Homebrew makes investments by consensus – it really works as a result of there’s simply two of us. We’ve accomplished it this fashion for 2 causes – first, it really works internally given our model of decision-making and respectful however loud debate. Second, it issues to us externally that founders realize it’s all the time Homebrew making the funding – by no means a scenario the place one in every of us was excited and the opposite one didn’t block it. To that finish, backing Regal, now a frontrunner in AI Powered Calls on your companies’ gross sales, assist and operations, was fast to mutual settlement. Satya and I have been excited by the imaginative and prescient and the cofounders’ earlier work expertise collectively. For the reason that world of AI is shifting fairly quickly, I wished to verify in with Regal CEO Alex Levin to ask 5 Questions.
Hunter Stroll: In a current 5 Questions the particular person I used to be interviewing stated to take into consideration your profession as a narrative you’re telling about your self. What story does your profession inform?
Alex Levin: I bear in mind learning the philosophy and psychology round consciousness in faculty and considering I’d pursue academia. In the future I spotted that I might research my matter for my complete profession and by no means get to the tip of what was already recognized. And at that second, it was crystal clear to me that I needed to discover a profession the place I might get to the forefront of what was recognized extra rapidly so I might assist contribute as a substitute of feeling like I used to be all the time behind.
After commencement, I dove headfirst into tech startups because it was clear they have been investing the long run day by day. I knew that sooner or later I wished to be an govt, and even the highest govt. And I understood that if I didn’t know the way the sausage was made—if I didn’t know easy methods to construct know-how—I’d by no means be eligible for these high roles. So early in my profession, I prioritized being near engineering groups, educating myself easy methods to construct software program, and studying easy methods to be a product supervisor.
Once I got here throughout Marc Andreessen’s article, “Why Software program is Consuming the World” in 2011, it felt like a vindication of the path I had chosen, however I used to be already down that path.
I’ve skilled each giant and small corporations, and I’ve discovered that I thrive in early-stage environments. It’s like how they describe the skilled leagues of any sport: “issues transfer sooner”. Additionally, there’s extra alternative, and you actually really feel like for those who’re not there sooner or later the undertaking doesn’t transfer ahead. I like that sense of accountability.
I’ve been lucky to tackle progressively senior roles, totally on the business aspect, and expertise scale, together with reaching $1.5 billion in income at Angi. And now, I’ve taken all the things I’ve discovered and put it to the check by beginning an organization myself.
Trying forward, I hope my story is one the place I proceed to tackle difficult alternatives, create one thing the place nothing existed earlier than, and construct corporations that really change their markets. And, ideally, not simply as soon as—however many instances.
HW: Just like you, I began Homebrew with a former coworker. How did your relationship with Rebecca evolve and do you bear in mind the ‘we’re going to do that!’ second?
AL: Whereas we have been at Useful, Rebecca and I each had our first youngsters, and I distinctly bear in mind speaking about beginning one thing collectively quickly afterwards. We spent months having normal conversations, however ultimately, we sat down and did a “Founder Relationship Quiz”. We went by means of a listing of 20 to 30 key questions—issues like our working kinds, how huge we wished the corporate to be, what a great exit seemed like, whether or not we wished to boost cash, what might go unsuitable, and what we have been most nervous about. After that, it actually felt extra actual as we knew we have been aligned.
It nonetheless took a while to land on Regal as the corporate we have been going to begin as we had two or three different concepts we critically thought-about (which is a narrative for an additional time). However after we talked to potential Regal prospects about our imaginative and prescient, and the way we wished to vary the best way corporations have interaction with their prospects, the response was overwhelming. Folks have been pulling for it. And as soon as we felt that “market match”, we jumped in quick.
HW: Regal is an AI-driven firm that was based previous to the ChatGPT launch. How have the previous few years accelerated the roadmap? How has it challenged the corporate?
AL: Coming from a background in product and advertising earlier than Regal, Rebecca and I have been used to nice instruments that might faucet into knowledge sources, personalize buyer interactions and even apply machine studying—it wasn’t actually AI again then—to do some fairly sensible issues. In the meantime, in touch facilities, groups have been afraid to vary something of their software program for worry it might all break, not to mention attempt utilizing knowledge for personalization or machine studying. So we knew it might be a little bit of an uphill battle to get contact facilities to vary.
