next chart.

So we’re trusted by our customers, in fact we’re more than trusted – we’re depended on by our customers, and that’s because we have been tested in critical applications in complex, real time environments. And here are three examples. So look in the upper left. We detected Russian forces in Crimea one month before anyone else. We detected fuel smuggling in Libya. And to the right, one of our products was used by US Central Command to shape our Iranian Engagement strategy to generate predictions of the results of various course of actions.

So fundamentally, we provide predictive and prescriptive analytics on whatever data is ingested and are now taking these products and applying them to commercial applications. At BigBear.ai, we provide projections that inform what decisions you should take to achieve your desired goals and your desired outcomes.

Now, we have three solutions for our customers: Observe, Orient and Dominate. We enable our customers to dominate their decision battlegrounds in critical national security environments, where decisions can mean life or death. These tools are entirely relevant and applicable to the commercial landscape, where they can enable leaders across all industries to make the best decisions faster.

Now for a quick snapshot, we’ve been in operation for 21 years.

Now we are projecting FY22 total revenue of $277 Million. And that equates to 61% net revenue growth. We have approximately $500 Million in contracted backlog and $4.5 Billion in near term pipeline opportunities. And on top of that we have a 93% historical contract win rate. 93 percent.

So I spoke earlier of how the volume of data is expanding.

Customers are spending billions of dollars amassing troves of data, and that increasing volume has actually slowed and complicated the decision-making process. Companies and organizations need a better sense of what data is important, how it relates to their operations, and what decisions will give them a competitive edge.



BigBear distills data and unearths insights. We deploy operationalized AI/ML to enable a better, more robust human decision-making process. Our tools have applicability across a range of different sectors translating into a large addressable market.

This global AI/ML market is projected to grow at a CAGR of approximately 40% to $310 billion by 2026. This is a huge market, which is a significant opportunity for us.

Two exciting commercial areas where our expansion is already underway are Maritime and Space.

We recently announced a transformational partnership with Virgin Orbit that will take us to the lead in the commercial space analytics market, as well as a joint effort with Redwire to establish a space cyber range capability. We’re also gaining traction to our expansion into the Transportation & Logistics, Energy and Retail verticals.

We see future expansion opportunities in infrastructure and media, as well as the Federal Civilian agencies.

Here we’ve highlighted our highly customizable capabilities, which fall into four buckets: data ingestion, enrichment and processing; cyber; AI and ML; and predictive analytics and visualization. We apply these capabilities in three broad applications: Location Intelligence, Maritime Intelligence and Media Intelligence.

For Location Intelligence, we provide situational awareness and impact analysis across multiple domains and on a global scale. This reduces surprises and informs decisions. This “command and control” application is needed in many industry verticals.

For Maritime Intelligence, our solutions allow customers to optimize their fleets, and get a leg up on their competition. This is the first step toward total logistics intelligence.

And the third, Media Intelligence. By working with BigBear.ai, brands can better understand the drivers of sentiment and develop new offerings or products based on current, relevant, actionable data.

Used together or on a standalone basis, these products address data and decision-making challenges for a diverse base of government and commercial customers.

Now let’s talk about our people.

So, I am driven by the need to make a difference, to have an impact, and I’m driven to get meaningful returns for my time.

So why did I come to BigBear.ai? Because it is a unique environment that meets all of those needs. And it’s clear to me that our highly specialized team shares those same values and motivations. Our workforce is made of dedicated professionals who are committed to finding the best possible products and solutions, these are people who know that the only way to truly innovate – and not just invent – is to have a deep understanding of our customers’ needs.

Our team members also value opportunities to give back to our community, and we are proud to participate in philanthropic and charity events for organizations like Fisher House, which supports America’s military by building homes where military & veteran families can stay free of charge while a loved one is in the hospital.



We have a diverse, supportive, collegial, family-like learning environment, and we are focused at every level of our company on making BigBear.ai better through diversity, equity and inclusion. One example of our culture that I want to mention is the recent addition of our women in BigBear resource group, which is dedicated to the advancement and empowerment of women within the company.

We have a greater than 90% retention rate, even in this highly competitive environment. Our team sees the value of our offerings, our company culture and a clear vision of how they, as individuals, can make a difference.

We’ve also made more than 75 new hires in the first half of this year, increasing our employee base from around 550 to approximately 625 employees. We’re building our Sales & Marketing team to take advantage of the opportunities before us in the commercial segment.

We founded a new Sales Engineering team to push commercial “lighthouse” prospects to success. We grew our product management and founded product marketing teams.

We expect to expand R&D, Design and Engineering, Technology Research, Operations, Customer Engagement and Training functions through 2022. Now this adds significant breadth to our operations and better positions the Company to execute on our expansion strategy.

And I should note that many of our employees have secret-level or higher U.S. government security clearances across many disciplines. This is a highly sought after talent category, as security clearance is a significant barrier to entry for our competitors.

The bottom line is that we are attracting major talent from established companies and organizations. They’ll tell us they find the combination of the game-changing nature of our BigBear’s applications and growth trajectory as a unique opportunity in the marketplace. Our leadership team is thrilled and quite frankly honored to lead such a team. With that, I will turn this over to Brian.


Thank you, Reggie. So, we provide technology to help you know and shape your world. This is not easy in a world drowning in imperfect data. This is why our inventions are so compelling. We have operationalized breakthroughs in Machine Learning that quickly deal with real-world situations at real world scale.

For example, enterprises need our tensor completion, which is Machine Learning that works when your data isn’t perfect, because it assumes that data only tells part of the story. So the model works to discover and fill-in the rest of the story using contextual, cross-source reasoning. Our tensor completion allows our customers to achieve value faster, and we built our product to make it and other advances easy to apply to problems.

Now to build out everything you need to achieve clarity and decision dominance in complex environments, we offer 3 AI powered products, which together let us offer end-to-end use case support, and separately let us service point customer problems.



Observe keeps tabs on the world in real time, reducing crazy amounts of activity ingested from hundreds of disparate sources into orderly, easy to consume reports on events, facilities, voyages and and many other entities.

Orient uses predictive analytics to mine for insights hidden in data, reading between the lines understand what data is telling you about your world.

It’s a low-code, massively elastic, intelligent workflow engine that makes our specialized algorithms quick to deploy at the scale required for global missions.

Now, Dominate provides AI powered advice to optimize decisions. And this is as operationalized as AI can get. We help our customers understand the impacts of decisions before execution, and help them find an optimal path from where they are to where they want to go.

Just like everyone uses map applications to route themselves from point A to point B, dominate is the must-have application to navigate enterprises to success.

And I’ll dig in more into the specifics of these products on the next slide, but first let me give you a sense of how impactful they are.

Observe is providing almost all of the location intelligence from open-sources for the defense community. That means we power the monitoring and alerting mechanisms protecting the lives of service members and the security of operations globally.

This is especially true in places that don’t get a lot of headline attention, where our human analysts don’t have the bandwidth to pay attention. Our- our analysis of cheap sensors tips users to when expensive sensors and human analysts should be brought to bear, increasing situational awareness and really reducing surprises.

Orient was recently deployed to a classified defense project where the team had been try to forecasts using sporadically available data from multiple, imperfect sources, and the best a large team of data scientists could do after two years, using a leading product from a well-regarded vendor was about 20% accuracy, a day or two in advance.

Within two months of deployment, we had increased forecasting accuracy to 96% and the lead time by a similar amount.

