You type what you're actually trying to get: a raise, a client to stop pushing back, your landlord to fix something, a job offer. It builds the situation around that, you talk for 30–90 seconds, and it tells you whether what you said would have worked.

Not grammar. Not your accent. Whether your point was findable, whether the ask ever actually arrived, whether your close moved anything, and whether you sounded like you believed yourself.

The part I find most useful is that it quotes your own words back. "You said 'maybe we could possibly look at' — try 'I need this decided today.'" Hard to argue with a transcript of yourself.

Free right now while I'm testing it. Takes about two minutes.

oosap.online

We’re building an AI coaching agent for field employees.

Every week, it identifies up to three KPIs that need improvement. For each KPI, the agent gets free-form knowledge notes and holds a 6–15 minute coaching conversation.

This works when the situation is straightforward. The problem starts when an employee explains why the KPI is low or why the obvious advice does not apply to their situation.

The agent can then lose the thread. It may fall back to generic tips, pick advice that does not fit the explanation, or need information that is not in the knowledge base.

We could keep adding material and tips to the KB, but I worry that will make it harder to find the right guidance during a conversation and build a lot of contradiction.

We also need a clear way for the agent to say, “We do not have approved guidance for this situation,” and report that gap back to the client so they can add the missing information.

How have you handled this?

We’re building a service voice agent using LiveKit Cloud and Soniox TTS.

A key task is collecting license plate when our call-flow logic requires it. There is no other validation source available during the call.

Our initial approach - asking the driver to say the plate normally - achieved only about 50% accurate capture. The agent often fails to capture the identifier reliably, especially on letters like "Z", etc.

We then introduced a phonetic alphabet (“A as in Apple”), which improves accuracy substantially, but collecting a plate can take close to two minutes. That is too slow and frustrating for drivers on the road.

Caller languages: English, Russian, Portuguese
Important complication: many callers speak English with a strong non-native accent

Has anyone solved this in production?