From the very starting in 2020 we had a imaginative and prescient of higher business-customer interactions powered by buyer knowledge + ml/ai + software program for groups to make adjustments simply. In 2020 the avant-garde AI was for aiding human brokers, not autonomously dealing with duties, however as we began constructing, we knew buyer knowledge and orchestration needed to be on the core of our platform, hoping that sooner or later, autonomous AI Brokers might take over the client dialog as a substitute of a human.
For years, although, AI simply wasn’t adequate. However we stored pushing on it as a result of human brokers trigger points as a result of they produce other motivations, weren’t all the time well-trained, might give up, or simply have an off day. The general public would possibly assume people are the gold customary, however anybody who has really operated contact facilities with human brokers is aware of it’s really actually troublesome, and there are many challenges with human brokers.
Then, a few 12 months and a half in the past, all the things modified. LLMs lastly reached a degree the place they might perceive and generate language and make choices much like people. And we bought to some extent that our AI agent demo utterly opened our eyes as to what was potential. We began specializing in creating an “omni-agent” system, one the place each AI and people might function seamlessly. Construct the insurance policies, scripts, orchestration, guardrails as soon as, and deploy them to each human and AI brokers.
A 12 months and a half in the past, not many corporations have been prepared for AI. They nervous about hallucinations, buyer reception, and whether or not it might actually work. However AI brokers have superior extremely quick, and the platform we spent 4 years constructing to assist human brokers take the suitable motion really gave us the best-in-market platform to function AI Brokers and we now have an enormous benefit over corporations simply beginning to construct AI brokers from scratch. So we’re seeing a seachange at client corporations – everyone seems to be evaluating AI Brokers in 2025.
The very best a part of this shift is that it’s made our GTM less complicated. To maneuver human brokers into Regal required ripping and changing their present contact heart software program. Now after we promote Ai Brokers, we roll out with out touching their contact heart software program, accelerating the gross sales course of and implementation.
HW: Content material advertising dominates the enterprise trade today, a lot of it about Synthetic Intelligence. Because you’re down on the sphere with the precise corporations shopping for these merchandise, what’s one thing that we’re lacking? Share an ‘earned perception’ that you’ve got from the work at Regal?
AL: Rebecca and I will not be naturally public individuals, so for years, we didn’t focus a lot on advertising. That was a mistake. With our newly constructed advertising crew (who’ve strongly inspired us to get on our recreation and step up), we’re lastly getting in entrance of extra prospects, and Rebecca and I are beginning to embrace constructing in public.
One easy earned perception is about easy methods to begin testing AI Brokers in manufacturing. Each firm needs to begin small. And most gravitate to small use instances (like out of hours) to check. I all the time advise towards that as even when that succeeds, it received’t have a cloth affect on the corporate and you will want to begin once more with a bigger use case. As an alternative, choose the biggest use case for voice (like your foremost inbound or outbound calls) and check your AI Agent on 1% of calls to begin. Then because it succeeds, scale as much as have affect instantly.
HW: What’s your recommendation for founders who is likely to be beginning corporations right now within the normal AI house – let’s concentrate on those that are utilizing AI to resolve issues for patrons (utilized AI) versus decrease within the stack base fashions or deployment infra.
AL: Rebecca and I are founders and angel buyers within the AI Voice house. You need to assume each firm could have entry to the identical LLMs and voices. The problem, then, is to construct an organization that thrives regardless of this actuality. Stated in any other case, construct a “thick” workflow or utility layer that can present worth it doesn’t matter what the LLM and voices do.
A lot of what Regal focuses on, and what we put money into as angels focuses on role-specific or trade particular workflow instruments for generative AI to resolve issues that LLMs alone can not. For instance, integrating AI into buyer knowledge methods, and enabling actions (like processing funds, updating CRMs, or sending SMS messages). Or making certain AI stays inside its meant scope, offers correct info, and acknowledges when it ought to say, “I don’t know.”
These challenges exist throughout industries and roles, and received’t be solved by LLMs alone.
Thanks Alex! To demo Regal, or study extra about their product, head to their web site. And if you wish to be a part of a quick rising firm that cares not nearly what they’re constructing, however how they’re constructing it collectively as a crew, properly, Regal is hiring.
