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LATEST POSTS 📚

If you’re new, not yet a subscriber, or just plain missed it, here are some of our recent editions.

🍿 Inboxed: Tech Partnership, Tech Poaching. A behind-the-scenes look at the technology hiring wars.
⚙️ Why AI Is Easy To Demo And Hard To Trust. An interview with Hamel Husain, Founder at Parlance Labs.
🚀 Hyperbound Goes Hyperdrive: A Zero To One. Creating the AI-sales training industry and taking names along the way.

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HOUSEKEEPING 📨

I feel like every so oftenm industries just get totally revamped and brought into the future. I think that’s what we are seeing at the minute with Attio, who just launched Ask Attio. I can literally log into my CRM now and talk to it like it’s a seasoned VP of sales.

In my Mixpanel deal, which was essentially dead, Ask Attio scoured the deal, pulled together the timeline, and suggested the best way to rekindle our relationship. I’ve tried it on a load of deals, and it’s pretty impressive how quickly and intelligently you get up to speed. Technology is pretty cool, I must admit. Anyway, enjoy today’s piece!

ZERO TO ONE 🌱

Unfiltered: Hyperbound From The Inside

Recently, I told the story of how Hyperbound went from ‘two engineers with too many ideas’ to a company moving at a speed that honestly looks fake from the outside. This time, I wanted to hand the mic over.

What you’re about to read is the unfiltered cut. Same journey, but through four different lenses: Sriharsha on the pivots, the velocity, and the moments that changed the whole company, Atul on the 2,000 interviews and the pain points that actually mattered, Jonathan on what it takes to build a role-play product that feels real, and Mia on the chaos of being employee #5 and turning marketing into a one-person operating system.

Sriharsha Guduguntla (Sai), Co-Founder & CEO at Hyperbound

Sriharsha co-founded Hyperbound straight out of school and led it from an early idea into Y Combinator and, not long after, into the enterprise. From the beginning, his focus has been on moving quickly, staying close to customers, and figuring things out in real time rather than waiting for perfect answers.

A lot of Hyperbound’s trajectory reflects how he operates as a founder. Decisions are made close to the problem, feedback loops are tight, and progress matters more than polish. Whether it’s product direction, sales motion, or hiring, his approach has been consistent: test fast, learn quickly, and keep pushing.

What was the initial idea?

The very first idea for Hyperbound started when Atul and I began the company in June 2023. We didn’t really know what we wanted to work on yet.

Even though we were testing those 17 ideas, the one thing we realized through the process was that sending emails really sucked. So we did what technical founders do: we built a script to write the emails for us and send them automatically. At first, I didn’t even want to use the script. I thought AI wouldn’t do as good a job as hand-personalizing. But Atul used it; I did mine manually, and it became a competition. The script worked.

Pretty quickly, we realized we were onto something. Other founders in our YC batch started asking if they could use our script because they were also trying to send a huge volume of emails. That’s when we started building the product. It naturally fits sales teams, especially BDRs, because that’s where high email volume lives. So the first idea for Hyperbound was essentially an AI SDR, even though we didn’t call it that yet.

Why is that not Hyperbound today?

After working on the email idea for a couple of months, we actually had strong early traction: 10 customers and $250k in ARR. So it wasn’t a lack of interest. The turning point happened at a conference. We were at a round table with several CROs, and one of them asked how two engineers managed to get 10 customers and $250k ARR in three months, especially since we had never done sales.

We told him we practiced our talk tracks and objection handling on an AI bot we had built. He was shocked. At the time, the bot responded every 15 seconds, which is slow compared to today, but even that concept blew him away. His first reaction was: “Are you selling this? The email idea is fine, but reps still need to get on calls, and nothing can replace that. Coaching reps is way more valuable.”

Hyperbound, as it exists today, came naturally once we put those puzzle pieces together.

Once you pivoted, how did you find your ICP, and has it changed?

Our ICP has changed a lot over time, and honestly, it’s still a work in progress. The economy changes, the market changes, so naturally, your ICP shifts as well. When we first launched Hyperbound on January 21st, 2024, we were just trying to close anything we could. We were excited because the launch went viral and our calendars were booked for six months. At first, that seemed amazing, but once we started taking those calls, we realized a lot of them were unqualified.