Dominate is almost magical. With it, you get this data driven advice, for questions like: what will the impact be on customer acquisition or sentiment towards our brand if we make investment A vs. investment B. Or what are the optimal ways to allocate our fleet to minimize a competitor’s growth.

The increased certainty we bring to planning is highlighted by the US Military now being 24x faster at creating and updating operations plans with dominate.

These plans get quite a bit of scrutiny, as our nation’s security rests on them, so our involvement is already a big vote of confidence and the increase in planning speed keeps our military at pace in accelerating change in the world.



Now, we’re not delivering purely automated decision making. We are augmenting human decision makers with automation that monitors more data than they ever could, mines what that data knows about the world, and then tests potential courses of action for impacts.

This gives decision makers the ability to act faster, with more certainty.

Next slide please.

Every BigBear product shares a common event-driven, serverless architecture, which we make sing by making every processing step a reusable module. These are steps like, “make a forecast” or “find objects in an image.”

All of these modules are API-wrapped “Legos” in our toolbox, and we snap them together in different combinations, composing our products and solutions.

Each of our products has some real secret sauce that differentiates it. And you know they work because of the critical defense situations they are trusted in.

Consider our Observe Knowledge base. You may be familiar with the parable of the- the three blind men and the elephant where each man touches a different part of the animal and each comes away separately thinking it’s a snake or a tree or a rock. Only by conflating their perspectives together, is a complete picture formed about the elephant. This is what Observe does, it pulls multiple observations of many different entities and then we conflate those together to better characterize the thing.

This is really important in a world where internet services personalize the data they expose. From advertisements to news feeds, trying to shape your decisions as a consumer by only showing you what they want you to know.

That isn’t putting you in control with objective data. BigBear.ai uses the tradecraft we have perfected with our defense customers to not let our sources hide data. And we do this globally, watching more than 600 million events in 200 countries every day.

Orient brings you the best prediction algorithms for real-world needs, like our tensor completion for tasks like data cleansing, forecasting, applying hundreds of specialized computer vision models, translating documents and much more. Users create on-demand workflows to answer their specific questions, and Orient scales the workflow execution, as big as needed, to keep pace with data streams.

Our tensor completion model really does set us apart. I mentioned before that tensor completion assumes there is more to the story than the data you give it. It uses context between these multiple different perspectives on a problem to improve the accuracy of predictions.

This makes common sense. For example, consider all the domains that would be relevant when forecasting turnaround time at a port: you need vessel telemetry, weather, construction schedules, labor sentiment. All of these things are informative and our tensor completion learns how all these perspectives relate to the port turnaround time, but unlike other methods it also learns how these perspectives relate to port turnaround time, but how they relate to each other, and works to identify hidden factors influencing outcomes. Then, if- if one import source is missing or erroneous, tensor completion can use its extra knowledge to still give good predictions.



Dominate creates data driven advice about possible courses of actions or business scenarios. You express your plans or goals in a simple visual way like dragging a line in a forecast chart up or down to where you want it to be and Dominate quickly shows you how the world reacts. It- it shows the likelihood of outcomes; first, second, third and large order effects of that decision. It helps you explore your world and optimize your outcomes. And this is one place where our tensor completion Machine Learning simulations we run that is mandatory. Seeing around corners into uncertain situations is what our methods do and no one else can offer this.

Now our- our “lego” like composability gives us 3 big advantages over our competition. First, we create new solutions very quickly—like being able to answer new customer questions on new data in 4 weeks for a new logistics customer.

Second, we improve other technologies our customers are using by simply connecting them to our API. This makes our go to market very synergistic with partners.

Finally, the impact of innovation is multiplicative because every module, or “Lego” we improve impacts every workflow that uses it.

Next slide.

Our- our technology is really transformative anywhere customers are making decisions on complex or incomplete data. But in the near team we are going to market with commercial offerings that are very similar to our Defense capabilities.

What we do is complex, and to make it easier to consume, we- we really are working on offering use case specific end-to-end solutions. Our focus is on a “land and expand” approach, put our foot in the door with one of our easy to understand offerings, like maritime intelligence or even just some data from Observe. And once the customer sees how powerful our solutions are, the horizontal composability of our platform allows to quickly grow inside the account.

Our Location Intelligence solution helps customers understand the physical world they operate in. For our defense customers, this helps ensure the security of critical infrastructure and situational awareness of where adversaries are operating.

But if you replace the word “adversary” with “competitor,” you have our initial commercial offering. Supporting site selection and site management that is very useful in energy and retail where the expensive building a new charging station or restaurant is not taken lightly. This is actually where the Space industry is also focusing, giving us new ways to understand locations. And we go beyond just warning an operations manager about upcoming disruptive activities like strikes or ports, we provide advice on how to mitigate those situations.

Maritime and media intelligence are likewise applicable to defense and commercial markets. And by going to market in commercial with what we have already built and proven with our defense customers, we’ve hit the ground running. And our ongoing product investments are quickly adding new insights for these markets and opening others.

Forecasting is a strength for our platform, that has obvious applications in Finance as we predict commodity and equity prices in response to events. Our tensor completion is- is well suited for assortment optimization, preventative maintenance and anomaly risk detection, things which, you know, have broad logistics applications.



But there’s really no limit to the number of end markets that will benefit from our predictive analytics.

Next slide.

So, a little about some of our customers. You know, the success of our vertical solutions can be best seen in the solutions we provide to one of our intelligence customers.

They can’t hire enough analysts to keep pace with the firehose of data that keeps growing with the proliferation of sensors around the world. You know, we showed up and applied AI to help automate their analysis. And we succeeded such that we were recently recognized by an award from their director. This customer is a great example of our “land and expand” go-to-market strategy as well.

We started small, providing our Observe data feed for media. The customer saw that our data was always easier than our competition to use and subscribed to our infrastructure, our facilities data feed along with Orient to start tracking behaviors and detecting changes in the environment. Our automation worked so fast that we outpaced all the analysts trying to produce global location intelligence by an order of magnitude and started correcting errors in data that had grown stale.

Dominate was next, enabling analysts to understand the behaviors of adversaries and understand operating environments, and now we’re the only accredited AI producing actionable tasking to units in the field. And Analysts are asking us about new things we can tackle together instead of just helping them play catch-up.

Next slide.

The commercial market is showing tremendous value. Within a month of engaging a large maritime logistics operator, we surpassed their expectations and made them want to accelerate their use of our fuller product line.

Competitive intelligence was the initial focus of our efforts, and one compelling early insight we provided within a month was a list of all the docks their competitors were servicing that they didn’t even know were possible customers.

We monitor competitor fleets and operations to help our customer out-maneuver them and capture more revenue.

As another example, we identified when competitors vessels were likely to be laid up for repair. That’s a short term opportunity when our customer can kind of swoop in and provide alternate shipping capacity.

With the value of those comparative insights we initially provided, this customer is now asking us to help them optimize their own operations, from setting shipping day rates, routing their fleet to best capture demand for their services and to reduce waste during trips.

Final slide here.



You know, we are positioned to fill a gap the market.

First, our anticipatory intelligence AI can generate and assess courses of action to provide advice and clarity for your decisions. I want to foot stomp again the- I really believe we are going to become the must have “navigation” app for enterprise decision making.

We make the most of our customers’ existing technology investments, provide this service from day one, with ready to use applications that- that still allow incremental growth over time.

