We Built an AI Support Agent That Resolves 80% of Tickets — AssemblyAI

频道: AI Engineer
视频: https://www.youtube.com/watch?v=pyvRID_CZZU
原文语言: en
统计: 共 16 轮 · Matt 12 · Joey 1

[0:00] Matt

Cool. [music] All righty. Can everyone hear me? Okay. All All levels coming through good. All right. Sweet. Well, uh, how's everyone doing? How's everyone doing? Better. Okay, there we go. Cool. Well, I hope uh the conference has been good for everybody. Appreciate you all being here to to listen to a chat from uh from me. Um for anyone who hasn't met me at the at the booth yet, uh my name is Matt Lawler. I'm a forward deployed engineer at Assembly AI. Uh mostly focused on bringing in inbound customers, um helping win their business, u making sure that they have a good time building with our API. Um so, how how many other like forward deployed engineers are out in the audience right now? Any of us? Okay, a couple. Cool. Well, um, still coming through. Okay. All right. Um, yeah. So, I wanted to make sure I I gave you guys an overview of kind of some of the go to market engineering stuff that we've been doing at Assembly AI that maybe you can apply to your own FTE team or maybe you can apply to your company um, as something that you can do as an easy win for your customers. Um, so for a little bit of overview of like assembly AI in case you haven't heard of us, um, we provide voice AI infrastructure. Um, so we train our own foundation models for speech to text primarily. Um, but we're starting to branch into other parts of the voice agent space. Um, so if anyone's ever had like a Fireflies note-taker join your meeting or something like that, you've also you've already seen a transcript from Assembly AI. Um, so we train our own foundational models not only for like async use cases like meeting recordings and things like that, but also for real time uh real-time like voice agents. Um, so if you've ever used like a a phone customer support agent when you called in somewhere and it wasn't a human or like drive-through ordering, they're more than likely building with Assembly AI's models. Um, and for anyone who's building a voice

好。（音乐）好嘞。大家听得到我吗？行，音量都正常。好，不错。那，大家今天怎么样？大家怎么样？——这回好多了。行，这就对了。好。希望这次大会大家都有收获，也谢谢各位过来听我聊一聊。还没在展位上见过我的朋友，我先自我介绍一下：我叫 Matt Lawler，是 AssemblyAI 的 FDE（forward deployed engineer，前线部署工程师），主要负责承接 inbound 客户，也就是主动找上门来的客户，帮公司把他们的生意拿下来，确保他们用我们的 API 做开发时体验顺畅。那现场还有多少位同行，也是 FDE 的？有吗？好，有几位。不错。呃，声音还正常吧？好，行。是这样，我想给大家讲讲我们在 AssemblyAI 做的一些 go-to-market 工程方面的事，也许你可以用到自己的 FDE 团队上，或者用到你们公司，当成一件能让客户轻松受益的事来做。先简单介绍一下 AssemblyAI，万一有人没听说过我们：我们提供语音 AI 基础设施。我们自己训练基础模型，主要是语音转文字，不过也开始往 voice agent 这个领域的其他环节延伸。所以如果你开会的时候进来过一个 Fireflies 的会议记录助手之类的东西，那你其实已经看过 AssemblyAI 出的转写稿了。我们自己训练的基础模型，不只面向会议录音这类异步场景，也面向实时场景，比如实时的 voice agent。所以如果你打电话到某个地方，接电话的是个客服 agent 而不是真人，或者你用过那种免下车点餐（drive-through），那它们十有八九是用 AssemblyAI 的模型搭的。另外，现在正在做 voice