That forced us to think seriously about what a qualified deal actually looked like. Early on, because we wanted initial traction, we closed tiny startups with just a couple of reps or random SMBs with fast sales cycles. But once we looked at usage metrics, it became clear that Hyperbound, at least the AI role-play product, wasn’t suited for SMBs.

It was far more valuable for larger companies with big teams, high churn, and constant rep turnover. So around mid-2024, we started tightening our ICP. We introduced a minimum of 20 reps just to qualify for a demo request. And we weren’t focusing on any particular industry yet. As we started closing more enterprise deals, we kept moving up-market. Recently, we bumped the minimum from 20 reps to 50 because the larger the company, the stronger the use case. Industry-wise, B2B SaaS is big for us, but staffing and insurance have also become big sources of traction. We’re also seeing a lot in 3PL and transportation/logistics.

Who was on the founding team, and why did you choose those specific people?

We’ve always kept the team lean. Even after raising our Series A, we still only have 14 employees. So choosing early people was extremely deliberate.

In March 2024, three months after launch, we hired our first engineer, Luca. He was a former colleague from Bloom, where I was a founding engineer. We already had chemistry, and he was incredibly fast. I gave him the offer, and the next day, he was in the codebase shipping features. That same weekend, he built a core feature customers were begging for, completely on his own. That level of energy and speed was exactly what we needed.

Next, we hired Jonathan from LeanData, where he was Head of Data Science. He was pivotal because he’s a machine learning engineer who’s also great with customers. In the early days, we didn’t have a CSM, so having someone who could speak to customers and build at the same time was huge. We then hired our designer out of the UK, who completely redesigned the app from the ground up. That was a big moment for our product quality and speed.

What was the best thing you did in the zero-to-one stage?

The best thing we did was adopt the mindset of ‘*sell before you build’*. It wasn’t one decision; it was a philosophy that shaped everything we did. Technical founders tend to default to building because it’s comfortable. But a lot of time gets wasted building things no one ends up wanting.

Atul and I always asked: “Why is this important to build, and who wants it?” If enough people wanted something badly, then we built it, fast. Being customer-driven saved us months of wasted effort and ensured we only built things that actually mattered.

What is something that bombed or didn’t play out as expected?

Post-sales. Everyone focuses on ARR, revenue, pipeline, and top-of-funnel. It’s easy to de-prioritize what happens after the deal closes, but that always catches up to you. Now that we’re deep into renewal cycles, there are deals where we look back and realize we could have done better with implementation, support, stakeholder alignment, and ongoing check-ins.

Moving fast early on meant we didn’t prioritize post-sales as much as we should have. Now, especially with huge companies like IBM and LinkedIn rolling out thousands of reps, post-sales is critical. We’re now investing heavily in account managers, CSMs, implementation specialists, solutions engineers, and support engineers because long-term usage and renewals depend on it.

What is the story behind you singing songs in the organization?

Atul and I originally met in high school choir, so music has always been part of our story. I also have a YouTube channel, TikTok, and Instagram where I post song covers—Indian covers, English covers, everything.

It’s something I love doing on the side. In another life, I would have loved to be a professional artist. The team jokes about it internally, but it’s genuinely one of my passions.

Atul Raghunathan, Co-Founder & CTO at Hyperbound

Atul co-founded Hyperbound and leads the technical direction of the company. Before starting Hyperbound, he worked in conversational AI and machine learning, including roles at Meta Ads and as an NLP researcher at Carnegie Mellon.

At Hyperbound, he focuses on turning customer input into systems that can scale. He led much of the early discovery work, structuring thousands of interviews into clear problem areas and using that data to guide product decisions and pivots. That approach has shaped how the team thinks about product scope, ICP, and what to build next.

How did you actually conduct 2,000 interviews?

We conducted a little over 2,000 interviews, but the crazier part is that we reached out to 35,000 people to land those 2,000. During the YC batch, my co-founder and I focused heavily on this. We would get on LinkedIn every day, seven days a week, and max out our connection requests. We sent emails until our domain reputation started to suffer. Anyone in the space was fair game, and everything was manually personalized in the beginning. It was eight to ten-hour days of pure outbound asking people to interview with us.