Our capabilities have been put to the test by the defense community where lives are on the line and failure is not tolerated. And BigBear.ai then provides that composable platform that lets you rapidly dominate your problems. This sets us apart from the market’s other options. Certain competitors are too monolithic. They force you to choose their way or the highway. Other vendors focus on generic flexibility and give you nothing until your developers have churned away for months. BigBear fits between these ends of the spectrum, positioning us to capture significant value.

Back to you, Reggie, to talk us through that growth.


Thanks Brian. So now let’s talk about the many ways we are leveraging our technology and products that Brian talked about to achieve growth. How we are helping organizations and companies operationalize AI augmented human decision making at scale, at scale?

The first is to execute on our existing $500 million backlog. These are contracts we have already won. We have already also identified and are pursuing roughly 150 near term opportunities collectively worth around $4.5 Billion.

We have been conservative in our projections. While we have an impressive 93% win-rate I talked about earlier on new business, it’s 100% on recompetes, but we’ve factored in lower percentages when we weigh our win probabilities.

Now, given that and the fact that we have grown historically at an approximately 30% clip without significant internal investment, we’re confident in our ability to expand into adjacent markets and meet our projections.

We are confident in our ability to grow in commercial markets. Our technology can support decision making processes across any data intense industry, and we’re specifically targeting the highest growth verticals first. In addition, we’re targeting opportunities for expansion into the federal civilian agency market.

Finally, on the right side of the slide, we are evaluating inorganic growth through targeted M&A, and we have more than 25 potential targets identified. M&A will contribute to our revenue in the coming years. We strategically employ M&A when it makes the most sense for our longer-term objectives.

So let’s talk about the commercial go-to-market strategy that we are executing. We’re leveraging our existing technology to penetrate commercial markets. Brian talked about the adjacency between the defense markets and commercial markets – where all of our products are highly relevant – through both direct sales and through channel partners.



So we currently have 16 partners and that includes Amazon AWS, Microsoft, Elastic, Qlik, Knime and FireEye.

We are actively building our sales team and our channel strategies for our high priority industry verticals, and we’re planning investments to enhance upselling and cross selling opportunities.

So BigBear.ai has recently received several significant wins and that supports our confidence in our revenue projections going forward. This accounts for over $150 million in total contract value.

We deepened engagements with existing customers, and that’s including an advancement of our relationship with the US Army’s Directorate of Operations and the beginning of the second phase of our contract with our largest maritime commercial customer.

BigBear.ai also secured two commercial partnerships in the space industry: an agreement with Virgin Orbit to develop next generation space solutions, as well as a joint effort with Redwire to establish space cyber range capability.

We also started working with UAV Factory to develop AI/ML for their unmanned systems that serve both commercial and defense markets.

In the next couple slides, we’ll dive deeper into the Virgin Orbit and UAV Factory engagements.

So let’s start with Virgin Orbit. Virgin Orbit is a premier provider of satellite and launch services, and we are excited to work with them to deploy AI-powered analytics platform for End-to-End responsive space launches.

Our partnership is an annual recurring revenue software agreement that is an effort to solve four complex problems. One: Processing data from each launch to generate insights in support of an effort to reduce time between requests and launch. Two: Providing predictive analytics on constellation positioning and optimizing the ability for sensors to collect data. Three: Conducting AI-driven vulnerability testing to ensure protection from cyber attacks; and Four: Optimizing manufacturing operations and reducing satellite production costs.

We have already begun to support Virgin Orbit with our Decision Dominance platform and look forward to the continuing support of their mission to open up space access.

As I’ve mentioned, we’ve also entered into an agreement with UAV Factory, a manufacturer and designer of unmanned aerial vehicles.

Through this engagement, we’ll help analyze and monitor the vast amount of data related to the supply chain and provide anticipatory insights on future impacts. As all you know, the increased attention on supply chain recently on things like equipment failures or changes in demand, optimizing operations and improving the decision-making process.

This engagement is also interesting because we are selling our software as an add-on to UAV Factory’s core products, thereby expanding our distribution channels.



Moreover, it allows us to explore the deployment of sensors on-demand to collect missing information for our customers.

Overall, we think this is a great example of an engagement that will help us expand our networks and our offerings.

Now we mentioned a couple of times, the land and expand strategy. This is where we regularly land a single foothold with a customer and expand as they learn our full value.

Our revenue and platform product growth is accelerating with each product created. To get in the door quickly with customers, we offer vertical end-to-end solutions. Once the customer sees how powerful our solutions are, the horizontal composability of our platform allows us to expand quickly inside that account.

Now as you can see from the slide, for instance, with an Army customer, we quadrupled revenue in 7 years, and with an Intelligence Community customer, we quadrupled revenue in less than 5 years.

On the commercial front, we are working with a maritime logistics company and have scaled from $25,000 in revenue to $2 Million in less than a year. They signed up for our Observe product, saw the value immediately and decided to implement our Orient product as well.

We expect to continue to compress the time it takes to ramp revenue.

Now, while not a major part of our growth plan, M&A will help us strategically build our capabilities and allow us to further accelerate customer penetration.

We have a track record for successfully selecting and integrating M&A targets, and we prioritize accretive M&A with companies that have a culture similar to ours, which helps dramatically with the integration process.

Culture, culture is really important to us. We are a team of mission-motivated, cleared, and trusted employees, and we look to targets that would fit well with us from a strategic, financial and cultural perspective. Specifically, where we can enhance cross-selling with additive technology, build into adjacent markets to accelerate customer penetration, or broaden our use cases.

Previously, we had noted this additional M&A had not been factored into our near-term company projections. I want to emphasize that we think very carefully about the build versus buy decision when we plan our horizontal and vertical expansion. Now given the targets we’ve identified, in certain cases, we think it will make more sense to acquire the capabilities we’re seeking as opposed to building them internally.

In these situations, it has become evident that we can not only avoid considerable R&D expenses but we can also hit the ground running and realize growth opportunities much faster.

This speed, well it’s critical in our markets where first-mover advantage can be significant. Therefore, I wouldn’t say our model is so binary as to say it absolutely does or does not include M&A.

With that, I’ll turn the call to Josh to review our financials. Josh.




Thanks Reggie. As mentioned previously, I am Josh Kinley the CFO for BigBear.ai, and I’ll be walking through some of our financial highlights and projections.

Let me start out by saying BigBear.ai is positioned from an exceptionally strong position early on and that’s a considerable differentiator for us. And I’m not just referring to the business and the numbers, but the underlying people, processes and infrastructure and that’s critically important for any public company.

So, we have mature, integrated financial systems and processes, with decades of operation under our belt. We have years of audits without a single deficiency or weakness. And we have an existing team already with public company experience already.

So this has really underscored the financial performance, in terms of our growing revenue, gross margin, and free cash flow.

On our revenue, BigBear.ai has historically grown at a CAGR approaching 30% as we’ve mentioned previously, and really that’s with just a focus on a few key select customers and a very modest sales and marketing spend. In other words, all of that growth, that historical CAGR, was driven by organic customer demand for our products and services.

So the projected 40% CAGR you see reflected here through 2025 as a result of our investments to penetrate new markets and to grow into existing and adjacent markets as well.

We are encouraged by the level of inbound interest we’ve already seen from commercial offerings and we have this growing backlog and strong outlook moving forward.

For those of you not familiar with our existing revenue profile, as of 2020, it’s roughly a 50/50 split between Analytics which is sales of our software solutions and Cyber and Engineering, which is largely the services-based component of our revenue.

