The Shift to Outcome-Based AI: How Sierra Is Engineering Enterprise Agents Beyond SaaS
PIVOT 公式チャンネル
Across the technology sector, the phrase "AI agent" has quickly become pervasive, yet its practical enterprise definition remains contentious. In an in-depth conversation with business media platform PIVOT, Clay Bavor, co-founder of Sierra and former 18-year Google executive, laid out the operational philosophy and commercial architecture behind the startup's rapid ascent. Rather than selling software seats or charging for raw token consumption, Sierra has staked its business on an outcome-based model, tying its compensation directly to solved customer problems and completed enterprise transactions.
Bavor discussed why building reliable production-grade agents is far more complex than wrapping frontier language models, how rising token expenses are reshaping corporate capital allocation, and why customer-facing AI may expand rather than merely eliminate human support roles.
Defining the Agent: From Simple Language Generation to Problem Resolution
When Sierra launched in early 2024, the concept of an AI agent required dedicated education—so much so that the company published an explanatory guide. Today, the term saturates San Francisco billboards, often with little distinction made between conversational bots and autonomous software.
Bavor defines an agent as a fundamentally new category of software that can reason, make decisions, and execute actions by leveraging the reasoning capabilities of large language models (LLMs). Rather than solely generating responses or drafting text, an agent connects directly to enterprise backends—such as order management databases, CRM systems, and transactional workflows—to execute end-to-end tasks on behalf of a company.
Sierra focuses specifically on customer-facing interactions across the entire consumer lifecycle, spanning customer service, technical troubleshooting, and revenue-generating sales advisory.
Regarding the widespread prediction that AI will cleanly replace human labor in support channels, Bavor notes that routine inquiries (such as order status checks or product returns) are already handled effectively by autonomous agents. However, he cautions against simple one-to-one labor replacement forecasts, invoking Jevons paradox: when the cost of executing a task drops dramatically, overall consumption tends to surge.
Freed from repetitive technical and transactional inquiries, enterprises may choose to redeploy staff toward higher-value, proactive outbound outreach and complex relational support that genuinely requires human empathy and judgment.
Expanding Beyond Support: Long-Horizon Workflows and Enterprise Scale
Sierra began in customer support—working with early clients like SiriusXM to troubleshoot satellite radio devices and transfer subscriptions across vehicles—primarily because language models were initially limited in reasoning depth. As model capabilities matured, the platform expanded into multi-step, multi-session customer journeys.
Under an initiative designated as "Horizon," Sierra's agents are deployed across extended timelines spanning weeks or months. These agents can guide consumers through complex financial workflows, such as home mortgage origination, insurance onboarding, car accident claim processing, or comprehensive travel planning.
The enterprise footprint behind this progression includes:
- Deployment across 40% of the Fortune 50.
- Adoption by one in three of the world’s leading banks.
- A client base where 30% of companies generate more than $10 billion in annual revenue.
- Practical volume milestones, such as originating over $1 billion in new mortgages each month for Rocket Mortgage in the United States, alongside powering conversational real estate search for Redfin.
Why Enterprise In-House Builds Falter: Voice Nuance, Guardrails, and Dialects
With foundational model providers like Anthropic and OpenAI introducing sophisticated coding tools, many enterprises consider building agents entirely in-house. Bavor contends that while building an initial demo is straightforward, taking an agent into production for millions of consumers requires addressing hundreds of granular technical hurdles that raw foundation models cannot solve out of the box.
Voice Activity and Ambient Noise Management
In conversational voice interfaces, latency and fluid conversational turn-taking are critical. Frontier models do not inherently distinguish between an active interruption (a customer interjecting to change the subject) and passive conversational affirmations (saying "uh-huh" or "yeah" while the agent is speaking). Sierra developed dedicated proprietary models specifically for voice activity detection to handle conversational pacing.
Additional real-world audio edge cases include:
- Filtering background television noise, barking dogs, and crying infants.
- Handling multi-party phone conversations—such as in healthcare environments where an elderly patient is assisted by an adult child—requiring the system to separate speakers and verify from whom legal medical consent is being obtained.