The goal wasn’t to validate a specific idea but to understand the pain points in the market. One of the first things we did was read ‘*The Mom Test’*. The idea was not to confirm what we already believed but to collect a set of pain points that could inform the ideas we should test next. We were a step earlier than most founders who start user interviews to validate something specific.

As we went through the interviews, we got much better at outbound. Eventually, we even built that first email-personalization assistant just to help us book interviews. Along the way, we realized that the pain point behind that assistant was real, but email personalization wasn’t the right solution. The interviews helped us generate a clear set of pain points around rep behavior, which is what ultimately led us to Hyperbound.

What were the main pain points?

The real answer is more technical and less glamorous than people expect. We approached it like a machine learning engineer doing feature analysis. We had a massive Excel spreadsheet: on one side, everyone we interviewed; on the other, around 200 checkboxes representing potential pain points. As people walked us through their daily, weekly, quarterly, and annual challenges, we checked boxes and looked for patterns, not just in volume but in who the pain points were coming from. We looked for clusters to understand whether certain markets shared similar issues.

If I had to summarize the high-level insights, we found that it’s extremely hard to close the gap between top and bottom performers. It’s hard with 10 reps, and nearly impossible with 1,000 or 10,000. The gap becomes even worse when the product is technical and requires both business and technical acumen. It’s further aggravated when companies sell platform products—you lose a lot of potential revenue in cross-sell and upsell, and deal cycles get longer because reps don’t position correctly.

So if I had to condense everything, closing performance gaps, selling technical products, and maximizing platform revenue were the biggest pain points. The original solution we pursued wasn’t the right one, but the pain points themselves were real and consistent, which is what ultimately guided the pivot to our current product.

Has the ICP changed as you’ve progressed?

Absolutely. There are a couple of factors to consider when setting your ICP. You have to think about who actually has the problem and who will be interested enough to buy quickly, but you also have to consider the realities of the business.

I break it down into two buckets: your ‘immediately addressable market’ and your ‘total or ideal addressable market’. We naturally shifted from our immediately addressable market—early adopters who moved quickly through procurement and gave us fast product feedback—to the end users who have the strongest long-term use case.

Practically, that meant moving from SMBs with 10–30 reps to enterprises with thousands. We even have a customer with more than 20,000 reps. We also learned that our product isn’t limited to B2B SaaS. We worked with the world’s largest diamond brokerage, the world’s largest freight brokerage, companies selling physical therapy services, even staffing solutions. Many of them saw even more success with our product than typical SaaS companies. That helped shape the broader ICP.

What was the best thing the team did early on?

At one point, I was doing almost 35 hours of weekly check-ins myself. But that’s what allowed us to keep our ear to the ground, build exactly what customers needed, and end up with extremely strong NRR heading into our Series A: 177% NRR in a space where you’d normally expect half your customers to churn.

Another big part of this was having post-sales operate as part of the product team. We hired engineers specifically for post-sales so they could hear customer problems and solve them immediately. An hour after a call, a customer might already have the fix or even a new feature. It wasn’t sustainable for long-term product direction, but in the early days, it created intense customer love and allowed us to move incredibly fast.

Why are you slow to hire?

We’re slow to hire because we’re extremely particular about who we bring on. Our hiring process itself is fast, but our rejection rate is very high. It all comes down to the type of person who fits a founding-style role, someone who’s comfortable with a job that has no playbook, who can own a revenue target or product goal without knowing exactly how they’ll get there.

Even when we have a strong candidate flow, it can still take months to find the right person. It took almost eight months just to hire our second designer. We had plenty of good candidates, but they didn’t meet the standard for what it means to be a founding designer; someone who can work with engineering without tightly defined scoping, product leadership, or structure. That’s why we’ve consistently been behind on hiring.

Mia Kosoglow, Founding Marketer at Hyperbound

Mia joined Hyperbound as employee #5 after deciding that sales wasn’t her long-term home and taking a leap into marketing, despite never having formally done marketing before. She went from SDR at Outreach to running Hyperbound’s entire marketing function solo for nearly a year.

