We built Ringg because headcount cannot scale calls
We started Ringg in 2023 after seeing B2B sales teams limit pipeline, not from weak leads, but from the finite hours reps can spend on calls.
Good reps spent 70 percent of the day on calls that did not need a person
Ringg's first version tested one question: can an AI agent open, handle an objection, and book without a rep picking up? We ran it on a partner's lead list in late 2023. Call quality surprised us. The qualifier rate surprised us more.
Since then, our focus has been simple: make the voice natural and the outcome dependable. Product decisions return to that: latency, accent handling, CRM sync, and DLT compliance for Indian numbers. Each is needed for a call that converts.
Voice AI that handles calls, not just scripts
Most voice bots follow decision trees. Ringg holds a conversation. That matters: a tree fails when a caller says something unexpected. A conversation agent asks follow-ups, handles objections, and moves toward the goal.
We are building for sales and support teams of any size to run calls at the volume they need, rather than the volume their hiring budget permits.
The team behind Ringg
Before Ringg, built and scaled outbound sales operations across several B2B software businesses. Ran high-volume pipelines where the constraint was always the human call floor. Started Ringg to remove it.
Worked on speech processing and NLP systems, low-latency inference, and accent-robust recognition for non-US English speakers. Brings depth in Indian-language acoustic modelling and real-time voice pipeline architecture.
Before Ringg, built and shipped call-centre workflow products at a telecom software firm and CRM platform. Focuses on integration, fitting Ringg into sales teams' existing tools without a two-week setup project.
Now hiring
Seeking engineers who care about voice quality and product managers with high-volume operations tools experience.