- Regional localization, such as equipping voice agents in Japan to understand and converse fluently in regional dialects like Kansai-ben alongside standard Japanese.
Safety, Guardrails, and Simulation Testing
Because enterprise agents are empowered to execute transactional actions—such as processing cash refunds or altering payment methods—they pose security and compliance risks if left unbounded. Beyond strict customer data compartmentalization, enterprise agents require behavioral guardrails ensuring they execute designated processes without deviating.
To validate an agent prior to deployment, Sierra constructs conversational simulators that subject the agent to 10,000 to 100,000 synthetic interactions. These simulations expose the agent to edge cases, contradictory instructions, and hostile inputs to verify adherence to enterprise policy before live traffic is enabled.
Dismantling the SaaS Model: Outcome-Based Pricing vs. Token Billing
A central divergence between Sierra and traditional enterprise software vendors lies in its commercial pricing structure. Bavor argues that conventional seat-based pricing is obsolete for autonomous software, while raw consumption metrics—such as charging per message or per API call—create misaligned incentives where the vendor profits from inefficient, prolonged exchanges.
Sierra operates primarily on outcome-based pricing:
- In customer service: Clients pay if and only if the agent completely and successfully resolves the customer's issue without human intervention.
- In sales advisory: Sierra collects a commission-style fee only when the agent directly drives a completed purchase or product upsell.
Addressing the practical difficulty of defining a "resolved" interaction—given that consumers rarely end phone calls with standardized declarations of satisfaction—Bavor explained that Sierra relies on straightforward resolution criteria. In ambiguous edge cases or gray areas, Sierra absorbs the cost rather than billing the customer, arguing that an imperfect outcome-based metric aligns enterprise incentives far better than charging for unvalidated conversational volume.
The Economics of Tokens and Internal Corporate Token Budgets
To insulate enterprise clients from escalating inference expenses, Sierra absorbs token volatility entirely; customers never receive an underlying token bill. To make this economically sustainable, the company avoids relying solely on expensive frontier models. Instead, it deploys a "constellation of models," combining major frontier LLMs with proprietary, fine-tuned, post-trained models engineered to perform specialized domain tasks faster, cheaper, and more accurately.
Internally, however, token economics have become a serious operational consideration. Sierra runs much of its own engineering and operational workflows through an internal agent named "Pine Cone," which currently generates approximately 70% of the company's code and assists executive staff with financial modeling and resource allocation.
This intensive usage revealed noticeable cost dynamics:
- Senior software engineers at Sierra routinely consume over $100,000 annually in inference tokens through coding agents.
- Runaway conversational contexts or looping API calls can quickly generate outsized expenses for minor tasks (Bavor cited an internal strategic query that inadvertently incurred $173 in context costs).
Bavor predicts that corporate finance will soon formalize token spending much like operational expenses or travel budgets. CFOs will likely restructure headcount planning to incorporate both base compensation and an allocated token budget per employee, alongside internal governance policies governing appropriate and cost-effective AI usage.
Market Traction, Deployment Speed, and Expansion in Japan
Sierra reported reaching $100 million in annual recurring revenue (ARR) within seven quarters of operation, progressing to $150 million in eight quarters, and hitting $200 million by its ninth quarter. In the US, Bavor reports that Sierra’s customer deployments touch roughly 90% of retail consumers and 50% of families in healthcare networks.
Enterprise deployment velocity has also shifted. Regulated global healthcare insurer Cigna went live on Sierra’s infrastructure in 56 days, contrasting sharply with legacy enterprise software rollouts that often span multiple quarters or years.
In Japan, where Sierra has established early deployments with SoftBank and mobile carrier LINEMO, the company recorded customer resolution rates reaching 97% and customer satisfaction (CSAT) scores of 93%. Bavor highlighted the Japanese service philosophy of omotenashi—delivering meticulous, non-transactional hospitality—as a vital benchmark for AI agent development. Because cultural interactions prioritize craft, quality, and the singular nature of each encounter (ichi-go ichi-e), meeting the expectations of Japanese consumers serves as a stringent test for the conversational fluency and nuance of their underlying technology.