Her role has been less ‘strategy deck’ and more real-time operating system. Events, competitions, content, experiments—if it could work, it got tested. Mia’s perspective matters because she’s lived the product from both sides: she knows exactly why sellers hate traditional role-play, and she’s been responsible for turning that pain into demand, pipeline, and brand at speed.

When did you join Hyperbound, and why?

I joined in August of 2024. I think I was employee number five. We debate in the office about who is which number because a group of us joined at roughly the same time, but I think I solidified myself in the top five, which is pretty awesome. Before being in marketing, I was an SDR at Outreach, and being an SDR was actually a big influence on why I started working at Hyperbound. I was in the middle of changing careers into marketing because I realized sales wasn’t for me. I wanted to exercise the more creative side of my brain, and marketing felt like the natural next step.

I started talking to as many marketing people as possible to understand what they did, what they liked, what niche they worked in, all that good stuff.

Then I had a Zoom call with Sai and Atul. I think there was an immediate connection because all of us were doing things for the first time ever. Sai and Atul had never founded a company before, and I had never really done marketing before. My marketing experience was slim to none. There was this excitement on the call, like we were all in it together, figuring it out, coming in with fresh perspectives, not running the same playbooks everyone else runs or repeating things from the last 50 companies. None of us had that. It felt exciting to know we were in the same boat.

Their confidence in me, even though I hadn’t done this before, was really reassuring. It made me excited to join because I knew I’d be in a position where I could test things, fail, learn, and try again. I was the first and only marketer for almost a year, so I knew I’d be able to do a bunch of stuff and see what stuck. And as an SDR, I saw the value in Hyperbound. I hated role-playing with reps. I hated it. If I had something like Hyperbound, I would’ve gotten way more comfortable on the phone much faster. Even now, the thought of doing a cold call makes me sweat. That experience helped my transition from sales to marketing because I understood the product’s value firsthand and felt like I could market it effectively.

How did the initial comms and marketing strategy come together?

It was very much, ‘Let’s throw shit at the wall and see what sticks,’ especially in the beginning. When I joined, I asked if I could meet the co-founders and align on strategy, and they basically said, “Yeah, we don’t really have that.” Then they said, “By the way, we have a call competition next week and no promo for it, so we need you to do all of that.” I had just started, but okay, let’s do it.

We did a lot of experimentation early on. Then we went back and focused on more basic foundational things for the first half of the year. Now we’re transitioning back into more experimental stuff. We’re still very fast-moving, but now with more data to guide us.

What percentage of budget do you allocate toward performance vs testing?

Yes. With our marketing budget, we always leave room for experimentation. Most of the budget goes to events because we’ve seen those work consistently. But around 15% of our budget is allocated to experimentation at all times. That number has stayed pretty steady, and when I’ve asked other marketers, 15–20% seems to be a common range.

How do you do events, why they work, and how well they work?

Our event strategy is very much centered around not doing booths at every event. Booths take a ton of time and internal resources. Instead, a lot of our events are built around Sai showing up, speaking, and establishing himself, and Hyperbound, as an industry leader. Sometimes we do a booth. Sometimes we just send Sai to speak on panels with customers. Early on, events were experimental because we didn’t do any for the first year and a half. Then we stacked the calendar with as many as we could and figured out which ones were worth our time.

Because we’re targeting enterprise and upper mid-market, events are the right environment. Those audiences love attending in-person events. Events give you in-person connection moments, and that kind of touchpoint impacts deals in a way you can’t quantify. Some of our biggest deals were facilitated at events or got a major push from event interactions. Even when we don’t have a booth, those touchpoints matter a lot, especially for enterprise deals.

What’s the reason Hyperbound has been so successful, in your opinion?

I think our team is really solid, and I could brag about our team for a long time. The team is what pushes us to move as quickly as we do. We all really love being here and building together, and I think that’s awesome.

It laid a foundation that made my job easier when I joined. I came in with a good base to build on because they had already done that work.

What does virality mean when you say it?

The space is so new. AI role-play didn’t exist before 2024. The tech literally was not possible to build before then. We hit the perfect wave: we were the first to do it, and no one had ever seen this before. It blew up because it showed a completely new way you could use AI. Especially for sellers who are always looking for tools that help them do their job easier and get better faster. We basically handed them something on a silver platter. We recorded their reactions, how they felt, and how surprised they were. That fueled a lot of the virality.

