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jawns ◴[] No.44974805[source]
Full disclosure: I'm currently in a leadership role on an AI engineering team, so it's in my best interest for AI to be perceived as driving value.

Here's a relatively straightforward application of AI that is set to save my company millions of dollars annually.

We operate large call centers, and agents were previously spending 3-5 minutes after each call writing manual summaries of the calls.

We recently switched to using AI to transcribe and write these summaries. Not only are the summaries better than those produced by our human agents, they also free up the human agents to do higher-value work.

It's not sexy. It's not going to replace anyone's job. But it's a huge, measurable efficiency gain.

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dsr_ ◴[] No.44974877[source]
Pro-tip: don't write the summary at all until you need it for evidence. Store the call audio at 24Kb/s Opus - that's 180KB per minute. After a year or whatever, delete the oldest audio.

There, I've saved you more millions.

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paulddraper ◴[] No.44975017[source]
This works unless you want to automate something with the transcripts, stats, feedback.
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Spivak ◴[] No.44975133[source]
Why wouldn't it, once you actually have that project you have the raw audio to generate the transcripts. Only spend the money at the last second when you know you need it.

Edit: Tell me more how preemptively spending five figures to transcribe and summarize calls in case you might want to do some "data engineering" on it later is a sound business decision. What if the model is cheaper down the road? YAGNI.

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1. paulddraper ◴[] No.44977492[source]
The "last second" is right after the call.

For example, if 60% of your calls this month mention assembly issues with the product, that information will help you improve it.

This is practical, not theoretical.