Manual AI evaluation took days, version mismatches created inconsistent results across engineer setups, and enterprise customers demanded rigorous security compliance including SOC 2 Type II certification.
Standardized development on Conda wrapped in Docker for consistent environments, automated compliance workflows, and centralized package management that enabled ISO 27001 and SOC 2 Type II certification while reducing setup time from days to hours.
How Hyperbound Built Enterprise-Ready AI Sales Coaching on Conda's Foundation
When Atul Raghunathan and his co-founder started Hyperbound, they knew their AI sales coach needed to do something most conversational AI couldn’t: push back. Real sales prospects aren’t polite. They’re skeptical, they have objections, and they don’t make it easy. But getting an AI to behave that way—across thousands of different scenarios—proved to be one of their biggest technical challenges.
“It’s very hard to get AI to be negative, to act like a real human would in high-intensity sales conversations,” explains Atul, CTO and co-founder of Hyperbound. “AI is tuned to be subservient, to be nice. That’s where having a robust evaluation framework that can shift the behavior of the models became critical.”
That evaluation framework is the system that tests whether Hyperbound’s AI actually sounds realistic. It would become the backbone of the company’s competitive advantage. But before Hyperbound standardized on conda, it was also its biggest bottleneck.
The Eyeball Test That Didn't Scale
In the early days, Hyperbound’s evaluation process was painful. Instead of automated testing, the team relied on manual observation, or what’s known as the “eyeball test.” They would retrain a model, adjust some prompts, and then spend hours calling it to see if it exhibited the right behavior. “It was like the eyeball test, but very painful,” Atul recalls.
For an AI-first company where conversation quality is the key differentiator, slow iteration cycles were a dealbreaker. “If we slow down the iteration cycles of our engineering team because evals are taking too long or are too manual, we just can’t keep up,” Atul says.
The problem went beyond just testing speed. Each engineer had a slightly different local setup, and version mismatches created unpredictable results. When Founding ML Engineer Jonathan Tran traveled for a conference and his laptop was damaged, he worried he wouldn’t be able to work on a critical customer issue. “I was like, oh no, I’m not going to be able to do this,” he remembers.
Building On What Just Works
The solution came from technology Atul had relied on for over a decade. “I’ve been using Anaconda for more than ten years at this point,” he says. “Honestly, I can’t even tell you where I first heard of it. It’s just always been the standard brand-name package manager.”
That familiarity bred confidence. “When you’re starting a company, there are so many uncertainties, so many things that are going to kill you or stop you in your tracks,” Atul explains. “You just want the default trusted package manager to have your back. You want to reduce the attack vectors on your company, and going with Anaconda just takes one more burden off my list.”
For Hyperbound, that meant standardizing everything in conda and wrapping it in Docker. Their entire simulation environment now runs within a conda-managed environment—everything from high-level feature engineering to simulated conversations for evaluation and testing. “Conda became the quiet backbone of our realism testing,” Atul notes. “It lets us validate model behavior faster and ship more lifelike AI sales reps without breaking anything. Every engineer gets the exact same environment, so we know our test results are consistent and our improvements are real, not just artifacts of different setups.”
From Days to Hours
The impact was immediate. Before conda, getting a new engineer set up with their evaluation environment took days. “We were running everything locally, and we didn’t have Docker set up either,” Atul says. “The amount of version issues we ran into, knowing that all the packages we had installed were compliant with the security standards we were going for—it was just a lot of manual work, a lot of checking.”
Now, it’s effortless. “We went from cautious iteration to rapid experimentation,” Atul explains. “Our engineers can now push and validate improvements in hours, not days.”
For Jonathan, the change meant true independence. “It makes it easier for not all the load to be on me,” he says. “If another engineer needs to take something over, they can replicate the environment and my setup and do any debugging they need to. I don’t need to be there for them to set anything up.” And when his laptop was damaged during that conference trip? He simply spun up a new environment on a different machine. Problem solved.
The Enterprise Pressure Test
As Hyperbound pursued larger customers, a new set of challenges emerged: Enterprise security teams wanted answers about package management, license compliance, and audit trails. “Reliability, security, and the safety of working with a vendor who knows what they’re doing makes all the difference,” Atul says.
Conda’s centralized package management became a critical asset. “Having licenses and all of our package management done in one centralized source helped us achieve ISO 27001 and SOC 2 Type II,” Atul notes. For a startup trying to sell to companies with 200 to 500 employees and larger, those certifications open doors that would otherwise remain closed.
“It’s very different when you’re speaking to an enterprise security or procurement team and you’re able to speak to the reproducibility and the package management and the license protection you’ve implemented as policies going back to the start of the company,” Atul explains. “We’re treated more like an established vendor rather than a new startup. When you’re growing fast and selling to companies that are 200 to 500 employees and larger, that can make all the difference.”
Automate the Basics Early
Looking back, one of Hyperbound’s key mistakes was waiting too long to automate compliance. “It took days to get engineers set up with everything in a compliant manner. Automate compliance early. Don’t leave compliance checks to be manual, especially on something as basic as package management. It just makes your life easier.”
His advice to other AI startups hitting similar challenges? “Take the time to set up the right infrastructure early on. There’s always that trade-off you have to make in the early days. ‘Do I take the time to set this up right, or do I allow myself to move fast?’ There are a couple of decisions you can make that allow you to both move fast and set things up the right way. Conda is one of those no-brainer decisions you should make early on.”
A Full Circle Moment
Today, Hyperbound is helping Anaconda’s sales team improve their own performance. It’s a collaboration Atul finds particularly meaningful. “Having Anaconda be a customer of Hyperbound is a very full-circle moment for me,” he reflects. “The piece of technology I grew up with back when I was a teenager—installing environments and managing them with conda for my first hobby projects—that same company is now using Hyperbound to manage some of the largest growth in their sales team in recent history.”
As Hyperbound scales toward a microservices architecture and deploys multiple Python servers across various environments, having trusted unified package management continues to pay dividends. “It just takes one large burden off our plate,” Atul says simply.
For a team laser-focused on staying six to 12 months ahead of competitors through rapid experimentation, that’s exactly the kind of infrastructure foundation they need: Invisible when it’s working, reliable when it matters, and never slowing them down.
About Hyperbound
Hyperbound is an AI sales coaching platform that helps B2B SaaS sales teams practice conversations with realistic AI personas before engaging with actual prospects. Founded by machine learning experts, the company recently closed its Series A funding and serves enterprise customers who demand both cutting-edge AI capabilities and rigorous security standards.
About Anaconda
Anaconda is built to advance AI with open source at scale, giving builders and organizations the confidence to increase productivity and save time, spend, and risk associated with open source. 95% of the Fortune 500, including Panasonic, AmTrust Financial, and Booz Allen Hamilton, and over 50 million users rely on the value the Anaconda Platform delivers through a centralized approach to sourcing, securing, building, and deploying AI. With 21 billion downloads and growing, Anaconda has established itself as the gold standard for Python, data science, and AI. It’s the enterprise-ready solution of choice for AI innovation.
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