[1:54] Matt

agent now, you can use us for the entire stack, not just speech to text. Um, so you can use our voice agent API and we'll string together the speech to text, the LLM, and the text to speech as well. Um, as our competitors know, as we know, as anyone in the voice AI space knows, demand is absolutely insane um, for voice AI right now. Um, so assembly at our current moment, we see around a thousand API signups every single day. Um, which is somewhat of the theme of why we were focusing on automation and wanting to build kind of a new support bot for our team because at least until like two days ago, I was the only uh onboarding engineer at Assembly AI. And so you can imagine, as much as I'd want to talk to a thousand new signups every single day, that's just not feasible. Um, so we had to build something to to solve that problem. And then for anyone here who doesn't know like what a forward deployed engineer is, it's a very catch-all title. It varies a lot at every different company, but effectively we want to get deployed with our end customers. We want to understand their use case. We want to make sure that we are building exactly what they need. Um, so we frequently commit code directly to their repositories. We usually meet with them several times a month, if not a week. Um, and we really try to understand their use case and get to know them very personally. Um so like at least on the inbound side for anyone who comes into assembly who wants to build with us we are the people um that are their main point of contact for anything they need on the technical commercial side anything that they might need they get from us their FTE um and we're responsible for winning their business um and building that trust with them.

agent 的朋友，整套技术栈都可以用我们的，不只是语音转文字。你可以用我们的 Voice Agent API，语音转文字、LLM、文字转语音，我们帮你全串起来。我们的竞争对手知道，我们自己知道，语音 AI 这个圈子里的人都知道：眼下语音 AI 的需求简直疯了。就 AssemblyAI 目前来说，我们每天大约有一千个 API 注册。这也多少就是今天的主线，我们为什么盯着自动化、想给团队造一个新的客服机器人：因为至少到两天前为止，我都是 AssemblyAI 唯一的 onboarding 工程师。所以你可以想象，我再怎么想每天跟一千个新注册的用户聊，那也根本做不到。所以我们必须造点什么来解决这个问题。另外，在座如果有人不清楚 FDE 到底是什么：这是个什么都能往里装的头衔，各家公司差别很大，但本质上，我们是要被「部署」到终端客户那边去的。我们要理解他们的使用场景，要确保做出来的正是他们需要的东西。所以我们经常直接往客户的仓库里提交代码，通常一个月跟他们开好几次会，甚至一周好几次。我们是真的会去吃透他们的场景，跟他们处得很熟。所以至少在 inbound 这一侧，凡是来找 AssemblyAI、想用我们的东西做开发的人，我们就是他们的主要对接人，技术上的、商务上的，他们需要什么，都找我们，找他们的 FDE。我们负责把他们的生意拿下来，也负责跟他们建立起那份信任。

[3:20] Matt

So it's a great services model. It's great uh if we can actually get that kind of relationship with every customer, but again with like a thousand API signups every day, I I can't be one to many serving that many customers and getting on that many calls. Um and so ultimately this doesn't really scale super well. Uh we want to make sure that we're freeing ourselves up to be leading uh humanled deals as much as possible, but we can't do that with every deal. Not every customer win can be like handbuilt by us. Um and we can't just continue to hire more and more FTEEs if we're going to try and serve more and more customers. Um, so ultimately like for anyone who's an FTE in here, if you're looking to become an FTE, if you already have an FTE team at your company, I would encourage you to take that knowledge that you have as an FTE and try to automate yourself out of a job. I think that should be your role every single day. If you want to serve your customers better, if you want to work with them more, um, and if you want to deliver a better customer experience at scale, you can't be the bottleneck to having a good customer experience. And we learned that firsthand. So again, we were drowning in like inbound um, volume. Um, you can see like we basically had a thousand API signups up there earlier. Um, and we had the natural instinct that a lot of teams do where we were like, we just need to deflect more conversations. I just want to talk to fewer people. I want to make sure I'm freeing myself up to work on more important stuff. Um, so we basically bought this off-the-shelf support bot and we're like, let's point it at the docs. It'll answer all the docs questions for us. It's going to be great. We'll never have to answer a simple question from customers again.