Because it was new, not a reinvention of something that already existed, people paid attention. Now the space is more crowded, which sucks, but it also validates the category. The fact that so many competitors are coming in proves there’s a real reason to be here and invest in this space.

What thesis didn’t play out as expected?

It was actually one of the first big projects I owned, and I was working with a lot of external people to bring the vision together. We host call competitions, and one of the reasons Hyperbound went viral early is that a call competition we ran did really well. When I joined, we ran another one. Then we decided to create a third one with a different angle—the goal was to show people that we weren’t just a cold-calling role-play tool. We wanted to highlight discovery calls, demos, renewals, and other call types.

We ran the competition in January. People talked about it all the time, and it was really cool, but not as many people participated as we hoped. I think I got a little too big for my britches thinking people with full-time jobs would want to spend that much time on a project that didn’t directly help them do their job. It wasn’t like, ‘Do our competition and get 60 really good leads.’ It was fun and awesome, but it didn’t produce the results we expected. People still bring it up, but it didn’t hit the mark in terms of direct inbound.

Are there any fun or interesting cultural rituals the company does?

Yes. There’s a card game called Coup, the whole point is to kill off everyone else and be the last one standing. The game is entirely based on alliances, deceit, lying, telling the truth, and strategic gameplay. If there were a game we’d sponsor, or that would sponsor us, it would be Coup. Every time someone new joins the company, we force them to play it as their initiation. A lot is said about you based on how you play Coup.

There are alliances, backstabs, and chaos. I personally love being an agent of chaos, like I’ll look someone in the eye and say, “I won’t kill you next turn,” and then kill them on the next turn. It’s fun and chaotic, and it's kind of our company game. Every so often, we’ll call everyone together on the couch in the office, and we all play Coup and lie to each other. It’s awesome. Games usually last around 15 to 20 minutes. Different cards give you different abilities: to kill, defend, steal, whatever.

Why do you still work at Hyperbound?

In every all-hands, Sai and Atul remind us that the position we’re in is extremely rare. Because we’re surrounded by so many successful startups and because people on LinkedIn constantly talk about ‘amazing things’ they’re doing, it can be hard to recognize that our situation actually is special. It starts to feel like, ‘Doesn’t everyone do this well?’ But Sai and Atul always emphasize that only a tiny percentage of companies make it to where we are, and we’re in that tiny percentage. It’s a unique position, and it’s really cool to be part of something where you have real influence over the success of the company.

I feel blessed to work here. I get to learn so much, and they trust that things will get done, even though I’m doing many things for the first time. I don’t think there’s another environment where I could fail forward like this. And we’re crushing it. It feels good to work somewhere that’s winning. I love to win and, honestly, I hate to lose more than I love to win. And we don’t lose very often. I also love my team. I like working here, which I feel like so many people can’t genuinely say about their job.

Jonathan Tran, AI / ML Engineer at Hyperbound

Jonathan joined Hyperbound as one of the earliest engineering hires, bringing a background in data science and machine learning. Before Hyperbound, he worked in B2B SaaS and completed a Master’s in data science at UC Berkeley.

At Hyperbound, he has focused on building and expanding the core role-play product, working closely with customers to make sure the system reflects real sales conversations. His work has shaped many of the product’s key features, including how role-plays are structured, how feedback is generated, and how the platform scales to support enterprise teams.

When did you join Hyperbound, and why did you join?

I joined Hyperbound in September of 2024. At the time, I was finishing a data science Master’s at Berkeley and figuring out what I wanted to do next. I was already in B2B SaaS and knew I wanted to be at a company that was AI-forward. Hyperbound fit that perfectly.

I also spoke with one of their angel investors. He actually hired me at my previous company, and he told me that Sai and Atul were working on something really cool. I originally talked to him in April, and the timing wasn’t right yet. But later, when they started hiring, he connected me with them. I interviewed, it felt like a great mutual fit, and that’s how I ultimately joined.

What stage was the product at when you joined?