As such, the Analytics revenue comes with a substantially higher gross margin than the Cyber and Engineering revenue. What you see here is a trend towards higher analytics or software through 2025. This isn’t a plan to make that change or projection as much as it is a continuation of a multi-year trend that’s already underway.

As few as just a few years ago, less than 10% of our sales were from software analytics and in 2020 it was north of 50%. As the software sales increases, it will be north of 75% by 2025. This, in turn, will impact our gross margins as you’ll see in just a few slides.

And lastly, on our EBITDA, BigBear.ai has always been EBITDA positive, and it’s accelerated through 2020, in fact. We ended 2020 north of 18%. So the 13% you see projected here for 2022 is a reflection of those sales & marketing and R&D investments that we’ve spoken to.

This is in line with what you’ve heard about our 75+ new hires year-to-date across Research & Development, sales and marketing and product and that service delivery teams.

By 2025 we’re going to see EBITDA rebound and be north of 20%.



So here you see the shift in revenue composition that I just mentioned. You can see the roughly 50/50 split in the near term between the two revenue streams. But as I mentioned we are in the midst of this change in terms of how our products are sold to both government and commercial customers. And the trend line here reflects that through 2025.

To dig into these revenue sources a bit further, the Cyber & Engineering component, as I’ve said, is largely services-based and while we see it continuing to grow, it’s going to become a smaller component of the larger revenue picture over time.

So with that clear delineation between Cyber & Engineering and Analytics revenue, it’s important to understand why that Cyber & Engineering revenue is here in the first place. So this revenue base reflects our long term, deep-seated customer relationships that we’ve had over decades honestly. And it has informed and shaped our R&D, our innovation, and our customer intimacy over time. So for that reason, we always want to have that presence with those customers, but moving forward it will be a much smaller piece of the overall revenue.

We’ve only project this to grow at a 20% CAGR, which is being conservative and could certainly exceed that, but our investments are focused on growth in the Analytics segment.

So on the Analytics revenue, we’ve talked about the investments we’re making in both R&D and sales and marketing and this is where we are focused. And this has what our customers have communicated over the last 5 years as we’ve seen our revenue mix change.

This higher margin revenue that we see here is really driving us in the additional markets. We expect to see this accelerating in a few ways. First: we have continuing growth of our organic relationships today with existing customers. This is historically grown approaching 30% without any investments. We see opportunities for adjacent federal and civilian markets. These are customers that have similar if not identical needs to the customers we are already serving today and, as Reggie mentioned, they’re starting to carve out substantial budgets for AI/ML investments in these agencies.

And lastly we have the Commercial sector with many companies realizing they need to implement AI/ML solutions to remain competitive in their respective markets.

So to look over at the chart on the right, note that by 2025, we are still early in the move into commercial markets with only about a third of our revenue coming from commercial sources. Both Reggie and Brian have mentioned the inbound interest we already have and we’re still early into ramping up our Sales & Marketing investments and we’re still realizing that interest. So the 35% projection you hear in 2025 means there is still considerable upside beyond this period.

If I could talk about the gross margins for a second. As I said earlier, a significant portion of our historical revenue has come through government contracts and relationships. For those not familiar with government contracting practices, the government is rarely fully outsourcing or buying a finished product. This is for one of a couple reasons, a lot of times you see specialized, atypical implementation environments and you can think of that as highly classified infrastructures with considerable integration and security concerns or they are simply unsure of what their desired end-state looks like. Think evolving national security mission, ever-changing requirements and collection and data needs.

So instead, they have historically requested that our data scientists, software engineers sit next to them, assess their evolving needs, and design and develop these technical solutions on prem. On the downside, that means there’s been a much higher labor component in these customer relationships and that’s translated into lower gross margins in the past.



On the plus side, it means that nearly all of our historical R&D spend was informed by and funded through these government contracts. That’s why historically our R&D expense was fairly modest.

But a few things are changing now. As our products have matured, they’re not requiring nearly as much labor for implementation and their flexibility within any customer engagement to address numerous operational challenges make parallel implementations for that government customer increasingly efficient and allows us to expand our product offerings for them.

The government is becoming increasingly open minded regarding non-labor based contracts, if you will. It’s especially relevant where the software, like ours, can adjust and evolve over time with their mission.

So as a result some of our more recent government engagements have considerably higher gross margin due in fact to a majority of the non-recurring engineering has already been incurred in the past. As we’ve seen this change, some of our more recent relationships with government customers have seen gross margins north of 60%.

And while that’s a great in the government market, it is still considerably lower than what we’re going to see on the commercial side where we expect that over time to exceed 80%.

I think it’s a good place to mention that, to Brian’s credit, he and his team have always looked at emphasizing that we are not creating one-off solutions, especially in the customer markets. Their design and development is focused on making our deployments agile and efficient and over time this is going to accelerate both our product adoption and the gross margines.

So to bring this back up to the top level, we expect the gross margins from our Analytics sales will constitute roughly 88% of our gross margins overall by 2025 and that’s driven by a gross margin percentages north of 70%.

Now that we’ve covered revenue and gross margin, but as you can see here, the impact on the bottom line is evident.

Even with our considerable R&D and sales & marketing expenses over the next 5 years, we expect EBITDA to exceed 20% by 2025. Recall that that’s only slightly higher than where we’ve been in the past with no investment on those fronts.

And in terms of free cash flow, and this is a huge differentiator for BigBear.ai as well, we’ve always been cash flow positive. Even with the investments in 2021, we expect free cash flow in excess of $13 million. And more importantly, with the growing revenue, the changing sales mix, and expanding gross margins, we expect to generate more than $100 million of free cash flow by 2025.

So we’ve given you a sense of the financial projections through ’25, I wanted to take a closer look at our 2021 year to date performance and end of year projections. I want to spend a little time here to make sure that everyone understands this.



I want to start out by saying that nearly everything we communicated with investors 5 to 6 months ago regarding our ’21 plans has come to fruition. The only variable there is that it hasn’t happened on the exact schedule that we anticipated.

So what I mean by that is, we’ve won the contracts we projected to include a recent 5-year recurring contract worth $140 million over the relationship. We’ve picked up new awards with Federal adjacent customers, including AFRL, which Brian mentioned, and a soon to be announced award you’ll hear in the coming weeks. And we’ve seen that commercial interest we anticipated, if not exceeded it honestly. Reggie talked about Virgin Orbit, the expansion of maritime opportunity and partnerships with UAV Factory. And a few others that we expect to announce in the weeks to come. So we’ve also invested in all of the areas that we talked about 5 or 6 months ago, the sales & marketing and R&D.

The one thing we couldn’t control in this equation was when our customers were going to make contract awards and this has been especially true with government customers who had had to deal with COVID -related workforce shortages and workplace restrictions.

Because of that, we experienced a roughly 6-month disconnect between the timing of our projected revenues coming on board, which shifted from the first half of the year into the second half of the year and the OpEx investments to make our commercial expansion plans come to fruition.

So I want to walk through this in a little more detail. It’s probably easiest to start out with the OpEx. You can see it breaks down here to our G&A, sales & marketing, and R&D. So our projected spend through year end is within 3% of what we projected in the March/April timeframe. The mix changed slightly, as we controlled costs in some areas to offset others, but in total it is within 3%. And we targeted these investments intentionally to bring the right people for expansion in the markets where we are receiving interest today and have the highest growth potential.

