Product Playbook From Idea to $300M ARR: Eilon (Gong)
Co-Founder & CPO @ Gong
With more than US$300M in annualized revenue and a valuation of US$7.5B, Gong is the leading revenue AI platform globally. Co-Founder & CPO, Eilon Reshef, has scaled Gong into one of the most impactful SaaS businesses, redefining how sales teams understand and act on customer interactions.
In this conversation, Eilon reflects on Gong’s journey - from the founding insight to building early product-market fit, from shaping the future of AI in revenue operations to how he thinks about hiring in a world where technology changes week by week.
Q: Let’s start with your journey. How did Gong come about?
Eilon: I’d just come off a sabbatical after selling my previous company when I met Amit Bendov, my co-founder at Gong. Amit had just gone through a poor sales quarter at his previous company and realized something fundamental: CRM systems weren’t telling him why things were happening. Manually entered CRM data - often subjective and incomplete - rarely reflects the true story of what’s happening in a deal, leaving leaders blind to what’s actually going wrong in customer interactions.
The real insight was that the answers were hidden in conversations - calls, emails, chats, even Slack and WhatsApp messages. The problem was that listening manually wasn’t scalable. I was already exploring machine learning in 2015 - before “AI” became the buzzword it is today. The question we asked was: what if you could run a revenue organization based on meaningful data - actual conversations, emails and other engagements - analyzed intelligently? That was the genesis of Gong.
We imagined a layer of intelligence that could automatically capture, analyze, and interpret every customer interaction, and then surface insights that help companies improve forecasting, coach their teams, and manage pipelines more effectively. Over the last 10 years, that vision has grown into Gong as you see it today: a high-growth company tackling critical revenue challenges, one use case at a time.
Q: Gong is often cited as a masterclass in product-market fit. What were the signals you had found it?
Eilon: At the time, it wasn’t obvious that we had achieved product-market fit. When we launched in 2016, we worked with 12 design partners - companies willing to use our very early software and give us candid feedback. After a few months, we went back to them and said: “We’re officially launching, and we’re going to start charging.” Out of the 12, 11 converted into paying customers.
We were actually disappointed - why not all 12? In hindsight, that was an incredible signal. Typically, you’d expect maybe 20–40% conversion. Achieving 90% showed we were solving something urgent. The 12th company also became a customer later, and still is a customer today!
Another strong signal was customer complaints. When customers use your product and care enough to complain - “Why didn’t this call record?” “Why didn’t these action items show up?” - it means the product has become critical to them. Indifference is a far worse sign. Looking back, conversion from pilots to paying customers, coupled with high engagement and feedback intensity, were clear markers that we had real traction.
Q: Many founders worry about overfitting to design partners. How did you avoid that trap?
Eilon: It’s an art, not a science. The key is to work with multiple design partners rather than just one or two. That way, you can identify which requests are generalizable. If a customer says, “Please integrate into my unique internal system,” that’s not broadly applicable. But if they ask for dashboards or reporting, that’s likely valuable for many.
There’s a common fear of building something too custom. But in my view, more companies fail because they build generic solutions that no one truly needs, rather than because they built something too specific. I’d rather make one customer wildly successful than make 100 customers lukewarm. That success often creates the foundation for broader adoption.
Q: Gong helped define “Revenue AI”. Where do you see the category going over the next five years, especially with AI agents emerging?
Eilon: We believe every profession will eventually have an AI-powered system tailored to it. For revenue organizations, Gong aims to be that system: an end-to-end, AI-first platform that becomes the single source of truth and continuously optimizes how companies run their go-to-market operations.
This doesn’t mean traditional software goes away. Instead, we’ll see a hybrid: some tasks done through dashboards and reports, others through conversational AI. The promise of AI lies in two dimensions: efficiency and effectiveness.
Efficiency: Automating manual, time-consuming work. Instead of spending two hours writing an email, you’ll spend two minutes with AI drafting it for you. Instead of filling CRM data manually, it will be auto-populated.
Effectiveness: Helping people get better at their craft. For example, we recently launched an AI-powered training tool. It doesn’t save time - it actually requires reps to invest more time - but it makes them better. That’s something you can’t achieve otherwise.
Over the next five years, I expect “AI for revenue” to become indispensable, just as GitHub Copilot is becoming essential for developers. Gong wants to lead that transformation for go-to-market teams.
Q: Many companies are rushing to add AI features. How do you avoid building “AI for AI’s sake”?
Eilon: Interestingly, sometimes building AI “for show” isn’t a bad thing. If your company wasn’t born AI-first, you still need to get your teams - engineers, PMs, designers - comfortable with the technology. Launching a handful of AI features, even if they’re redundant, creates feedback loops. Customers give input, and teams gain firsthand experience. That sets the stage for more systematic, high-value AI.
At Gong, iteration has been crucial. For example, we launched account briefs that summarize all customer conversations into a single snapshot. Customers loved it - and then asked us to also pull in public company data from the web. We hadn’t thought of that initially, but version one sparked new ideas. You can’t get to the breakthrough features without shipping early versions. It’s about creating momentum and avoiding stagnation in a “local maximum.”
In other words: start fast, iterate relentlessly, and let both customers and your own team guide where to double down.
Q: Finally, how do you think about hiring in the AI age?
Eilon: I think about two profiles. First, you have executors - PMs and engineers who can absorb what’s happening in the field and execute features quickly. That’s relatively straightforward. They need technical skills and the ability to stay up to date, but the role resembles traditional product and engineering positions.
The harder profile is the innovator - people who are hyper-curious and stay at the cutting edge. These are the ones who know what’s happening in AI every week, who read the newsletters, experiment with new tools, and bring fresh ideas to the table. They combine curiosity with enough technical understanding to separate hype from real opportunity. In conceptual or leadership roles, that curiosity is the most important trait. Without it, you stop growing. And in AI, if you stop growing, you fall behind quickly.
The pace is relentless. One day Microsoft releases a new text-to-speech model. The next day it’s a new agent framework. None of these are singular breakthroughs, but if you don’t follow them, you fall out of the loop within months. Hiring people who thrive in that environment is critical.