A common failure mode in launching an AI voice agent has nothing to do with the model: a business writes the script from what the team believes customers ask, the agent goes live, and it stumbles in week one on questions nobody wrote down — not because the AI is weak, but because the script was a guess.
The fix is not a cleverer prompt. It is better input: the same calls your human team is already taking, read closely before anyone writes a word of script.
The script that gets written from a guess
Left alone, a script gets assembled from what marketing thinks the pitch is, what the founder remembers hearing once, and whatever the last few complaints happened to be. Each of those is a real signal and none of them is a sample — they are what stuck in someone's memory, not what actually happens on the average call.
The gap this leaves shows up exactly where it is most expensive: the agent handles the pitch it was written for, and stalls the moment a caller asks something the script's authors did not think to include.
The script that gets written from a transcript
The alternative is mechanical, not clever: read what your own team already says on real calls, at full coverage rather than a handful someone happened to remember, before writing anything down.
Every one, not the few anyone happens to recall.
The real objections and questions, not the ones assumed in a meeting room.
What works, what gets fumbled, what never gets answered at all.
Not from a guess about what a caller might say.
The agent's first script should read like your best rep's best week, not like a guess about what customers probably ask.
What this catches before launch, not after
Read at full coverage rather than a sample, existing calls surface the objection that actually kills the sale most often — not the one a meeting room assumed it would be — along with the question your knowledge base has no answer for yet, and the pace and tone that a real conversation on your line actually runs at. Locator's own cross-call analytics group exists for exactly this: recurring requests, typical objections, and where deals are lost, pulled from calls that already happened rather than guessed at — the same full-coverage argument digital quality control makes for a human floor applies just as directly here.
Most AI rollouts do not fail on the model
McKinsey's 2025 State of AI survey found 88% of organisations using AI in some form, and only 6% reporting real value from it at scale. The gap between those two numbers is rarely the technology. It is usually that the thing got deployed without being grounded in what actually happens in the business it was meant to help.
A voice agent is the same story with a phone line instead of a workflow. The agents that hold up on a real call were briefed on real conversations before they went live; the ones that stumble were briefed on an assumption about what a caller would say.
Locator is how you get the transcript before the agent
Locator listens to 100% of a team's existing calls and scores them on 30+ parameters — the same groups that later train a neuro-agent: sales stages, objections, script adherence, and where deals are actually lost. It is deliberately usually the first thing we build, because the agent that follows it is trained on real conversations from a client's own call history, not a canned script.
The order that actually works
Listen first, on 100% of the calls you already have. Script second. Launch third.
None of this requires committing to an agent before you have looked. Locator runs standalone as often as it runs as the first step — what it shows you decides which one makes sense.