So to go back up to the top line and to address revenue, we have already included a level of conservatism in our projections regarding award dates from government customers, but even some of those award dates ended up slipping. As I said in the beginning, the important part is that we won everything we had projected, we’ve added $150 million to our backlog since the end of Q2, but the impact of the delays are obvious in the 1st half of the year in our end of year revenue projections. So ultimately that revenue that didn’t show up on time, which meant we were making our investments in the first half without the revenue on board to sustain the bottom-line during that period. We firmly believe, however, that those investments are critical to our going forward plan and are going to hire out a much higher yield or much higher ROI than the extra EBITDA would have in the first half of the year.

While the results and the timing difference don’t accurately reflect the going forward performance of the company in the first half, this is largely addressed in the second half as you see here. The revenue sources were brought on board in the 3rd quarter, much of it coming in the August timeframe, so the 2nd half is much more in line with the performance we expect through the entire year.

The one additional item I want to make sure everyone understands on the revenue is the delayed revenue by no means is lost revenue lost. What I mean by that is that the bulk of that came in in the form of long-term 5 year engagements with a period of performance that stretches out to 2026. So the fact that that revenue or those contracts were awarded 6 months late essentially means the entire profile shifts 6 months to the right if you will, giving us 6 months of additional revenue in 2026 and giving us further out revenue visibility in that time period.



Also our pipeline is diverse enough to still make our original end of year projections achievable even after these contract awards and together this is ultimately why we are comfortable with the ’21 revenue projections seen here and why we have already re-iterated our full year 2022 projections.

I also want to talk about the gross margins here briefly and want to explain the results here. There are two non-recurring items that we saw in 2021. The first is higher sub-contract costs that we incurred in really through 6 to 7 month period that we had to realize those expenses to quickly ramp up the revenue opportunity with the growing customer relationship that we have.

And the second is we made a cognizant decision to make a heaver investment on a key pursuit that we’re pursuing for 2022. In this case, this is an enormous opportunity for the company. The customer has narrowed the field from 10 highly visible, well-recognized competitors that were going after this. They have narrowed that from the 10 competitors to 2, which BigBear.ai is one of those final two competitors. So a lot of marquee competitors were eliminated in that process and we have concluded that it is worth it to make the near-term gross margin impact, or take on that gross margin impact to ensure we get the win in 2022.

So, with all that said, the final highlights I want to outline here is we delivered on the contract awards we projected at the start of the year, we expanded our backlog by $150 million with the awards that came in, our investment in the sales & marketing and R&D fronts are already yielding results to include a multi-year $6 million ARR engagement with a commercial customer, and our second half results when the revenue did line up with the investments we’ve been making are in line with our earlier projections.

Once again, we are comfortable with the ’21 projections here and are reiterating our unchanged 2022 forecast.

So, here we thought it would be useful just to provide a quick revenue bridge from our first half revenue to the end of year projections. Our pipeline of anticipated awards several you may hear about in the coming weeks give us considerable confidence in the range that we’ve already communicated. And all of these awards are providing increased visibility into our 2022 projections as well with a higher percentage of the revenue coming into backlog.

And so, here you see a 2022 revenue bridge, wanted to spend a moment on this to give you better insight really into our confidence on these numbers. First, our current backlog in terms of commercial and federal customers as of mid-September is $157 million for 2022. And that represents 57% of that revenue is already in backlog for the year.

On top of that, we’re optimistic about several near term awards coming up in Q4. And that would allow us by year end to have up to 66% of the 2022 revenue already captured as backlog. Beyond that, we have anticipated commercial revenue, a portion of which we are already in discussions for today that will provide an additional $15 million of 2022.

And the balance that you see there is really coming from our pipeline of existing opportunities that we’re already tracking. This is comprised of 140 active opportunities that we are pursuing right now. That pipeline on an unweighted, unfactored basis has a value of $386 million in 2022. And while our historical P(win) is north of 90% on these opportunities including recompetes, this entire pipeline is weighted at roughly 33%, meaning we’re very comfortable with the 2022 projections on the revenue side.



So, with the financial overview behind us, I wanted to focus quickly on some of our key competitors or our peer group in this space on both a performance and evaluation basis.

So here you see companies that are very similar to BigBear.ai. Many of them operate in the same markets as us, companies like Palantir and C3.ai have a strong government presence similar to us. These companies have disruptive technologies that are in the early adoption phase in both government and commercial markets. And a lot of these companies, quite honestly, are funding their growth today with aggressive sales and marketing and R&D spends, which in most cases means that they have little or no profits today.

So let’s take a quick look at how we compare on a few different metrics.

So on revenue growth, BigBear.ai’s historical CAGR of nearly 30% already puts us in the middle of this peer group. With our forward looking projected CAGR of 40% through 2025, driven by our investments that we’re making today, this puts us above most every company in this space.

And on the gross margin side, and the changes we discussed on the revenue mix have our gross margins of approximately 70% by 2025. It’s a hair below the average, but the impact of our sales and our sales mix over time is putting us pretty much right at the average of this peer group.

On the EBITDA front, as we mentioned, our historically EBITDA actually puts us ahead of most of these peer groups already. So looking forward even after our investments and what we see moving forward, we’re really well positioned ahead of most of the companies in this space. And in terms of the rule of 40 comparison, when you look at our rapid growth combined with our expanding EBITDA, puts us ahead of nearly every company in this space.

So with all those metrics in mind, you can see our current valuation multiple of 5.6 times our 2022 revenue is at a considerable discount to the relative valuations of this peer group. These companies are largely valued at 14 times to 17 times the 2022 revenue.

And looking at our enterprise value, we see that BigBear.ai is valued at a sizable discount of roughly 70% to the peer group average here based on both our 2021 and 2022 revenue projections. Given our history of strong performance and our considerable visibility in future revenues, we believe the value proposition here is evident with significant upside potential.

So, if I could wrap up with just a few final thoughts. BigBear.ai is operating one of the most exciting spaces in the market today. It’s really in the early innings, if you will, in terms of market adoption and opportunity. The growth opportunity in front of us is evident, but it’s even more exciting honestly when you’re coming at it from such a strong position. But what I mean by that is BigBear.ai has an established known and profitable customer base already with long lasting engagements. We have this strong customer retention and 100% recompete win rate. The majority of our near term revenue projections are already known in terms of backlog and recompetes. And we have this disruptive one-of-a-kind flexible platform that can integrate into existing infrastructures making it easier for customers to make that investment decision for BigBear.ai products.

And lastly, we’re doing all this with the backdrop of accelerating revenue, gross margin, EBITDA and free cash flow. So given the strong position and market valuations, we’re exceptionally confident in the opportunity in front of us, our continued growth in the future success of the company and its investors. With that, I’m going to hand it over to Raluca to give an overview of the transaction and GigCapital4.




Thank you so much Josh and good morning everybody. My name is Raluca Dinu and I am the CEO of GigCapital4. GigCapital4 raised $359 million in February 2021 as the fourth Private to Public Equity fund in the GigCapital Global family of technology-focused SPACs.

Today GigCapital4 team received the SEC approval for our GigCapital5, our sixth SPAC.

We are a group of 15 Silicon Valley based technology entrepreneurs and executives, all of us operators with many years of experience in the TMT businesses globally. Our mission is to team with bright, ambitious entrepreneurs to bring their fast-growing company to the public market and provide ongoing support to thrive as public company. We do this by employing our Mentor-Investor playbook and we commit 3 to 5 years down the road, long-term partnership with the company following the completion of business combination.

