Why we built Vozz
For a long time, sales tools have mostly helped around the edges of selling.
CRMs help track the deal.
Call recorders help remember what was said.
Transcription tools help summarize the conversation.
Sales enablement platforms help store the playbook.
Coaching tools help managers review calls after the fact.
All of that is useful, but there’s still a major disconnect.
The actual sales conversation is still happening live, in real time, with a human on the other side of the call. And that’s where the hardest parts of selling happen: asking the right follow-up question, catching a buying signal, handling an objection, remembering what the prospect said last time, connecting their pain to the right solution, and knowing when to slow down or move forward.
I’m in sales myself, and I kept running into the same problem. Most tools could tell me what happened after the call, but they couldn’t help me while the conversation was still happening.
That’s why we built Vozz.
Vozz is a real-time AI sales copilot designed to help salespeople during live conversations. It brings together call context, previous conversations, prospect details, playbook guidance, objection support, and proven sales patterns so reps can show up more prepared, confident, and helpful in the moment.
The goal is not to make sales less human. It’s the opposite.
When AI is used well, it should help salespeople listen better, ask better questions, remember what matters, and create a better experience for the person on the other side of the conversation. Nobody wants to feel like they’re being pushed through a script. Buyers want to feel understood. Salespeople want to feel equipped.
That’s the gap Vozz is trying to close.
We’re building for reps, founders, and sales teams who believe better conversations lead to better outcomes for both sides of the call.
Would love feedback from anyone building in sales, AI, or conversational intelligence.
Latency is the real make-or-break here, a half second delay during a live call is enough for the rep to feel like they're being fed lines instead of thinking on their feet. Testing it against real or simulated calls before trusting it live seems like the right instinct from that commenter. How's it handling a prospect who goes completely off-script, does it actually adapt or fall back to generic playbook language at that point?
The real-time angle is the right bet, and also the brutal one. I build live voice agents, and the thing that separates "demo magic" from "usable in a real call" is latency — the moment a suggestion lands even 2 seconds late, the rep has moved on and the buyer heard the pause. Same honest question as Obasekore above: what's your end-to-end budget for a cue to surface mid-conversation, and does it degrade gracefully when the call goes off-script, or fall back to generic playbook lines? Nail that and the "during vs after" distinction becomes a real moat. Congrats on shipping.
The distinction between helping during the call instead of explaining it afterward is what stuck with me. That's a much more tangible shift than just another AI sales tool.
I can picture a founder asking ChatGPT what can help them handle live objections without hiring a sales coach yet. In that moment they're not comparing features. They're wondering which tool actually makes the next call feel different while it's happening.
I'm curious if you've noticed early users talking about one specific conversation that went differently because Vozz was there instead of describing the product itself.
What stood out to me is that you're moving AI from analyzing conversations to participating in the conditions that shape them.
The harder challenge isn't giving reps more information. It's helping them make better decisions without pulling their attention away from the person they're talking to. If you get that balance right, that's a fundamentally different category of sales tool.
Congrats on shipping, rfirst1. The distinction you are drawing (real-time vs after-the-call) is the right one, but it is also the hardest version of this to build well. The two things I would want proof of before trusting it in a live call: does it keep up without lag when the conversation moves fast, and does its guidance actually reference what the prospect just said, or does it default to generic playbook advice once things get unpredictable?
I do product testing and walkthrough videos for early sales tools. For something like this, I would want to run it against a couple of real or simulated call scenarios rather than a single demo click-through, since that is where a live AI copilot actually proves itself. Happy to talk through what that would look like if useful.