所以这是一种很棒的服务模式。要是真能跟每个客户都建立起这种关系，那当然好，可还是那句话，每天一千个 API 注册，我一个人没法一对多地服务这么多客户、上这么多通电话。所以归根结底，这套模式不太能规模化。我们希望尽量把自己腾出来，去主导那些需要真人来带的单子，但不可能每一单都这么做，不是每拿下一个客户都能靠我们亲手一点点做出来。而且要服务越来越多的客户，我们也不能就这么一直招越来越多的 FDE。所以说到底，在座的不管你本身就是 FDE，还是想成为 FDE，还是你们公司已经有 FDE 团队了，我都鼓励你把自己作为 FDE 攒下的那些知识拿出来，想办法把自己的工作自动化掉，让自己「失业」。我觉得这应该是你每天的职责。如果你想把客户服务得更好，想跟他们有更多合作，想在规模化的前提下交付更好的客户体验，那你自己就不能成为好体验的瓶颈。这一点我们是亲身领教过的。还是那句话，我们当时快被 inbound 的量淹没了。刚才屏幕上大家也看到了，我们基本上每天有一千个 API 注册。我们当时的本能反应跟很多团队一样，就是：我们得把更多对话分流出去。我就是想少跟一些人聊，想把自己腾出来去做更重要的事。于是我们基本上就买了一个现成的客服机器人，心想：让它对着文档就行了，所有文档类的问题它都能替我们答，多好啊，以后再也不用回答客户的简单问题了。

[4:41] Matt

And it only resolved about 10% of the conversations. Um, so it helped. Um, you can imagine if we had like a thousand conversations a day, a hundred of those were now entered by a bot. But then our team still had to field 900 tickets a day. So it wasn't exactly very scalable. Um, so 10 10% uh end to end resolution rate wasn't good enough. And ultimately we couldn't iterate fast enough to be able to fix this. We didn't have access to the system prompt. We didn't have access to the tools. We didn't have access to the rag infrastructure. So anytime we needed to change something, we'd go to this vendor and they'd say, "Well, it's on the road map." and that didn't quite work for us. So, we wanted to build our own. So, we built uh another member of our team rather than hiring one and we named him Joey. Um so, anyone now who reaches out to us, whether it's on our chat widget on our site, whether you reach out to contact sales, whether you reach out to our support alias, you're going to meet Joey instead of a human. Um he's the first line of defense now for every single conversation that happens in bounded assembly. And he's built to be the most infinitely scalable FTE on our team. Um, so he remembers past conversations with you. Um, we track all of his metrics via Pylon because he's still a member of our support team and so he has access to Pylon to manage conversations and remember who you are.

结果它只解决了大约 10% 的对话。有帮助，确实有。你可以想象，假如我们一天有一千个对话，其中一百个现在由机器人回答了，可我们团队每天还是得处理 900 张工单。所以这实在谈不上有多能规模化。10% 的端到端解决率是不够的。而且说到底，我们迭代的速度不够快，没法把这个问题修好。我们碰不到 system prompt，碰不到工具，碰不到 RAG 基础设施。所以每次需要改点什么，我们就去找这家供应商，他们就说：「嗯，这个在路线图上了。」这对我们来说行不通。所以我们想自己造一个。于是我们没有再招一个人，而是给团队「造」了一位新成员，给他起名叫 Joey。所以现在任何人联系我们，不管是通过我们网站上的聊天窗口，还是去联系销售，还是发到我们的 support 邮箱，你遇到的都是 Joey，而不是真人。现在，每一个 inbound 到 AssemblyAI 的对话，他都是第一道防线。我们造他，就是要让他成为我们团队里最能无限扩展的 FDE。他会记得之前跟你聊过什么。他的所有指标我们都通过 Pylon 来追踪，因为他毕竟还是我们客服团队的一员，所以他有 Pylon 的权限，可以管理对话，也记得你是谁。

[5:52] Matt

Um, and he's built on top of the cloud agent SDK. And so the cool things that he's able to do is he can manage his own infrastructure. He has a file system. He can write and debug code for you. He can call tools. He can give himself new abilities. Um, and so he's actually able to operate more like an FTE and less like just a standard chatbot that you'd be talking with, you know, on on most major sites nowadays. So, um, if you want to know a little bit of like how to build your own Joey, um, the architecture kind of has like four major parts. Um, so as everyone might know from working with Claude, Markdown is your friend. You should be giving Markdown files to Claude so that he understands everything about your business. And so we wanted him to have access to all of our documentation and be trained up on it all the time. So, we checked all of our docs out as local markdown and gave it to Joey. Um, and so anytime that we push an update to our documentation system, he's able to get all of those files and he has them locally in his file system. So, actually what's really cool is if our entire doc site was down right now, you could go to Joey and still get live answers on all the newest stuff that we've shipped without our team having to actually get involved. um for retrieval so we can give you a good answer. We partner with Voyage so we use their embeddings um so that he gets the most relevant documents up front so he doesn't have to spend a lot of time searching and he can get you a faster answer. And then since he has all these docs as local files if he gets confused or he needs to string together multiple resources to create a response he can agentically search the file system that he lives on so that he can go and find those resources for you. Um so he can kind of go into this like deep thinking mode where he's able to search across all these resources and then we shipped it on railway. We didn't want to

