From $1M to $13M ARR: AirOps CEO on AI Content Sales

A chance sidewalk conversation with Sam Altman during SF Pride sent Alex Halliday down the LLM rabbit hole before ChatGPT existed. That early bet on AI—combined with an obsessive focus on high-taste marketing customers—took AirOps from $1M ARR in Q1 2024 to $13M by Q4 2025 and a $40M Series B from Greylock.

Key Facts

• ARR Growth: $1M in Q1 2024 → $13M in Q4 2025 (Alex Halliday)
• Series B: $40M raised from Greylock (Alex Halliday)
• ACV Range: $60K–$250K enterprise contracts (Alex Halliday)
• Pilot Conversion: All but two pilots converted to annual contracts in 2024 (Alex Halliday)
• PMF Signal: Closed $300K–$400K ARR in one week while founder was away (Alex Halliday)

How a Sidewalk Chat with Sam Altman Sparked AirOps

Alex Halliday spent two years building nights and weekends before raising any money. A casual conversation with Sam Altman about LLMs—months before ChatGPT—redirected the company toward AI at exactly the right moment, giving AirOps a meaningful head start on the market.

AirOps was incorporated in 2020, but Alex and his co-founder spent nearly two years exploring ideas before raising a dollar. Their original product was a workflow tool for product and frontline teams drowning in spreadsheets and CSVs—a real pain, but not the one that would define the company.

The inflection came mid-2022. Alex was playing with early OpenAI models on a plane to Atlanta when he realized something fundamental had shifted. 'I just felt like the laws of physics that govern software were changing,' he said. That intuition was sharpened by a chance sidewalk conversation with Sam Altman during SF Pride. When Alex asked what had him excited, Altman's answer was simple: 'The AI stuff's getting really good.' Alex had no frame of reference for LLMs at the time—but he went deep fast.

The company raised its first money and assembled its initial team at the end of 2022, giving AirOps roughly a year of AI-native product development before the market caught up with them.

"I just felt like the laws of physics that govern software were changing. It was just one of those moments where you're like, wow, there's this general purpose intelligence." — Alex Halliday

"He got me onto LLMs, and I just totally fell down the rabbit hole, and the business started moving in that direction." — Alex Halliday

Why AirOps Picked Marketers Over Every Other AI Customer

Marketers found AirOps before AirOps found them—building 50–80 step AI workflows on a product that wasn't designed for it. Alex doubled down because marketers have high taste, reject mediocre solutions, and push products to excellence faster than any other customer type.

The customer discovery wasn't a top-down strategic decision—it was behavioral. AirOps had put a simple four-step blog post template on the platform, half-expecting no one to use it. They were wrong. 'We would log into the platform on a Sunday evening and we would just see all this activity,' Alex recalled. Marketers were building 50–80 step workflows to do precise content work with LLMs—far beyond what the product was designed for.

That unsolicited intensity was the signal. Alex could have tried to serve every use case—legal, finance, operations—but intentionally chose not to. 'The idea of being truly horizontal and trying to do everything for everyone just kind of lowkey terrified me,' he said. Instead, he committed to one customer for a decade.

His reasoning for choosing marketers specifically: they are creative, they have high taste, and they don't let you off the hook with mediocre work. That relentless rejection, he argues, is what forces a product team to get genuinely excellent.

"Build against the high-taste user, and they will keep rejecting you till you pass their very, very established exit criteria for a problem." — Alex Halliday

"I remember just thinking, is this the customer that we want to build the business against?" — Alex Halliday

• High-taste customers reject your product until it truly passes their standards
• Observing actual platform behavior (not surveys) revealed the real ICP
• Going horizontal with AI use cases risks satisfying no one deeply
• Committing to one customer enables a decade of compounding product depth

What Best-in-Class AI Content Strategy Looks Like Today

The best AI content strategy combines information gain (unique internal data published externally), intent mapping across evergreen and frontier questions, and structured content that 'spoon-feeds' answers to AI models for citation. SEO and AEO are converging into one unified system.

