For Starters

Announcing, the Great Contact Center Rewrite

Well, today's a big day. It's a big day for me personally. It's a big day for EndeavorCX, our company, and I believe for the contact center industry at large. I've been fortunate enough in my 30-ish year career now to have seen and played a part of multiple market transitions. Twenty years ago, we were moving from TDM to voice over IP. Ten years ago, that migration from on-premise to the cloud and CCaaS and SaaS taking over the world began. And it's no secret or surprise that the arrival of AI represents the largest opportunity for our industry to become what it can be. The problem is that the vendors that led the last market transition to the cloud are failing to lead this one. It's a different set of skills. It's a different competence. And instead, they've chosen hype over reality. They've chosen to issue press releases instead of release notes and ship product. They've chosen to change their messaging every quarter based on whatever the trend is for the day. This quarter, it’s agentic AI. They’ve chosen high per-agent-per-month pricing for bundles that are obtuse and don’t drive actual results. And they’ve also chosen to lock up your data in proprietary formats and models, making it impossible for you to stay in control of your own destiny. So today we change all that. Today I’m calling this the great contact center rewrite. It is unprecedented in the size, scope, scale, and depth of what we are doing. Today we’re not releasing a new product. We’re releasing a new architecture for the entire industry that puts you in control. It puts the data where it belongs: in the center. CCaaS is no longer the center of the universe. Your data is. Over the next 12 weeks, we’re releasing 12 products, not a singular product. We’re shipping 12 new solutions that will transform the way you think about AI, customer experience, agent performance, and operational excellence. We’ll be announcing some amazing things that you’ve never seen before—things I’ve never seen before. In my 25 years of building data teams and data products, we’re talking about knowledge bases that construct and write themselves, curate themselves, and deploy knowledge across the entire enterprise. We’re talking about the ability to have a data scientist equivalent running on open source language models that can pinpoint issues inside of healthcare plans, as an example. The constraint to realizing our potential in the industry is access to data—and two forms of data in particular. One is the contact call detail records. The other is the transcript. Whether vendors want to tell you this or not, 80% of all use cases in the contact center AI space start with a transcript. Call summary, sentiment analysis, identifying customer journeys, friction points, automating quality assurance—it all starts with the transcript. Today, those transcripts are locked up in proprietary systems. You have to pay a ton of money to get them out, or pay the hyperscalers a ton of money, or deploy a third-party solution, which also costs a lot and requires engineering effort to create an ecosystem to drive your downstream AI strategy. The first product we’re launching today is Prism. It is the first to market that is built on an open source model with a whole lot of pre-processing and post-processing know-how wrapped into it. It can connect to your contact center environment in as short as 15 minutes and start showing you insights. No code. No developing. No engineering. Nothing. It’s priced per minute, per talk hour. We’re at 90% cost reduction over most of the models and transcription engines in the market today. What that does is it sets the foundation. Now you own your transcripts. You own your call detail data. That serves as the library. It serves as your context. It serves as compliance. You now break free from having your data, your call recordings, your transcripts locked up in whatever proprietary system you’re paying a lot for every month. We’re making it cheap, fast, easy. This is the foundation. Over the next 12 weeks, as we release new functionalities and features, you’ll be set. If you already have transcripts today—awesome. Let’s bring them aboard. We can serve as your archival metadata layer now and forever. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit chriscrosby.cx [https://chriscrosby.cx?utm_medium=podcast&utm_campaign=CTA_1]

Just Make It Easy (J-M-I-E)

