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BotBot

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

Triggers list grouped by behavioral, temporal, and contextual conditions

Backed by

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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.

Voice AI behavior locked in before launch

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.

Brand experience controls: promos, regional tone, personas, dynamic specials, and daypart tailoring
Behavior changes feeding into charts of business outcomes
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.