Industry Consolidation: How Winners Will Be Decided
With artificial general intelligence (AGI) potentially approaching within the coming years, Bavor acknowledges that the conversational AI space is heavily contested. Competitors span hyperscalers (including his former employer, Google), venture-backed AI-native startups, and incumbent customer service software providers attempting to retool their platforms.
According to Bavor, long-term survival in this sector will not be determined by surface-level model demonstrations or raw architectural claims, but by two operational factors:
- Enterprise Trust: Operating agents that interface directly with an organization's most critical asset—its end customers—requires flawless security, rigorous guardrails, and consistent reliability under heavy traffic.
- Measurable Business Impact: Software vendors must prove tangible outcomes—demonstrable increases in completed loans, lower net resolution costs, improved retention, or concrete healthcare administrative outcomes.
In an enterprise environment increasingly fatigued by speculative AI initiatives, Bavor concludes that the companies that survive market consolidation will be those willing to stake their commercial success entirely on whether their systems actually resolve the underlying problem.
AI agent. Fortune 50 agent.
Welcome. Great to have you here.
Thank you very much. I will practice my Japanese. Very nice to meet you. Thank you for having me. I'm very grateful to be here.
Thank you so much. That's the perfect pronunciation. Is this your first long-form interview with Japanese media?
I think I did one other for print. This is my first on video and audio, and so I'm very excited about it. I love Japan. I'm so excited we're there, and I'm really looking forward to speaking with you.
Thank you so much. We have been looking forward to talking with you so much. So let's dive in. AI agent is such a buzzword now, and everyone means something different, I believe. You're leading this market in customer-facing AI, right? So, at Sierra, what is an AI agent?
Well, first of all, you're exactly right about AI agents being this buzzword that's everywhere. Here in San Francisco, the main highway up and down between San Jose and San Francisco is 101. There are many billboards, advertisements on it, and almost every billboard is something about AI agents. There's actually an ad—I can't remember what company it was, but they've taken over the transportation buses in San Francisco, and the ad just says, "agents, agents, agents, agents, agents." I don't know what they do other than something with agents.
What's funny is when we announced the company just two and a half years ago in the beginning of 2024, no one was talking about agents. No one knew what an agent was, and we had to explain to our friends, to our family, to prospective customers what they are. We even published the Sierra Guide to AI Agents to help orient people on what they are.
As I think about agents—and I'll talk a bit about the agents we build—they're software. It's a new kind of software. It's software that can think and reason and make decisions and take action using the underlying power of large language models, the same type of AI models that power ChatGPT and Claude and so on. The interesting thing about those models is, of course, they can generate and understand language. They can answer questions. They can advise you on things. But you can also use those same language capabilities to solve problems, to reason, and then enable these agents to actually take action with systems like order management systems, look up information from customer databases, and so on.
Where we specialize is in one very specific type of agent, which is agents where a company like a SoftBank or a LINEMO can build an agent that can serve its customers for sales—advising on products, product recommendations, or technical troubleshooting—anything where a great company is interacting with its customers. We can build an agent to help them better serve those customers and to grow their business as well.
What becomes possible? Like 80% of the work is going to be replaced by AI, or what percentage do you think?
You know, it's so interesting. I have a lot of humility in trying to predict where the world is going to go over the next couple of years. Unquestionably, simple things, many things are going to be replaced by AI agents. In the retail setting, much of what customers are calling in about is, "Where's my order?" or, "Hey, I need to return this pair of shoes." AI agents are today already amazing at that. And so I think those types of tasks AI agents will be able to handle.
The interesting thing—you've probably heard Jevons paradox come up in several different settings, and the idea there is when something becomes much cheaper, consumption of it goes way up. And so it's not clear to me whether actual jobs are going to be one-to-one replaced or whether companies will say, "Great, I'm using AI to handle this set of things. Let me have our incredibly talented people connecting with our customers over the phone, making outbound calls, doing things that really only people can do now that this other thing has been handled." So, I'm very confident in the ability of AI agents broadly to do really complex and valuable things. I'm less confident in kind of the knock-on implications of that on whether people actually employ more people to do more with their customers or less. So, we'll see.