他是搭在 Claude Agent SDK 之上的。所以他能做一些很酷的事：他能管理自己的基础设施，他有一个文件系统，他能替你写代码、调试代码，他能调用工具，还能给自己添加新能力。所以他干起活来其实更像一个 FDE，而不像你如今在大多数主流网站上聊到的那种标准聊天机器人。那么，如果你想了解一下怎么造一个你自己的 Joey，这套架构大致有四个主要部分。用过 Claude 的人应该都知道，Markdown 是你的好朋友。你应该把 Markdown 文件交给 Claude，让他了解你业务的方方面面。我们希望他能访问我们全部的文档，并且随时都对这些文档了如指掌。所以我们把所有文档都 check out 成本地 markdown，交给了 Joey。这样每次我们往文档系统推送更新，他都能拿到所有这些文件，就存在他本地的文件系统里。所以特别酷的一点是，就算我们整个文档站现在挂了，你去找 Joey，照样能拿到关于我们最新上线的所有东西的实时回答，而且不需要我们团队出面。检索这块，为了能给你一个好答案，我们跟 Voyage 合作，用的是他们的 embedding，这样他一上来就能拿到最相关的文档，不用花很多时间去搜，也就能更快地给你答案。然后，因为这些文档他手里都有本地文件，所以如果他搞糊涂了，或者需要把多份资料串起来才能拼出一个回答，他可以对自己所在的文件系统做 agentic 搜索，替你把那些资料找出来。所以他可以进入一种类似深度思考的模式，在所有这些资料里搜索。然后我们把它部署在了 Railway 上。我们不想

[7:23] Matt

deal with infrastructure. We didn't want to spin up an EC2. We didn't want to have to change instance types. So, we deployed it all on Railway. Their service is pretty incredible. Basically, we can just go ahead and see if he has a bad conversation with someone in Slack. We can write a PR real quick and we can deploy it and within 30 seconds there's a new live version of Joey. So, we're able to iterate really, really quickly. Uh we've actually had times where we've actually monitored a live conversation, caught a bug, deployed a fix, and then it's been proof for the rest of the session, and that customer doesn't even have to know that we we shipped that fix behind the scenes. Um so Joey is able to to basically get updated whenever we want. Um so we actually have full control over everything that that he does. Um and so the results were pretty remarkable from us building this. And it's another reason why and wanted to encourage everyone who's here to try and build your own Joey. So, we went from 10% to 80% endto-end resolution rate in just the first week of deploying this this build with a pretty naive implementation. And we did that all for around $700 a month in both token and inference uh or infrastructure costs.

折腾基础设施，不想起一台 EC2，也不想到时候还得换实例类型。所以我们把它整个部署在了 Railway 上。他们的服务相当厉害。基本上，我们在 Slack 里就能看到他是不是跟谁聊得不好，然后很快写一个 PR、部署上去，30 秒之内就有一个新版本的 Joey 在线上跑了。所以我们迭代得非常、非常快。我们其实真有过这样的时候：盯着一场正在进行的对话，逮到一个 bug，部署一个修复，然后这场对话剩下的部分用的就已经是改好的版本了，而那位客户甚至都不必知道我们在幕后上线了这个修复。所以 Joey 基本上是我们想什么时候更新就能什么时候更新，他做的每一件事，我们都有完全的掌控。我们把这个东西造出来之后，效果相当惊人。这也是我想鼓励在座每一位都去试着造一个自己的 Joey 的另一个原因。我们的端到端解决率从 10% 升到了 80%，而且就是在这个版本部署上线的头一个星期里做到的，实现方式还相当朴素。而做到这一切，token 加上推理，呃，或者说基础设施的成本，一共大约每月 700 美元。

