- प्लैटफ़ॉर्म
- YouTube
- अवधि
- 2:34
- बोली जाने वाली भाषा
- अमेरिकी अंग्रेज़ी
- व्यूज़
- 1.9 हज़ार
- औसत कट
- 8.09s
- स्क्रीन पर चेहरा
- 40%
- गहरे फ़्रेम
- 19%
Inside Notion's Multi-Agent System for Customer Feedback की ट्रांसक्रिप्ट
फ़िल्म के अपने ऑडियो से एक स्पीच मॉडल द्वारा लिखा गया, ब्रांड द्वारा नहीं। उस पल पर जाने के लिए किसी भी पंक्ति पर क्लिक करें।
- 0:00Hey, I'm Sandra, and today I'm going to show you a series of custom agents that I built within Notion
- 0:05that track our top feature requests coming from our customers.
- 0:09We've actually named each agent after a Sesame Street character,
- 0:12just to keep the agent interaction really fun.
- 0:15This system creates a monthly report that shapes our product roadmap,
- 0:19and we call it the Go-To-Market Top 10.
- 0:21So let's get started.
- 0:26Our first agent is the Intake Agent.
- 0:28It picks up the new gong call, reads the transcript, and
- 0:32asks itself one question.
- 0:34Is there a feature request in here?
- 0:36Let me show you an example of this agent running in real time.
- 0:40Nine minutes ago, we got a gong call, and our agent screened it and
- 0:44realized there was no feature request detected, so it labeled it as rejected.
- 0:49Doing its job.
- 0:52In the case that a feature request is detected on a call,
- 0:56the extractor agent wakes up.
- 0:58Now this agent pulls the exact quote from the transcript.
- 1:02For example, let's pull up this prior run.
- 1:05We can see that this feature request was detected from
- 1:08the CISO about admin controls.
- 1:13The next agent in our system is the matcher agent,
- 1:16which does a few things.
- 1:18First, it looks across our existing feature request database,
- 1:21which lives in Notion.
- 1:22And it either matches a request with something we're already
- 1:24tracking or recommends that we create a new one.
- 1:29Next comes the logger agent.
- 1:31This agent helps us link the feature request to the matcher's recommendation.
- 1:36It associates the extracted quote and customer information from a Salesforce
- 1:40worker with the feature request.
- 1:45We have our extracted quote,
- 1:46we know which feature request to associate it to, and we have our business metrics.
- 1:51So now the reporter agent brings it all together.
- 1:54It compiles all the feature requests from all the sources I mentioned into a digestible data-backed report on the first of every month.
- 2:02This is great because it ties back feature requests to real customer quotes, with sources, and to business impact.
- 2:11I love handing this work off to custom agents because it's important work that I couldn't imagine us doing any other way.
- 2:17With hundreds of hours of calls every month, this system filters a noise and
- 2:22gives us a clear data-backed picture of what customers are actually asking for.
- 2:27And as for your feedback, keep it coming and see you next time.
40 पंक्तियाँ · 406 शब्द
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