What can a company do once they use Sierra's AI agent?
We began in the area of customer service and customer support because, frankly, at the time language models and the agents you could build on top of them could only handle so much sophistication. Early on, we worked with retailers, telecommunications companies, and media companies in the US. One of our earliest customers was a company called SiriusXM. They're the largest satellite radio provider in the world, and we help people fix radios that might have stopped working or move their subscription from one car to the next.
Fast forward to today, and the direction for our company is agents that directly drive revenue. We recently announced something called Horizon, and the idea there is to enable long-horizon agents—agents that can work not in a single instance where say you called in needing help with this or that, but instead might have a set of interactions over weeks or months to help you take out a loan to purchase a home, set up an insurance policy, or plan a trip. It's everything from technical troubleshooting and "where is my order" to fully originating mortgages, helping you sign up for a new insurance policy, or if you've gotten into an accident in your car, notifying your insurer and processing the insurance claim, and all of that.
So we started small, but we've gone very broad in all sets of customer-facing things. We work today with 40% of the Fortune 50, the 50 largest companies in the US. We work with one in three of the world's leading banks. Fully 30% of our customers have over $10 billion in revenue. We're serving all of these companies to serve their customers really across every part of their customer life cycle.
Now here's what I keep wondering. Claude and OpenAI and others are now letting almost anyone build their own agents, right? So some might say, why don't companies just build their own in-house? Why do they still need Sierra?
It's the right question. First of all, what has happened even over the last nine months—there was an inflection, as you know, in December with a breakthrough where all of a sudden the coding agents became immensely capable. No doubt the capability of the models and the coding agents built around them have gone up and up and up. As it turns out, there are a few things that are really important and really complex that you have to get right to build an agent that can serve millions, tens of millions, hundreds of millions of customers across languages on the phone, on chat, online, on WhatsApp, whatever channel it is.
It starts by deeply understanding the industry of the company where that agent is being deployed. In order to build an agent that can from beginning to end originate a new mortgage, you have to deeply understand the lending industry and all of the complexities and regulations around that. A company like our own, we go extremely deep in the industries we serve—all of the major industries—understanding what excellent looks like in serving customers in that domain. That's part one.
Part two, there are several hundred small and big things that you have to get right in order to reliably put one of these agents in production. I'll give you some small examples, but I think they help tell the story. One is things that are intuitive to you and me: understanding when someone is just saying "uh-huh" or saying, "Actually, can I get in a point?" and trying to interrupt, right? How does the AI know when to stop speaking, or when you're just saying, "Yeah, yeah, I got it. Uh-huh, mhm"? Interruption detection or voice activity detection is hugely important to creating a fluent voice agent. But nowhere in Claude or ChatGPT does that. We've had to actually train our own specialized models for that. That's one of a hundred things in voice alone that we've needed to get right.
Can you detect whether I'm speaking to you or babies are crying or dogs are barking behind me or something?
The TV's on. Actually, in the healthcare setting, this is very interesting. Very often, if an elderly patient is calling in, they may have a son, a daughter, a friend on the phone as well. And so separating two separate speakers and understanding from whom are you getting consent, that's another challenge. Voice turns out to be incredibly complex. In Japan, another example: if you're calling from Kyoto or Osaka, the agent should be able to understand and speak in the Kansai dialect and be native to that. That's one of a hundred things even in Japan alone that you have to get right.
The other thing is when you have these agents and they're interacting with millions of your customers, they're potentially processing refunds, like sending money back to bank accounts or credit cards and more. You have to make sure that they're doing the right things. Not only security, of course—ensuring that our customers' data stays theirs and their customers' data stays their own—but also putting guardrails and protections around agents to ensure that they do what they're supposed to do and nothing more. Hugely important, very complex to get right.
