How Radiyal Launched a New Product Category With Queue

Radiyal is building infrastructure for real-time voice personalization — a category that barely existed two years ago. When the founders set out to launch, they had two engineers, no room to hire, and a Series A runway that demanded efficiency on every front. But the product they were building was technically deep — distributed systems, low-latency streaming, and complex ML integration. Two engineers working alone couldn’t build it in time. They needed a way to multiply output without multiplying headcount.

Category creation requires speed

The team adopted Queue from the first week of serious development. Agents handled API integration scaffolding, generated test suites for each new module, kept documentation current, and took on the majority of routine refactoring. The two engineers focused exclusively on the hard problems: the distributed coordination logic, the low-latency pipeline architecture, and the ML model integration patterns that were actually novel. When early investor feedback shifted the product’s core focus, the team realigned their Queue agent workflows within a day. No agency briefing. No consultant onboarding. The product moved at the speed of their thinking.

"The product moved at the speed of our ideas because Queue handled everything that wasn't core innovation. For a team our size, that changed everything."

Ethan Brooks

Co-Founder & CTO, Radiyal

Compound returns on small teams

Launch week exceeded every expectation. The product shipped on time with test coverage their Series A investors noted as unusually thorough for an early-stage team. Code output per engineer was 4.8× what comparable two-person teams typically achieve at the same stage. More importantly, the quality held — no critical production incidents in the first three months, which is rare for a system operating at Radiyal’s level of technical complexity. Queue agents didn’t just write more code. They wrote consistent, well-covered, well-documented code.

Raising on the back of a working product

Three months after launch, Radiyal closed a $9M Series A. Multiple investors cited the codebase during technical due diligence — noting the test coverage, the architectural clarity, and the documentation depth as signals of a team that operated well above its size. One partner told the founders: ‘Your engineering output looks like a team four times your size.’ For a category-defining startup competing against well-funded incumbents, that signal was invaluable.

Still building, still fast

Eighteen months on, Radiyal’s team has grown to six engineers — but their Queue usage has scaled with them. New engineers onboard faster because agent-generated documentation is thorough and current. Feature branches stay cleaner because agents handle test coverage before review. The founding team’s philosophy hasn’t changed: focus human judgment on the hardest problems, and let Queue handle everything else.