The question I probably get asked the most: how do you know your voice agent is actually working in production? I think of it like blood work. One marker tells you very little. Grouped, they show a pattern, and the pattern points to what needs fixing. Voice just has more layers.

Is there a GTM tool where you can define your own signals and it just gives you leads based on them? And I don't mean the standard “raised a round, hiring SDRs” stuff. Signals specific to my market. What are you all using?

Never imagine I would come to speak at the college my dad went to. Manipal Institute of Technology :) #TedX Excited for tomorrow ♥️ #MIT

We mapped 16 voice AI companies building in automotive retail. Then we pulled the 5 failure modes we see most in live dealership calls at @SuperBryn_AI. Most of them sound fine on the call. The damage shows up later. Full company list in the thread blow. Bookmark this and let me know if we missed someone.

Building something of our own, together. Gandhi Jayanti spent well 🙏 #theusualSuperBrynshenanigans @devanshtrip @parthjain_1 @DrNeethuJoy

A lot of you have found me here over the last couple of weeks, so probably a good time to introduce myself properly :) I'm Nikkitha, co-founder and CEO of @SuperBryn_AI. We're building the reliability layer for voice agents, and these days I split my time between Bangalore and SF. A few things on how I got here. > Was born in Kerala and grew up in the Middle East. > After graduating, I decided I wanted to become a civil servant and spent 3 years preparing for the UPSC. (failed pretty miserably, btw 😅) > During my final year of preparation, I joined a startup, and somewhere along the way, I fell completely in love with the world of startups. > Eventually started Shoppre, an ecomm cross border tech comapany and grew it to ~$2M in annual revenue. > Shoppre was profitable and scaling, but I knew I wanted to build in AI. So I stepped away from operations (the team runs it beautifully on their own) and chose to start from 0 again. > For almost a year, @DrNeethuJoy and I went through the whole 0→1 mess. > After a lot of iterating, that became SuperBryn. > Raised $1.2M from @Kalaari and angels, and today we work with companies around the world on making their voice agents reliable. > Outside work, I like to dance, make art when I get the time, and give an unreasonable amount of attention to my dog, Django :) If you're new here, hello 👋 I mostly write (or at least try to) about what I'm learning while building SuperBryn: Voice AI, enterprise AI, startups, customers, and occasionally the strange career decisions that got me here.

We tested Jev ourselves: 0.55s median latency from India, too slow to check every turn of a live call. So where does it fit in voice AI? Risky actions mid-call, screening every call after, catching fixes that break what worked. And where it doesn't.

Outbound voice agents are going to be the next big revenue unlock for voice AI. And if you're building one, there is a chance your conversion rate is lying to you. Let me explain: If you give the world's best sales rep a list of dead leads, their close rate will look terrible. But, no one who's run a good sales org would look at that number and conclude the rep is bad. We understand sales depends on more than the rep: lead quality, offer, pricing, brand, timing. But when a voice agent goes live, I often see teams forgettining the same distinction. Conversion drops, the agent gets blamed. Conversion = Lead Quality × Offer × Brand × Timing × Agent Performance Change any one of these and conversion moves. A great voice agent can't make an uninterested lead care. It can't make an expensive offer affordable. And it can't create demand for a product people don't want. The interesting part is that most of these signals are visible inside the conversations themselves. > Are customers objecting to price? > Were leads already disengaged before the agent started selling? > Are certain intents converting better than others? So when conversion drops, you shouldn't be asking what's wrong with the agent?" but instead asking "which variable changed?" Conversion tells you something changed. The conversations tell you why :)