I'll give you one last example as part of that. Before releasing an agent, how do you know that it's going to do the right thing when anyone could say anything to the agent? You build a very sophisticated simulator of users and customers to talk to your AI agent, and you have 10,000 or 100,000 simulated conversations where you try every permutation. You throw curveballs and wrenches and other challenging things at the agent, and you make sure that it passes every one of those tests.
Back to your question: many companies are asking, should we just build our own? I think when they realize just how deep you have to go, they choose to partner. It's not buying some narrowly configurable software application; it's building on a platform. It's building with us. One of the things that has really set us apart is how transparent and how extensible our platform is. Our customers can build basically anything on it, and that flexibility is what has enabled us to serve many of the greatest companies in the world.
That's so fascinating. And the other thing that fascinates me is how you sell it. Your pricing is what's shaking up the industry now. Could you elaborate on that?
I appreciate you asking. In the AI space, we're very proud to have really pioneered what we refer to as outcome-based pricing. As I said earlier, AI agents are a new type of software. They're software that you hire not to help you be 8% more productive, but to actually get something done. And we think that our customers should only pay when the AI agent gets that thing done and gets it done well.
In the customer service and support setting, if and only if one of our agents successfully and fully resolves a customer's issue—helped them get their mobile phone back up and running or get their payment method changed to something new—that's the only time our customers pay. When our agent gets the job done for them, it aligns our success with their success. We only win when our customers win.
Maybe more interesting, in the sales setting, if one of our agents helps make a sale—finds exactly the right product for one of our customers' customers, or introduces a customer to an additional product—it's only then that we get a small sales commission on making that additional sale. It very naturally aligns our incentives with our customers. The ROI, the return on investment, is just clear as day. Only when you are saving a bunch of money or making a bunch of money are you paying Sierra. A company is motivated: how much can I do with these agents? That's our approach, and we think it's right. It's new, so we're still learning and our customers are still learning, but we think it is the right business model and the future of how people will pay for software, in particular these agents.
ROI becomes very simple. That sounds great for the customer, but honestly, for you, defining and measuring an outcome can't be easy, right?
Yeah, it's a good question. I don't know about you, but I don't end every phone call I have with a customer service department saying, "I was fully and completely satisfied with that interaction. Everything was perfect. I felt omotenashi; that was amazing." That's not how you end the call, right? Instead, someone hangs up, and so it's not always crystal clear. What we found is, in the sales case, it's very clear: a sale was made, and our agent had an interaction. In many of the support cases, it's very clear.
We found that rather than trying to get really complex in defining outcomes, generally simpler definitions of them work. Knowing that there may be some gray zone, we just say, "Hey, those are on us. We pay for those if it's not clear." So it's imperfect, but imperfect outcome-based pricing we think is so much better and so much more aligned to delivering real value and real ROI for our customers than something that's based on the number of messages sent—who knows what those messages are about—or another form of consumption. Of course, seat-based pricing no longer makes any sense. What is a seat? So you're right that it's imperfect and there are some shades of gray in places, but we've found it to be vastly better than the alternatives.
Token costs are rising so fast right now. Does this model actually hold up for Sierra?
You're right that every business leader, not even technical leaders, but CFOs are now asking, "What am I spending on tokens?" It's a real concern for every company that is watching its AI spend. One of the benefits of working with a company like Sierra is we handle that for you, so our customers never see a token bill. You're right that for us it's real risk that we take on and that we have to manage.
The approach we've taken is to build our entire platform on what we call a constellation of models. This is not one model, but a set of models: some frontier models—and we work with every one of the major providers—and also many of our own fine-tuned models that we post-train ourselves, which are often better, faster, and cheaper than even the frontier models. We're able to intelligently combine those to deliver an amazing experience for our customers' customers, and also be able to build a business for ourselves.
Which raises a bigger question about how work itself changes. Do you believe companies will start handing out token budgets like salaries?
It's super interesting. I've actually thought about this a lot. We run much of our company now on our own internal agent that we have built called Pinecone. Pinecone now writes about 70% of all of our code. I use Pinecone a dozen times a day to do financial analysis, to look at headcount planning and allocation, to help me reason through complex strategic decisions, and more. And of course, writing 70% of your code with an agent that consumes a whole lot of expensive tokens is itself expensive.