We carefully evaluated a large number of companies for a potential business combination. Based on our business, financial execution, and go to market screening criteria, BigBear.ai stood out, meeting all of our engagement criteria.

BigBear is one of the very few companies that connects multiple parts of the software demand and service continuum through advanced AI capabilities. It has a best-in-class management team with a world-class academic and industrial pedigree, deep network of relationships in the defense and intelligence communities, and an entrepreneurial spirit with the discipline of execution to be deployed in building this dream.

We look forward to continuing this partnership with BigBear.ai. Now let’s turn to some more detail on the transaction.

We continue to make strong progress toward the completion of our business combination with BigBear.ai and expect to close the transaction in the fourth quarter of this year.

Proceeds from the transaction will be used to accelerate the growth plans the BigBear.ai team just described. Specifically, to fuel commercial growth projects, expand the sales and marketing teams and accelerate product offerings via an increase in R&D.

BigBear.ai also will pursue inorganic growth through targeted, accretive M&As, as Reggie mentioned.

In terms of financing, $359 million will come from cash held in trust. We expect the combination of $200 million convertible note and trust proceeds to deliver $326 million of cash to BigBear.ai’s balance sheet at closing.

On a proforma basis, the $1.56 billion enterprise valuation represents 5.6 times the 2022 projected revenue of $277 million, which we believe is a very attractive pricing opportunity for such a pioneering data and analytics software company.

$109 million will be used to repay all previous debt and $75 million will go to the sellers, AE Industrial Partners, which has served as a key, growth-oriented business and financial partner in supporting BigBear.ai’s growth and the various companies that have been combined into the BigBear.ai platform.



The business combination has customary terms, where the pro-forma combined company will be 72.9% owned by the current owners, about 21.1% by the SPAC shareholders, and about 5.9% by the GigCapital4 Sponsors & Insiders.

In conclusion, the combination of BigBear.ai and GigCapital4 will bring an attractive and promising opportunity to the public market, substantiating our commitment to support artificial intelligence and machine learning technologies.

In light of the expected enhanced financial performance of BigBear.ai, profitable, and being such a well-run enterprise, we believe that the proposed valuation represents an incredibly attractive entry point, with significant upside.

Thank you all very much for listening to our presentation.

I would now like to turn the call over to Sean Battle, Chief Strategy Officer of BigBear.ai, who will facilitate our Q&A session.





Thank you, Raluca. If you’d like to submit a question, please use the form at the bottom of the webcast window. We’ve received a couple of questions already, so Reggie, here’s the first one. The first question is from Louis DiPalma of William Blair: Is the current backlog closer to $500 million when taking into account the $140 million, five-year contract?


Thanks for the question. Over to you, Josh, for that.


Sean, I’m sorry, could you repeat that one more time?


Yeah, absolutely Josh. Is the current backlog closer to $500 million when taking into account the $140 million, five-year contract?


Yes. So that backlog is reflective of the $150 million of contract awards we’ve received really just in the last month. I guess the one other thing I’ll point out on that front is that if you look at our filings to date, at the end of Q1, that was – sorry, I’m looking for my specific number here – roughly in the $380 million ballpark. And so with contract awards we’ve received since then, we have seen that backlog grow by more than 25%.


Alright, thanks Josh. The next question we have is for Raluca, I believe. Can you cover the expectations for redemptions?




Sean, I would be happy to. What we need to address is the certainty of closing. Redemptions are what they are as investors must act according to their respective charters. GigCapital team is keenly aware of all conditions needed to be met for closing, since we have already successfully closed three combinations, two of them this year. We completed GigCapital1-Kaleyra in November 2019, GigCapital2-UpHealth in June this year, and GigCapital3-Lightning eMotors in May.

For GigCapital4-BigBear, we have $200 million committed via convert instrument. We will meet our minimum cash condition in partnership with AE Industrial Partners, the owners of BigBear. For the float, we are in deep discussions with our close partners and investors to put in place for purchase agreement and meet float conditions for the exchange. GigCapital team pioneered this instrument and used it for two of our previous combinations, Kaleyra and UpHealth. We will certainly keep our partners that committed to the convert apprised of all the agreements we put in place, actually hoping they will participate too.

One last comment on this. Everybody knows the long term investors choose not always to participate in this segment of the SPAC prior to the combination, but they do come in in a subsequent equity raise after the combination. Gig team will continue supporting the S-1 equity raise after the combination. We’ve done that with Kaleyra, and it was extremely successful. Thank you so much for the question, Sean.


Thank you Raluca. The next question we have is: Why did we structure the deal with convertible notes?


Yeah Sean that’s another question for Raluca.


Thank you so much to both of you. That’s a great question. So three reasons. Number one, PIPE market today at $10 is shut down, as everybody knows, and we did not want to go to more derogative terms for the PIPE. We believe that the convert instrument funding for a company that delivers, that executes, and that’s as profitable as BigBear, is the best choice as this instrument is not dilutive, brings the desired cash on the balance sheet of the company, while not changing the quick ratio.

GigCapital team pioneered this instrument and applied it already three times in our previously closed combinations, and it worked very well. Last point, again not to forget, long term investors choose to skip this segment but they do participate in the equity funding S-1 on the other side of the combination, and GigCapital team absolutely supports this path.


Thanks again, Raluca. The next question is a multi-part question, from Mike Latimore of Northland Capital Markets. It’ll probably be going out to a few of you to answer. The first part of the question is: You have 100% win rate on recompetes. How many deals is that? Tens? Hundreds? So Reggie if you want to assign that or answer that.


Sure. So thanks again for the question. Josh can you handle that one please?




Yeah absolutely. That is a good question, and I don’t have the exact number in front of me on that. But I would characterize it as the following. We had mentioned that we have relationships that have lasted more than a decade with a lot of key customers.

In the case of BigBear.ai, all of those prime contracts, those relationships we have over the years, every single one of them reflect that relationship and the fact that we’ve never lost one of those engagements. So a lot of those are sizable engagements, to be quite honest with you, that have grown over time like those Army relationships that Reggie briefed in the presentation. When you look at those over I’ll say the last five to ten years, I would put the number of those recompetes that we have 100% win rate, in the I would say the tens. Probably the twenty to thirty timeframe. But that’s because of the size of those engagements and just that we’ve won these things over and over again, up to two, three times as they’ve been recompeted.


Ok, thanks Josh. The next part of Mr. Latimore’s question deals with the source of the revenue and the question is: Can you give a sense of how much revenue comes from Intelligence vs. Maritime vs. Media use cases? And also, Observe vs. Orient vs. Dominate?


Yes, I think that’s another question for Josh.


Those capabilities are a little bit of a, I’ll say an apples to oranges. The Media and the, I’m sorry what was the other one that was thrown out with Media, the Maritime?


Yeah it was Intelligence vs. Maritime vs. Media.


I would say if we’re looking at our Analytics sales today, and we said that that’s a little north of 50% of our 2020 revenue. Boy, please don’t hold me to these numbers. I think we would tell you that what we have seen, and Brian may have alluded to this in the past, is that we have various customers come in and oftentimes start with one part of those capabilities. So I’m breaking it down by Observe, Orient, Dominate. And then we expand that over time.

With the Observe and Orient being, I’ll say the meat behind the capabilities we bring to bear, I would say that those two roughly make up 70% of the revenues between them. With customers often choosing to implement Dominate further down the road over time. That’s really the acceleration of the decision making process once they get access to the analytics and the Observe data.