[8:22] Matt

So, we resolved 80 80% of our our tickets end to end with no human in the loop. And some people like to game these metrics. They'll say like, oh, we resolved 80% but only on like a subset of types of tickets. Not the case here. you cannot reach a human directly unless you go through Joey and you tell him to escalate. So he actually does handle 100% of these inbound tickets for us now. Um and he only escalates 20% of them to a human for the right reasons. Maybe it's a rate change. Maybe it's a data opt out. Maybe you need to sign an agreement with us or you have needs that only an FTE or someone from legal needs to be involved in. These are things that actually still need a human but eventually we can automate. Um and so the best part is whatever Joey can't do yet gives us a very clear list of what we need to do to continue to scale our team. Um, if he's not able to send them a BAA, well, okay, maybe we should give him a link that he can reference as a resource. Um, if he's not able to talk to customers about pricing, well, you can negotiate with Joey. Um, if you go on the site and you test him out afterwards, you can tell him exactly how many hours you have. Um, and he'll actually quote you a rate and then you can negotiate with him. Um, so we tried to automate a lot of these other things that he was still escalating and there's still there's still plenty more to come.

所以，我们 80% 的工单是端到端解决的，全程没有人工介入。有些人喜欢在这类指标上玩花样，会说「哦，我们解决了 80%」，但其实只算了某几类工单。我们这儿不是这样。你没法直接找到真人，除非先经过 Joey，并且明确让他升级。所以现在进来的工单，他确确实实是 100% 都在接。其中只有 20% 会被他升级给人工，而且都是有正当理由的：可能是要调整费率，可能是要做数据退出（data opt-out），可能是你得跟我们签一份协议，或者你的需求只能由 FDE 或法务的人来介入。这些事眼下确实还需要人来做，但最终我们都能自动化掉。最妙的地方在于，凡是 Joey 现在还做不了的事，就等于给了我们一张非常清晰的清单，告诉我们要让团队继续扩展下去，接下来该做什么。他没法给客户发 BAA？那好，也许我们该给他一个链接，让他当资料来引用。他没法跟客户谈价格？现在你可以跟 Joey 讲价了。你们会后要是上我们网站去试试他，可以直接告诉他你到底有多少小时的量，他真的会给你报一个费率，然后你还能跟他砍价。所以，那些他原本还在往上升级的事情，我们已经试着自动化了不少，后面还有很多要做。

[9:29] Matt

But the real thing that, you know, we wanted to test out by building Joey was we wanted to dog food our own product, too. We didn't want to just like build something that deflects more stuff from our customers. We didn't want to build something that just um is kind of this general support bot that anyone can build. We work in voice and our customers build voice agents. So, I wanted to build a voice agent and so I wanted to give a voice to Joey. So, now rather than just chatting with him via text, you can actually chat with him using your voice as well. Um, so we have a voice agent API as I mentioned earlier. Um, and we integrated this into Joey this week. So now if you go onto our site, you can switch to voice mode and you can talk to Joey using just your voice. Um, because I ultimately think for any FTE here, no matter how much you've been deployed with your customers, no matter how embedded you are with them, the best way to understand what your customers are building is to actually build their same product yourself. And that's exactly what we wanted to do with this. So we put our voice agent API into Joey. Um, it's all speech in, speech out, one websocket connection where we string together our speechto text model, an LLM and text to speech, all optimized for voice. It's real-time and low latency.

不过，我们做 Joey 真正想验证的一件事，是我们也想 dogfood 自己的产品。我们不想只做一个替我们把更多客户问题挡掉的东西，也不想做一个谁都做得出来的通用客服机器人。我们是做语音的，我们的客户做的是 voice agent。所以我想做一个 voice agent，也就是想给 Joey 一副嗓子。于是现在你不光能用文字跟他聊，还能直接用语音跟他聊。前面我提过，我们有一个 Voice Agent API，这周我们把它接进了 Joey。所以现在你上我们的网站，可以切到语音模式，只靠说话就能跟 Joey 交流。因为说到底，我觉得对在座的任何一位 FDE 来说，不管你在客户那边部署了多久、嵌得有多深，要理解客户在做什么，最好的办法就是自己动手把他们那个产品也做一遍。我们做这件事，想要的正是这个。所以我们把 Voice Agent API 放进了 Joey。它完全是语音进、语音出，只用一条 WebSocket 连接，我们在里面把自家的 speech-to-text 模型、一个 LLM 和 text-to-speech 串在一起，全都针对语音做了优化。实时，而且低延迟。