The approach we've taken so far is to let people know how much they're spending when they're using these tools. Every time an engineer submits a pull request, submits a new feature, they have a sense for how much it cost the company for them to build that feature. There was one analysis I did where I had a runaway set of API calls and a lot in context, and I was like, "Whoa, I don't think that was worth $173 to the company." Where things will start is just making sure that there's a feedback loop so that, in particular in smaller companies, employees are literal owners of the company and so are trying to be financially responsible.
I think we'll probably see CFOs starting to think about capital allocation: you've got capex, general opex, and then headcount. Headcount will actually be composed of actual salaries and then a token budget. I don't think we've yet discovered what the approach is for if you burn through your token budget: can you spend some of your salary to buy more? What are the incentives to use it responsibly? The expense is becoming significant enough that CFOs and business leaders will need to figure that out, and I think we've seen some early signals of what that looks like.
That's becoming so real now.
We're already there, where the top engineers in our company are spending well over $100,000 a year on these coding agents. By the way, I think that's great; the value we're getting from it is extraordinary. But are all tokens being used for super valuable things? Almost certainly not. And so how do you put some parameters around it to ensure that people are being thoughtful about it and using a company resource in the best interest of the company? You have travel policies and expense policies; I think we'll have token use policies as well.
Very interesting. Anyway, I kind of digressed, sorry, so let's go back. You're already live at some of the biggest companies in the US, right? So what's the real on-the-ground—you mentioned a few examples earlier, but any numbers that stand out?
First of all is just our revenue growth. We hit $100 million in annual recurring revenue in just seven quarters. We hit 150 million in eight quarters. We hit 200 million in nine quarters, and our growth has only accelerated. As I said, it is such a privilege to get to serve. In the US, in retail, we reach 90% of Americans. In healthcare, we reach 50% of families in the US. We're relatively earlier in Europe, in Asia, and in Japan, of course, but it gives you a sense for our momentum.
Maybe one interesting case study: in the US, Rocket Mortgage is the largest originator of home loans in the US. Today we originate over a billion dollars of new mortgages for them every month. We help them reach out to millions of customers every year who may be thinking about refinancing their homes. We rebuilt their home search product, Redfin, on the Sierra platform, so it's like you're speaking with a real estate agent: "I'm moving. I'd love to be walking distance to a school for my kids." Just the very real business impact for our customers that is behind those revenue numbers, I think, says a lot.
In Japan, we're relatively earlier, but even early on with SoftBank and LINEMO, early deployments there, we've had resolution rates—meaning the percent of time that our agents can successfully resolve the customer's problem—as high as 97%, and customer satisfaction at 93%. One of the reasons I am so excited for Sierra to be in Japan is I really feel that Japanese companies set the standard for quality, for craft, and for service and hospitality. I've been quite inspired by the concept of omotenashi and extreme hospitality, just welcoming guests. I love the challenge that serving the Japanese consumer poses for Sierra. We are going to have to be a better version of ourselves in order to meet the standards of the great companies of Japan and the Japanese people, and I'm very excited about that.
That's a very Japanese trait, I guess.
And one I admire deeply. Before we started talking, I mentioned I've taken my sons to Japan many times. It's one of my favorite places in the world. And so I'm pleased to have more excuses to visit Tokyo, maybe go west to Kyoto, and I need to get to Hokkaido and eat a bunch of uni and enjoy the best seafood in the world. I'm happy to have more excuses to visit.
You put it perfectly, actually. Thank you so much. It's not about the transaction. We have a phrase called ichi-go ichi-e—like every encounter happens only once, so you give it everything. But honestly, we don't even think about it consciously, which might be exactly why it's so hard to teach an AI. So I'm curious.
That's wonderful. It's the impermanence of an interaction, and it's only going to happen once, so how can you treat that moment with care, craft, intentionality, and thoughtfulness? One of the ways I think about what we're trying to do is enable the great companies of the world to be at their very best, the very best version of themselves in every interaction with their customers. If you think about what the great companies of the world do, they figure out how to make something amazing, how to do something with excellence, and then scale that. It's been hard to scale fluent, helpful, high-quality conversation, whether it's about advice on what product to buy or helping you solve some problem.