It’s funny you ask that, because when I was interviewing, I did a lot of research and noticed they were talking about outbound email and SDR workflows. I think the original name came from ‘hyper-personalized outbound,’ which became Hyperbound. But by the time I interviewed, they were already working on the conversational AI role-playing product. So for the most part, the product when I joined is the product as we know it today.

There have just been a lot of improvements, new languages, more sophisticated call types, new variations within those call types, and additional features that support a fuller learning program rather than a single role-play.

What have been the most impactful releases in your time at the company?

When I joined, the general framework for building a conversational AI bot was already in place. Since then, we’ve focused on adding context and more conversational options while trying to abstract them so any B2B SaaS or tech company can use them. For example, call types were much more limited back then. We’ve added more call types and more emotional states. Early on there were only options like ‘nice,’ ‘less rude,’ and ‘rude.’

For example, talking to a head of procurement and a VP of sales simultaneously, trying to get your champion to push the deal through procurement. That’s been a very interesting development.

And also the Chrome extension, which makes it easier to build a bot. Today, enablement leaders curate lists of role-plays for reps. But with the extension, if you’re on a prospect’s LinkedIn page and want to practice with ‘them,’ you can generate a bot on the spot. It takes their job title and LinkedIn information and creates a version of that prospect for you to practice with before the call.

What’s the stack, and how did you actually build the product?

At a high level, we’re trying to be the premium product in the market. So the stack depends on what’s best at any given time. Hyperbound breaks into two main parts. The conversation itself and the AI feedback you get after the conversation. Some models are better for conversational flow; others are better for analyzing the call afterward. So we choose the best model available at that moment. Sometimes OpenAI is better, sometimes Claude is better, sometimes we need a huge context window, and Gemini is the right choice. There’s no single model we always use.

On the voice side, we’re also provider-agnostic. We really like ElevenLabs, but there are others like Cartesia and Hume. We try to pick whatever has the best voices at the time. Interestingly, the ‘highest-quality’ voices aren’t always best for us. If a voice is too clean and polished, it sounds less realistic. Sometimes a slightly muffled voice or one with pronunciation quirks actually feels more human, like someone you’d really talk to on the phone. So counterintuitively, a less ‘perfect’ voice is sometimes better for our product.

What went super well in product development, and what didn’t go so well?

One thing that really moved the needle was building the AI scorecard builder. Earlier, I talked about the two halves of the product: the conversation and the scoring. On the post-sale side, we kept hearing: “I love the conversations. They’re realistic. But how do I build an effective scorecard for my team?” We were hitting a bottleneck because building a good scorecard wasn’t intuitive unless you had done it multiple times. We kept getting on calls to help customers write the prompts. People weren’t sure if they were doing it correctly, which slowed adoption.

The AI scorecard builder changed that. Now, instead of starting from zero, customers can upload artifacts from their sales process or enablement materials, describe the kind of scorecard they want, and get a baseline with 10–15 criteria they can then tweak. That made things dramatically easier for them, and that went really well.

As for what didn’t go so well, I’d say this more generally: sometimes we ask customers for feedback during a weekly check-in, but those conversations aren’t always useful. When you ask someone what could be improved, they haven’t been thinking about it for ten hours; they’re just answering in the moment while juggling an entirely different job. We’d sometimes build exactly what they asked for, and then it wouldn’t be used as much as we expected, or it would only apply to a subset of customers. So I’ve learned it’s not always about giving customers exactly what they think they want. It’s about giving them what they need, even if they don’t yet know how to articulate it.

Extra reading / learning

• How Hyperbound is redefining sales training with AI roleplays - October, 2025

• Hyperbound Goes Hyperdrive: A Zero To One - February, 2026

And that's it! You can keep up with Sai, Atul, Mia, and Jonathan on LinkedIn and check out Hyperbound on their website.

BRAIN FOOD 🧠 

TWEETS OF THE WEEK 🐣 

TOOLS WE RECOMMEND 🛠️

Every week, we highlight tools we like and those we actually use inside our business and give them an honest review. Today, we are highlighting Attio—powerful, flexible and data-driven, the exact CRM your business needs.

See the full set of tools we use inside of Athyna & Open Source CEO here.

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