Category Visionaries How Hamming AI accidentally created a new category by focusing on customer problems instead of category creation | Sumanyu Sharma ($3.8M Raised)
Hamming AI has emerged as a pioneer in voice agent quality assurance, creating what founder Sumanyu Sharma calls a "new category" of QA for conversational voice agents. After spending a decade building data products at scale at companies like Tesla and Citizen, Sharma recognized an acute pain point as voice agents began proliferating: enterprises desperately needed confidence that their voice agents would work reliably before launching to production. In this episode of Category Visionaries, Sharma shares how his team accidentally created a new category by following their instincts and leveraging a decade of expertise in reliability testing, audio processing, and machine learning.
Topics Discussed:
- The evolution from Tesla's data science team to founding a voice agent QA company
- How "wandering the desert" for months led to finding the perfect problem-solution fit
- Building a completely inbound-driven go-to-market strategy in an emerging category
- The decision to launch before feeling ready and building alongside customers
- Why the voice agent market skeptics were wrong about market size
- Creating enterprise trust through reliability testing at scale
GTM Lessons For B2B Founders:
- Follow your instincts when you have deep domain expertise: Sharma spent months "wandering the desert" looking for the right problem until voice agent QA clicked. He emphasizes that when you have a decade of relevant expertise, you can recognize the perfect problem when it appears. As he put it, "when you see it, you kind of know... I am perfectly equipped to solve this specific problem. I'm built for this." Founders should trust their instincts when they have genuine domain expertise rather than overthinking market validation.
- Build something people want before focusing on category creation: Unlike many founders who start with category creation in mind, Hamming AI "accidentally" created their category by obsessively solving customer problems. Sharma notes, "We weren't looking to create a category. We were just looking to solve a problem that we feel passionate about, that we are already experts at." This customer-first approach led to organic category emergence and sustainable demand.
- Launch before you feel ready and build with customers: Sharma's biggest learning was launching with a "half-baked" product rather than perfecting it in isolation. "We didn't have a product that we thought was incredible. We just thought, hey, it kind of works, but let's actually build the product together with customers." This approach accelerated learning cycles and created stronger product-market fit than months of internal development would have achieved.
- Leverage contrarian insights from deep market proximity: While others dismissed voice agent QA as "too small," Sharma's data science background and proximity to builders gave him conviction. He analyzed the fundamentals: "Voice is a universal API for people. Voice agents are just becoming possible. They will be unreliable. Therefore, testing is very important. That's the math." Founders should develop conviction through first-principles thinking rather than consensus market opinions.
- Focus obsessively on customer success over marketing in emerging categories: Hamming AI remains completely inbound-driven, focusing entirely on making existing customers successful rather than traditional marketing. Sharma explains, "The voice space is so small where if you are doing a good job and if you build a product that people love, they will tell their friends about it." In nascent categories, product excellence and word-of-mouth can be more effective than broad marketing campaigns.
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