Alex sees the old SEO vs. AEO divide collapsing. 'Google is going AI first with AI Overviews and AI Mode, ChatGPT, and now Claude,' he noted. The core practices of good SEO still apply—but the contrast is turned up on information gain: publishing materially new information the market doesn't already have.

AirOps customer Zola, for example, uses internal wedding pricing data to create pages answering highly specific cost questions. Klaviyo publishes internal email open rate data combined with expert interviews. Both are winning because they bring unique information outside the business walls—something no AI can fabricate from training data alone.

For content to be cited by AI models, structure matters as much as substance. Alex describes the goal as 'spoon-feeding those answers to the models and preparing them for citation.' That means identifying the right intent cloud (evergreen questions plus emerging frontier questions), creating content that addresses them with specificity, and tracking which pieces actually influence model responses—not just human clicks.

"Information gain—so adding materially like net new information to the market through the content that you write—is one big theme." — Alex Halliday

"You want to be quoted a nice big paragraph in a ChatGPT response saying on the Product Market Fit show, Alex Halliday said, XYZ." — Alex Halliday

The Sales Motion That Converted Almost Every Pilot to Annual

AirOps runs a consultative, education-led enterprise sales process. Deep discovery, prepared sellers armed with competitive data, and a rigorous 4–5 week onboarding converted all but two pilots to annual contracts in 2024—at ACVs ranging from $60K to $250K.

When Alex and his co-founder hit $1.3M ARR selling themselves, the forcing function was hiring their first two AEs. That handoff—nerve-wracking as it was—required them to strip away founder intuition and codify what was actually working. 'You can do jazz as a founder in a way that you can't do when you have real sellers in,' Alex said.

The sales personality that won: consultative, disarming, and deeply prepared. AI buying is emotional—buyers are anxious, unsure what to tell their boards. 'Showing up and being disarming, helpful, knowledgeable, and prepared really worked,' Alex noted. Sellers research each prospect's organic growth performance and competitive standing before the first call. Discovery leads; product pitching follows.

Onboarding is where trust is built. AirOps takes 4–5 weeks to onboard a customer through a rigorous process that includes calibrating brand voice—something customers think they know but rarely do until they see examples. They also run training cohorts where customers become certified 'content engineers.' That education layer, combined with proof-point-driven outbound using real case studies, is what keeps the pipeline moving even when no one has heard of you.

"Last year, I think we had all but two pilots convert to annual. Which in the world of AI is actually insane." — Alex Halliday

"Education-led sales and a consultative-led sales has been a big part of how we have gone and done this." — Alex Halliday

• Deep discovery before any product demo—earn the right to pitch
• Seller prep includes competitive data on each prospect's organic performance
• 4–5 week onboarding builds trust and dramatically improves pilot conversion
• Education cohorts create 'content engineers'—customers who champion the product internally

The $1M to $13M ARR Growth Story: Positioning Was the Unlock

AirOps reached $1M ARR in Q1 2024 and $13M by Q4 2025. The primary driver wasn't a product breakthrough—it was positioning. Clearer messaging, a repeatable sales process, and the AI market tailwind compounded together to create explosive growth.

The numbers are stark: $1M to $13M in roughly six quarters. But Alex is clear that the product didn't change dramatically—the way AirOps talked about it did. 'It was the machine starting to work, the positioning getting better, and just it really clicking with the market,' he said.

Proof-point-driven outbound was the early growth engine. When nobody knows your company, you borrow credibility. The Deepgram case study—where their CMO used AirOps to 35x website traffic—became the email hook that got prospects to respond. Investor name-dropping helped early, but real customer results replaced it as the proof base expanded.

The PMF moment Alex identifies is behavioral, not metric-based: he stepped away for a week and the company closed $300K–$400K in new ARR without him touching any of the processes. 'I came back, and I had nothing to do with any of those processes,' he said. That autonomy—of machine over founder—is his clearest signal that something real had been built.

"I think when I stepped away from the business for a week and we closed $300k or $400K in new ARR. And I came back, and I had nothing to do with any of those processes." — Alex Halliday

"I have been humbled so many times over when we have not clearly articulated what we do in a way that resonates with the buyer." — Alex Halliday

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