Today, my advice to startups is to tattoo the following acronym somewhere where you will see it every single day: J-M-I-E, which means Just Make It Easy. I came up with this several years ago when I was traveling relentlessly and found myself flying Southwest a lot—not because they were always the cheapest carrier, but because they Just Made It Easy. I could book last minute. I could cancel last minute. I could change a flight last minute without fees, without calling. And so I found myself a very loyal customer because of that. I started adopting that mindset into our companies—just make it easy for someone to do business with you. Just this last week, I've reached out to a number of startups that I thought could help us, particularly with emerging AI capabilities that are interesting. Only two of them actually made it easy to get on the phone, walk through a demo, walk through the product, and then actually made the buying process easy—very clear with their expectations and what they did and didn’t do. And, you know, they fumbled through the PowerPoints or whatever, which is fine. I still do that. The rest of them made it almost impossible to do business with. I had one dude from a pretty well-funded startup where the soonest appointment they had was five days out. Then he was five minutes late for the demo, so I had already bounced, and he emailed me wondering why. Just yesterday, I had a sales meeting booked, and it was declined by the founder because he made some assumptions about us as a portfolio instead of actually just taking a 15-minute call and realizing that I probably would have done two sales with him. And those are just very recent examples. The list is long with folks who, for whatever reasons, are just making it difficult and introduce unnecessary friction just to do business with them. The point here is to step back and look at all of your processes—whether it’s sales, onboarding, post-sales support—just make it easy to do business with, just make it easy to transact. And man, you're going to accelerate a lot faster than being a pain in people's ass. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit chriscrosby.cx [https://chriscrosby.cx?utm_medium=podcast&utm_campaign=CTA_1]

The M&A Drought: Understanding the Current Market Shift

Last week, I did a post about how I felt that AI in the contact center industry had plateaued rather than seen a bubble. We'd run out of innovation, effectively. And the result of that was a lot of companies that look alike. There was a comment made to that post, which was quite insightful—part of the reason we still see so many companies doing the same thing is because of a lack of M&A activity in the market. And I think that's quite astute because one of the most important pieces of advice I give a startup or a founder is to know the sea you're swimming in and to know it cold so that you can predict the currents before they change. Every startup at some point is going to have a liquidity event. You're either going to run out of cash and die, or you're going to go IPO—which very few companies actually achieve. Or you're going to exit through acquisition, which is the most common way to realize liquidity, aside from going defunct. But right now, very few companies are buying. Rewind five years, ten years, fifteen years, twenty years—the M&A activity, particularly in contact centers, has been quite rich. You've got NICE and Verint, both roll-ups built over two decades that started out as call recording and then diversified. Beyond that, back in the day, you had Cisco, Avaya, and Genesys, all of them quite acquisitive. At some level, Zoom was in the early days as well. Fast forward to today, and really, in this post-GPT moment, the only companies getting acquired right now are the ones adding material cash or cash flow to the acquirer. Take NICE—they’re only buying companies that will improve their quarterly earnings. Look back two years—prove me wrong. Five9 is interesting. I think they probably overpaid for Aceyus, but that was more of a tuck-in strategy that would have added some level of cash contribution to their earnings. Zoom hasn’t acquired anyone since Solvvy a few years ago, probably because their organic growth is at a level where they don’t have to make a lot of acquisitions. The FANGs—the Facebooks, Apples, Netflixes, and Googles—they’re also not buying right now. They’re either building this stuff in-house or waiting for the market to mature enough. The point of this is that no matter where you're at in the adoption or growth lifecycle, you need to be thinking about who the acquirers in your space are. How do you align with them? How do you understand what’s important to them? And then, at some level, how do you think of your company not as building products, but as the product—so that at some point, you’ll have that liquidity event or some type of acquisition. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit chriscrosby.cx [https://chriscrosby.cx?utm_medium=podcast&utm_campaign=CTA_1]