[10:31] Matt

It handles all the pauses, interruption handling, barge, all of that kind of stuff for you. U, we bring our own voices, so you don't have to find another provider. And it's all the same tools that he already had if you were communicating with him via text. It's just now you can actually talk to him like you were a human. Um, and hopefully later this week we can hook them up to a phone number. So now we'll have an Assembly AI phone number so you can get real-time support from Joey whenever you need it. So let's go ahead and talk to Joey a little bit and we can walk through some of the architecture live. Um so for anyone who wants to know a little bit more about how this was actually built um we basically have all of the the docs checked out locally in Markdown. Um so every single time someone puts an update out to our docs, these files also get locally checked out into this repo. So they stay perfectly in sync with any new features that we're shipping, our change log, our pricing, all of these major uh pages from our website get converted to markdown and get given to Joey. Um so he sees all of these in markdown format and he's able to see what the URL is so he can site his sources to the customer in case they want to double check his work or if they just want more information.

停顿、打断处理、barge-in（用户插话打断）这一类事情，它全都替你处理好了。我们自带音色，所以你不用再去找别的供应商。而且他能用的工具，跟你用文字和他沟通时已有的那些一模一样，只不过现在你可以像跟真人说话那样直接跟他讲。顺利的话，这周晚些时候我们还能给他接上一个电话号码。到时候就会有一个 AssemblyAI 的电话号码，你什么时候需要，都能从 Joey 那里拿到实时支持。那我们就来跟 Joey 聊两句，顺便现场过一遍架构。想多了解一点它到底是怎么搭出来的朋友可以看这里：基本上，我们把所有文档都以 Markdown 的形式 check out 到了本地。所以每次有人给我们的文档推一次更新，这些文件也会跟着 check out 到这个仓库的本地。这样一来，它们就能跟我们发布的任何新功能、changelog、定价保持完全同步，我们网站上这些主要页面全都会被转成 Markdown，交给 Joey。所以他看到的都是 Markdown 格式，而且能看到对应的 URL 是什么，这样他就能向客户注明出处，方便客户想核对他说得对不对，或者只是想多看点资料。

[11:33]

Um at the same time, um all of it is provided as like Claude MD instructions. I think it's like a 30,000 line Claude MD of all these guard rails and and advice on how he should be operating with customers. Um, and this is what we majorly update whenever we see that he had a bad experience with a customer or gave a wrong answer, we ship an update to this cloud MD and now for every conversation that he has going forward, he's able to be better. Um, and it's all contained as a Docker file. Um, and so it's really easy to go and just containerize and deploy him on Railway. Um, and so again, every time we ship an update to this repo, a new version's already live on Railway and no one had to manually monitor the deployment. Um, so let's go ahead and uh I'll fire up Joey here. we can ask him a pretty typical question so you can see what our voice agent API is like. So I'm going to simulate this locally because I I know probably many of you are going to test Joey after this and I don't want to add to the queue for our actual support team. Um so I'll I'll go ahead and just do a local simulation of it. But typically, yeah, you would go into here, you'd meet Joey, you'd want to have a typical question. You might talk to him via text, but let's go ahead and talk to him with voice. Hi, this is Joey at Assembly AI. How can I help you today?

Matt： 与此同时，这一切都是以 CLAUDE.md 指令的形式提供给他的。我记得那是一份大概 3 万行的 CLAUDE.md，里面全是各种 guardrails（护栏），以及他该怎么跟客户打交道的建议。我们主要更新的也就是这个文件：每当发现他跟某个客户聊得不好，或者给了个错误答案，我们就给这份 CLAUDE.md 发一次更新，此后他的每一场对话都能做得更好。另外，整套东西都装在一个 Dockerfile 里，所以很容易就能把他容器化，然后部署到 Railway 上。还是那句话，我们每往这个仓库发一次更新，新版本就已经在 Railway 上线了，不需要谁去手动盯着部署。好，那我这就把 Joey 启动起来，我们问他一个很典型的问题，让大家看看我们的 Voice Agent API 是什么样的。我会在本地模拟，因为我知道在座很多人待会儿多半会去试 Joey，我不想给我们真正的支持团队的队列再添负担。所以我就直接在本地模拟一下。不过正常情况下，对，你会从这里进来，见到 Joey，带着一个常见的问题。你可以用文字跟他聊，不过我们这次就用语音跟他说。

Joey： 你好，我是 AssemblyAI 的 Joey。今天有什么可以帮你的？

[12:41] Matt

Hey Joey, I'm looking to build an ambient medical scribe with Assembly AI, but before I can test, I need to assign a BAA. Is that something that you can do?