When you look at how it’s applied to different use cases, I think some of those use cases, whether they be Maritime or Media and such, that is looking at how they’re going to be rolled out to commercial customers, which doesn’t give an apples to apples comparison with our current revenue base, which is government customers, which use them for an entirely different set of purposes. I hope I was able to address that question there.




Josh, the next part of this is what percent of revenue is recurring? And how do you define recurring? Annual subscription?


With our commercial customers, we expect all of that to be in a recurring relationship. We’re generally looking at recurring one-year SaaS-based subscription models for those commercial customers. With that said, I spoke briefly earlier to how the government consumes our products and services, and in those cases, when they bring us on to deploy those capabilities into their space, just because of how the government procurement process works, you can view those longer relationships most always five-year periods of performance. So once we’re awarded an engagement with a government customer, you can almost view that as five-year recurring revenue where that money is programmed into their budget and they expect – we know what the total value of that relationship is over the five years.

It’s a little bit of a different explanation on the government vs contracting side, but quite honestly, that’s one of the differentiators for us because we have a lot of competitors going to market that are trying to find that revenue base that’s going to help sustain their operations and help them grow moving forward. The fact that we have this large government presence today gives us visibility up to five years into the future with that recurring revenue year over year. Of course, that is going to help us fund our commercial expansion as well.


Ok thanks Josh. Reggie, the last part of this question is related to tensor completion. Is it mainly about filling in data holes?


Yeah it’s a great question. We get a lot of questions about tensor completion. Brian, you’re the best person to talk about that technology.


Thank you Reggie. Mike, I’ll even make a point to go back to your earlier question that Josh referenced about the breakdown between Location, Maritime and Media Intelligence. Just be aware that Location Intelligence is currently the bulk of our business, especially in the government side. We do quite a bit of Location Intelligence. AS a matter of fact, we have 1.8 billion places of interest that we’re tracking, so that’s definitely the bulk of our customer base. Maritime is next in line, followed by Media analysis is a much newer entry for us that we’re getting involved in.

But going back to your tensor completion question, yeah, that’s one way to think about tensor completion. If you have this data structure, that data structure has gaps in it, and filling in those gaps is what you’re doing to complete the tensor, that’s the literal definition of the statement. You can think of that, though, as what those gaps represent. They might represent – the future is a gap in the data structure, no one knows those values, so filling in the future becomes filling in those gaps in the tensor. You might not have visibility into things that happened in the past, so you need to fill in those holes in your data structure. That is a good foundation for thinking about tensor completion and absolutely, we take full advantage of all the ways you can interpret that method.




Alright, thank you Brian. Our next question is: What do you expect to be the biggest commercial opportunity for BigBear.ai in the next few years?


That’s a good question, Sean. I think if you listened, what we were talking about earlier is how we found so much incoming interest from commercial companies in our technology and our capabilities. And these are from a variety of different markets, different sectors. Some of the larger ones we’re talking about right now, we talked earlier about Virgin Orbit, we talked about Redwire. This is that emerging space, commercial space market, which we see as something extremely exciting, extremely important in both the national defense and the commercial spaces.

We also – Brian talked a lot about Maritime. That’s another one that we’re working right now, we see a large expansion in that. We’ve also got a lot of inbound into Retail. That said, there are a number of other markets out there, and because our technology is so applicable so broadly, these are things that we will be evaluating over time. So I think this is an exciting time for us to look across this vast space and vast opportunities we have here. One of the things we want to be concerned about here, is not spreading ourselves too broadly. We want to make sure that we’re focused on the most impactful opportunities going forward.


Thank you Reggie.


Sean do you mind if I jump in briefly on that as well?


Sure, go ahead, Brian.


One of the things I want to try to point out as well is that BigBear isn’t just about exploiting sensor data and providing insights about what’s happening in the world. When you think about what we’re doing for some of our space industry partners, we’re helping them – as a matter of fact, all of our customers – we’re helping them be more agile. More agile in controlling their operations. So think about folks who are operating in space, like our Virgin Orbit partner. They’re looking to produce faster services for their customer. The ability to, in Virgin Orbit’s case, launch payloads in hours versus months, and then make those payloads immediately applicable to a customer’s business problem, in terms of not waiting for a satellite to burn in for a few months before you can start exploiting the data from that satellite; making sure that for other customers we know where to direct collection assets to get the largest ROI for the data that’s being collected. That command and control is something that BigBear is very much focused on – being able to help our customers not just make the most of the data that they are collecting, and producing insights that help customers make decisions, but optimizing.

Let me phrase it this way: You’re never going to know everything you want to know about everything all the time. There’s just not enough processing power and collection going on in the world to do that. So you need a hierarchy, a system of being able to shine a flashlight on the things that matter, when it matters, so that you can get the data you need to make the right decisions. That’s a huge part of what we see ourselves differentiating in the industry. That’s why I think the space domain, the Internet of Things, this new need to help shine that flashlight on what matters, very compelling for our space domain customers.




Thanks Brian.


As a reminder, you may submit questions by using the form at the bottom of the webcast window.


Thanks for that, Brock. Alright Reggie, the next question is do you require the transaction proceeds to achieve your projections?


I think right now the answer is no, but I think we are excited about this transaction because it allows us to accelerate. If you look at our goals, we can achieve these without this, but one of the challenges we have is that this is a fast moving space, and in order to have the kind of growth we need in the timeframe we need, these proceeds will tremendously accelerate that growth, in terms of research & development, our commercial projects, sales and marketing staff, and accretive M&A.


Thank you Reggie.


Hey Sean, if I could chime in on that a little bit, and just add that we heard Raluca speak earlier, and I think that that is one of the major differentiators for us that certainly made them look at BigBear.ai differently. The fact that we, number one, have this strong revenue base, and we have proven our performance rather than a lot of companies out there that are projecting or promising performance in the future. The fact that we have the financial underpinnings of a company that already has the free cash flow to internally make investments to fund its growth, sets us apart from most other companies.

And as Reggie mentioned there, when you’re in a market that is so fragmented today, the AI/ML market, because all of these federal agencies and commercial entities realize that they need to start making these investments, it’s so fragmented today. So it’s not a question of whether or not the opportunity is there, it’s how quickly can the company move to capitalize on that growth opportunity. So it’s really the first mover advantage.

While we have a fully sustainable and growing business base today, it sets us apart from other companies, the use of the proceeds going forward is really an opportunity for us to accelerate that and capitalize on the opportunities faster than the competitors out there, and I think that’s one of the things that catches everybody’s attention is what’s in front of us.


Hey, thank you, Josh, we have another question that I believe goes to you. And that is on the adjustment to 2021 projections. Can you discuss the delay that caused that adjustment? And were they typical government delays? Or were there other factors at play?




More than more than happy to address that. So, as I mentioned earlier, we had already, you know, I think we’re very conservative in our projections. And we have in our pipeline inserted a level of conservatism with regard to when government contracts are going to be awarded. We know we were in the midst of COVID at the time.

We certainly saw our government customer base realizing those impacts; they were short staffed to begin with, they had a lot of workplace closures. Because of the nature of these engagements, some of these contracts being classified. These are not things that people can do from their living room, if you will. And so the fact that that workforce was not available to push through the contract awards, it added a level of complexity to the timing for those awards.

