Variant A
LiveOrder confirmed → offer add-on
+6.2%avg ticket
Learn which voice AI behaviors drive better outcomes.
Your voice AI talks to customers all day. BotBot lets you change how it talks, test that change on real conversations, and see what it did to your numbers. No engineering ticket.
Works with the voice stack you already have.
Built for the calls that carry revenue: Ordering, booking, scheduling, upsell, service recovery
Upsell timing
Experiment · day 12
Variant B
Rolled backMid-order → offer add-on
+11sslower
Backed by
The problem
Every call today ran on a guess somebody made before launch.
The greeting. How fast it talks. When it offers something extra. What it says when an order goes wrong. Someone picked all of that in a doc months ago, and it has run that way on every conversation since.
Nobody knows whether it was right. And changing it means filing a ticket and waiting a month, so nobody finds out.
We have watched a single behavior change move ticket size and satisfaction at the same time. We have also watched changes do nothing at all. The only way to tell them apart is to run it and look.
Meanwhile the questions keep piling up:
Does that upsell line grow the order, or does it just make the call longer?
Is a warmer greeting worth the eight seconds it costs you?
When something goes wrong, what wording keeps someone from hanging up?
What you get
Change how it behaves, in plain English.
Tone, pacing, persona, promos, how it handles a rush versus a quiet afternoon. You describe what you want in normal words and switch it on.
Your engineers keep the flow in code through the SDK. Everybody else works in the UI. Nothing gets redeployed.
Lunch-rush upsell test
Avg order value · 11am–2pm · 42 stores
BUpbeat Uma
$11.20+14.3%sig.A
$9.80baselineClassic Casey · control
What works at Burger Stop — learned from 12 experiments
Higher upsell intensity+9.4%
Faster voice at lunch+3.1%
Warmer greeting (+8s)-1.8%
The part that matters
Then find out what it did to your numbers.
Run two versions against live traffic and BotBot pulls in what actually happened. Order value, booking completion, handle time, whichever number you told it to care about. Location by location and hour by hour if that is how you operate.
Do that for a few months and you end up with something useful: a record of what works on your customers. Not on customers in general. Yours.
How it fits
Bring your own voice stack, or use ours.
Both setups work, and the part we handle is the same either way.
You already have an agent
BotBot plugs into it. Your model, your voice provider, your runtime all stay where they are. Three lines to integrate on Pipecat, Deepgram, ElevenLabs, or OpenAI.
You would rather not build one
We can supply the agent as well, already wired up for testing and measurement from the first call it takes.
What we own is the piece nobody owns today, which is working out what good behavior looks like for your brand and then proving it. An analytics tool tells you what happened last week. This tells you what to try next.
The brands that win with voice AI will not get it right on the first try. They'll learn faster.
In a 15-minute walkthrough, we'll show you:
Live behavior controls
Experiments down to a single location
Side-by-side performance comparison
How behavior changes affect speed, completion, and what customers do next
No pitch deck. Just the product.