AI and agents and what we're trying to build for our customers is part of the solution to that: enabling great companies to scale quality. I get very excited about that, in particular when we're working with truly some of the greatest organizations and companies that have ever existed, and having the privilege of enabling them to better serve their customers.
It's a very crowded field. You show me the numbers, but with results like that, no wonder everyone wants in, right? Which brings us to competition. Rather than who beats who, what does this market look like in five years?
We are approaching artificial general intelligence. It seems like it will emerge over the next couple of years, and so I think it's hard to fully predict beyond that. I'll go back to where you started, which is our revenue growth is extraordinary. The great thing about a giant market is it's a giant market, and we are the unequivocal leaders in this market. We define the category in terms of logos, revenue, and most importantly, the impact we're driving for our customers. We are second to none, and I'm incredibly proud of that.
The thing about being in a giant market is everyone else knows it's a giant market. And so you have hyperscalers—companies like Google, where I spent 18 years of my career, in this space with great models and products in a similar domain. You, of course, have AI-native startups, and then companies like the service incumbents that have been in the space for a while that have been trying to get into AI agents. Because it's a giant market, others see it, and there's plenty of competition.
We focus on building the best platform and technology, which we have had from day one: the best voice quality, the most extensible, most transparent platform. We focus on how we partner with our customers. We don't just throw a piece of technology over the wall and say, "Good luck." We have incredible teams that can partner and work alongside our customers to diffuse this technology into their businesses and companies. It enables us to go live with even some of the largest companies in the world in days—it's not quarters, it's not years. Cigna, one of the largest international healthcare insurers and health companies, went live in 56 days, and they're a healthcare company in a regulated space.
We focus on best platform and technology, best partnership model, and then the third point that you referenced was our commercial model. We think it's unique; it aligns our incentives with our customers, and I think our customers really appreciate that.
Do you think we'll see a real shakeout, like companies that don't make it? If so, what decides who survives? You mentioned technology, then platforms. What decides?
Ultimately, customers decide, and it's who delivers the most impactful business outcomes and the best quality of service for their customers. We are obsessed as a company with our customers' businesses and with helping them grow and improve their businesses. Everything we do is in service of that. Our first two values as a company are, number one, trust—think about what these companies are trusting us with: it's their customers, it's the most important thing—and then customer obsession.
The companies that succeed, I am quite confident that those companies will have earned and maintained the trust of the largest, most sophisticated organizations in the world, and then been absolutely obsessed not with how cool their tech is or anything about themselves, but about their customers and driving an impact on quality, on revenue, on subscribers, on products sold, on healthcare outcomes—on all of these things. Being obsessed with that, to me, is what will determine who the winners here are.
Obviously, behind that has to be phenomenal execution: building the best, most expansive product and platform, making it the most capable, showing up with the right partnership model and all. But ultimately, it comes down to how you are delivering for your customers.
I could keep going, but we're almost running out of time, so let me move on to the last one. Any message to Japanese companies?
Yes. First of all, it would be an absolute privilege to work with you. One of my heroes is Akio Morita. I remember my first Sony Discman. I remember my mom's Walkman. I remember my first PlayStation. It was a very special day when my parents brought home a Sony Trinitron TV. I have such deep personal respect and affection for the Japanese aesthetic, for the focus on design and quality and craft.
More than anything, I would just love to say it would be an absolute privilege to work with you. If you have hard problems in your business to solve, ways that you want to show up better for your customers in more places for your customers—if you ask yourself, "Gosh, if I had another thousand people to help me better serve the companies I serve or the people I serve, what would I do?"—we would love to talk. For all the reasons I described, we would be honored to partner with you to help you show up as your very best with your customers.
Clay, this has been fantastic. Thank you so much for joining us today.
Thank you so much for having me. I really appreciate it. Very nice to meet you, and thank you.
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