Market Bubbles, Plateaus, and Gray Swans

Today, let's talk about market bubbles, plateaus, and gray swans. For the last six months or so, I've been looking at AI in the context of the market, and it has felt pretty bubblicious—like we were hitting a bubble with a ton of companies jumping in. Some of them couldn’t even spell "contact center" a year ago. And some can’t spell "AI" most days. At this point, there are probably hundreds of companies doing pretty much the same thing. Most of them are either focused on voice automation and chatbots—much easier to build today than even a year ago—or they're working on some flavor of call summarization, quality assurance automation, sentiment analysis, or CSAT. They take a call transcript, evaluate it against criteria through a prompt, generate output data, and put a front end on it—suddenly, it's a product. We had already seen some of the air coming out of that, especially with the recent struggles of high-profile startups and the noise around deciphering real differentiation. Then last week, DeepSeek released its R1 model, catching the market by surprise. It wasn’t a black swan event like GPT’s original release, which was a true paradigm shift, but it reset the market in many ways by unlocking new capabilities that didn’t exist a week ago. Now, it's possible to run one of these high-performance models on commodity hardware, and cloud hosting providers are offering the full model at a fraction of previous costs. This shift opens up new possibilities for tackling complexity. I no longer think the market was in a bubble. Instead, it had plateaued—everyone reached the same level, running in circles trying to figure out the next differentiation. Some companies will recognize the opportunities that these new models and paradigms unlock, using them to create new products and categories. Others won’t, simply because these models introduce new capabilities that many don’t have the context to apply. If a model touts PhD-level benchmarks, but you don’t have the expertise or the right questions to ask, you won’t get significantly different results from R1 than you would from GPT-4 or any other model. But those who understand the potential of models that don’t just generate code but can tell you what code to generate—or that don’t just create data but bring in vertical context to analyze it and surface new trends—will create an entirely new category of solutions. We're going to see a divide: companies stuck at the plateau, having reached the limits of their competence and vision because they’ve only focused on automating what we already know how to do, and those who keep climbing, separating themselves by bringing entirely new capabilities to market. Some of this will be driven by open-source models, making it possible to buy a Mac Mini and generate high-quality data 24/7 while leveraging more powerful models for reasoning-intensive tasks. The accessibility of these capabilities is shifting rapidly, and I expect we’ll see a wave of innovation until we hit the next summit. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit chriscrosby.cx [https://chriscrosby.cx?utm_medium=podcast&utm_campaign=CTA_1]

The Currency of Trust

Few things can be more important to an early-stage company's success—or any company's success, really—than your ability to build trust with the market, with your customers, with your employees, the folks that you're recruiting, and partners in the industry. Exercising that leadership and integrity draws people to place their faith in you. That includes everything from: Will you be in business tomorrow? If I'm going to write you a check and build parts of my organization around you, how can I trust that you'll have the financial resources and ongoing ability to deliver? A recent example is this week when the whole DeepSeek controversy hit the internet. The open-source AI model came out from China, and there was a lot of fear and uncertainty in the market about what that meant for people. If it was deployed, would data be going overseas to China? Could it cause some type of security issue? Then there was the camp we’re in, which embraced this new technology pragmatically to ensure the success and integrity of our business and our customers. Through all of that, not a single customer reached out to us with concerns about what we might be doing with their data and this new DeepSeek model. I believe the reason for that is trust. One, contractually, we have a whole lot of safeguards in place to ensure data integrity—making sure data doesn’t leave the United States, strict access levels, HIPAA controls, and SOC 2 compliance. For us to start moving data around would be a breach of trust. It would be a breach of contract. But beyond that, our customers also know that we have the vision and pragmatism to be good stewards of their interests. That’s what building a reputation for integrity and trust in the industry comes down to—demonstrating that you are a good steward. I see us as stewards of our customers' data and their money. They invest with us every day, and we are responsible for delivering outcomes and results. As a startup, you have to demonstrate that level of maturity and leadership day in and day out—whether that’s through contractual agreements that create safeguards for clients if something goes wrong, or, more importantly, by proving every day that you’ve earned their respect. You need to show that you understand the market and these issues as well as anyone, and that you always have your customers' best interests in mind. While the world is melting down over what’s the right answer or the wrong answer, sometimes the best thing you can do is reach out to your trusted advisors, get educated, and then relay that back to your customers so you can lead them through the process. As you do that, you not only build trust and engagement, but you also build long-term relationships. That increases customer lifetime value because they know you're not going anywhere—and they know that you have their best interests in mind. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit chriscrosby.cx [https://chriscrosby.cx?utm_medium=podcast&utm_campaign=CTA_1]