So, you know, I keep going back to we won every engagement that we spoke to investors about five to six months ago. But there’s COVID-related delays or something that was simply out of our control, especially due to the resurgence with the Delta variant and further restrictions that happened. So, you know, with that said, all it did was shift that revenue six months to the right. So it did impact our end-of-year projections, because we didn’t onboard that as quickly as possible.

But we are looking and even incorporating that conservatism, or more conservatism moving forward. I am optimistic, I’ll say. Several opportunities, we talked about things that we expect to onboard in Q4, and it seems like things are back on track, so to speak. And so the things that we are projecting between now and year end, we have very favorable feedback from the government that those things are going to happen on time, so we don’t see any additional risk there.


Okay, thanks, Josh. We have another couple of revenue related questions. The first one is: What do you expect to drive revenue within your traditional government market in the next few years? And the second one is, I’ll save that for after this question, Josh, to see if Reggie wants to throw it out to other folks.


You want me to read the question Reggie? I’ll repeat it, just to make sure everybody got it. So the first question is: What do you expect to drive revenue within your traditional government market in the next few years?


So I’ll talk about a few things there. Number one, we had mentioned the maturity of our products, and how the government is now more open to moving away from traditional labor-based engagements, because they see the impact our products can have on their operations. So we have seen a lot of evolving conversations with the government where they’re interested in expanding the scope of our existing relationships, we have a couple of examples in our pipeline of exactly that. So they want to accelerate the deployment of our solutions in that space.

The other thing on the government side, and I had mentioned this briefly before, is we have seen that 30% CAGR, historically, that was with a focus on a relatively small, and I don’t want to make it seem like it’s just a few customers, because we support huge enterprises, you know, Army, Intel agencies, etc. But, you know, that 30% CAGR is just based on the needs of those entities. And you know, let’s say 20 different government customers that that we support today with our products and services.



The thing is, there are hundreds of agencies out there, federal and civilian agencies, with identical needs, drowning in data, as Reggie mentioned. And, as we said, they are programming the need to build AI and ML into their day-to-day operations to make sure that they can keep pace with what they need to be able to achieve. So when we look at adjacent markets in the federal space, we realize there are hundreds of organizations out there with these needs. And it is easy for us to deploy our solutions there. And so as we ramp up our sales and marketing workforce, we don’t need a lot of research and development here because of how closely it aligns with what we’re doing today. But as we look at those adjacent markets, I think we’re going to see some considerable growth among those new government entities.


Let me add some of that too Josh, I think you’ll see just from the lower ranks all the way to top leadership in the federal market space, right? Because you’re finding folks that are literally drowning in the data, the analysts, etc. They’re demanding these kinds of tools and capabilities, but also leadership, from executive orders to national committees, etc., are demanding and as well, right? So there’s a national committee that was chaired by former Deputy Secretary of Defense Bob Work, which really came out with the need for the government to adopt and adapt to these kinds of tools. So I think what you’ll see is an increasing emphasis on this as a joint AI center, right, the JAIC. So all these things are really driving the entire government to adopt these kinds of technologies.


You know, I’ll even speak to some near term initiatives that are very well suited for us to grow into, you may have heard of the joint all domain command and control activities that are going on inside the Defense Department. I think this is the third offset strategy, right? How do we automate more of our battlefield decision making? How do we produce a more agile command and control structure? And we’re already I think Reggie, even in our in the slides went through highlighted one of the early contracts that we were awarded to support and bring our Dominate capability into that JADC2 world.

So there’s this as Reggie and Josh both pointed out, there’s both the expansion of our footprint going beyond defense and Intel into civilian and other agencies. But there’s also just the shift from wanting descriptive analytics to wanting prescriptive analytics inside these accounts. And that’s really where we are focused and I think you’re going to see a lot of growth within our existing customer bases. Like the Army, right? We’re chasing a doubling of our revenue from the Army, just because they want to use this in a prescriptive way to automate their force management business processes. So some tremendous opportunities out there for us on the traditional federal side.


Alright, thanks, Brian. Reggie, next question is: To what extent will M&A contribute to your projections for 2022 and through to 2025?


Yeah, thanks. I think that I addressed a little bit of this during the presentation. But, you know, right now we’re looking to expand our capabilities technically, we’re looking to expand the customer sets we have. And the ways to do that; can we do it organically? Or we can do that through accretive M&A. And I think that’s the kind of valuation where going forward, I mentioned that we had 25 plus targets that we’re



looking at for M&A. And as we go through those analyses, as we started looking at some of the markets we talked about, the real question is build versus buy, build versus buy, right? So we can build it internally. That may make sense. But that takes time, it takes resources. And if it’s possible to buy that through M&A, that may be the right answer too and so we can actually meet our projections right now without necessarily without the M&A. However, as we go forward, we may find out for timing, for efficiency, etc. It may make more sense to include M&A in that, in those projections.


Thank you, Reggie. Our final question is: Big Bear.ai was created from strong legacy brands. Is the company fully integrated and ready to potentially digest further acquisitions in the future?


Yeah, that’s a good question. Right? A lot of folks are interested in the integration question, because we all know, it’s a difficult, difficult process. So we’re very happy with our integration, very happy I think, if we look at it from the back end, to the front end.

And here’s what I mean by that, from the back end, we’re talking about our back end infrastructure, or systems or tools, these kinds of things. We are talking about human resources and compliance, talking about security. And we start moving into our technical teams, then we start moving into the customer facing growth teams. And what I can happily report is we are integrated at all those different levels. And I think this is a tribute to the team. But it’s also a tribute to some things we brought up, one thing particularly we brought up before, and that’s culture. Because I think when you start looking at the difficulty that companies experience with integration, so much of it has to do with culture.

And one thing is the companies that have come together to form this powerful platform, have all very similar cultures and one of the advantages we have with working with our partners like AE and Gig, that’s part of what they understand to be part of our power, is having this culture. And so yes, we’re very happy with where we are, with our integration, and feel very comfortable, in going forward, with integrating digital companies as well.


Reggie if I could as well, there’s a story I like to tell about the integration path. You know, the four companies that came together in BigBear, the first time we encountered each other wasn’t during the M&A process. These companies have been in business for, you know, ten, twenty years in some cases.

And in the customer accounts, we’re working, we had been working together, producing each of the different pieces of an end-to-end solution for these agencies for a number of years before we thought about doing the M&A activity. And what the M&A activity really allowed was, it took the barriers down between our end-to-end solutions so that we could actually deploy it faster than we were already deploying it. We were partnered on almost everything, we were doing business on. Pro Model and NuWave were supporting the Army, BigBear and NuWave were supporting the DIA, we were all doing this work together as end-to-end solutions. And the M&A really just made it so that we could have a reduced barrier to entry. So now we really can offer a one-stop-shop for what was already our customers were acquiring in its different pieces.




And Sean, you know, I’ll add one other thing to that question. And I say this because I received this comment recently, from an independent external party, that they have never seen M&A executed as seamlessly and quickly as what we have proven in our past.

And what I mean by that is the ability to pretty much within a six-month period to integrate on the system, the culture, the process, and the technical baseline is pretty remarkable. And so we’ve proven that in the past. And really, I think that sets us up well for the future. Because, you know, we have now done this several times, and the ability to bring additive technology or new market presence into BigBear.ai on such an accelerated schedule, I think is going to give us an advantage moving forward.


All right. Thank you each for your answers there and as I said, that was the final question. I’ll turn it back over to Brock to close us out.


That concludes the Q&A session and webcast. Thank you for joining.



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