嗨 Joey，我想用 AssemblyAI 做一个 ambient medical scribe（诊间环境式病历记录工具），不过在能开始测试之前，我得先签一份 BAA。这事你能办吗？

[12:51] Joey

One sec. Let me check that for you. Yes, you can execute the BAA yourself right away. Just complete our standard HIPPA business associate agreement, but note that you'll need a paid account with a card on file first. And signing the BAA automatically opts you out of model training. The relevant docs are linked in the chat.

稍等，我帮你查一下。可以的，你现在就能自己把 BAA 签掉。只要填完我们的标准 HIPAA 业务伙伴协议（Business Associate Agreement）就行，不过要注意，你得先有一个绑了卡的付费账户。另外，签署 BAA 会让你自动退出模型训练。相关文档的链接已经放在聊天里了。

[13:23]

Cool. Thanks, Joey. Well, I got to I got to get back to my demo. I'd love to chat more, but uh thanks for all the help. You are very welcome. Good luck with your demo and feel free to reach out if you need anything else later. Have a great day.

Matt： 好嘞，谢谢你，Joey。那我得回去接着做我的演示了。本来还想多聊几句，总之谢谢你帮了这么多。

Joey： 不客气。祝你演示顺利，之后还需要什么，随时来找我。祝你今天愉快。

[13:39] Matt

Isn't he so nice? [laughter] I enjoy talking to Joey. I think our customers will enjoy talking to Joey. But this is a great example of like two years ago when I first joined assembly on our support team. This is a conversation that would have to be handled by a human. This is something that if you roached out in in chat and a customer uh or a support engineer wasn't able to uh answer you immediately. They'd have to wait. They'd have to get a response from us. It was a canned response. Um it was just not a great customer experience. And now you have an FTE that you can talk to 24/7 for these basic questions. Um and you can do it through voice. So you can actually test our product at the same time. Um so there's there's kind of a meta layer there of where if you're talking to Joy about how to build a voice agent API, you're already using it. Um, and you're already seeing exactly what your product experience could look like. So, if there's anything that you should get out of this talk, it's not just that you should build your own Joey. Um, but I think that it's like if you're if you're an FTE at your company, you need to be dog fooding your own product all the time. It's not enough to just be teaching your customers how to get started with it. any new feature that you launch, any new product that you offer, you need to be teaching your customers, um, with all the knowledge that you've built from actually using and building and trying to ship the same product. Um, so I had to run through all the same walls. I had to figure out how to deal with latency. I had to figure out how to deal with turn taking or bargain or interruption handling. Um, and I think I have a lot better empathy of how to like work with customers that you want to use our voice agent API. Um, because now I've actually used it. I've used the same API. It's the exact one that my customers build on and I can give them better advice because I've

他人是不是特别好？（笑）我很喜欢跟 Joey 聊天，我想我们的客户也会喜欢跟 Joey 聊天。不过这是个很好的例子：两年前我刚加入 AssemblyAI、进支持团队的时候，这样一段对话是必须由真人来处理的。那时候你要是在聊天窗口里来问，而客户……呃，支持工程师没法马上回你，客户就只能等，等我们给个答复，而且给的还是套话。这样的客户体验实在算不上好。而现在，这些基础问题，你有一个 24/7 随时能聊的 FDE，还能用语音来聊，所以你等于同时在试用我们的产品。这里面有点 meta 的意思：你在跟 Joey 聊怎么用 Voice Agent API 搭东西的时候，你其实已经在用它了，也已经亲眼看到你自己的产品体验可以是什么样。所以，如果说这场演讲有什么是你该带走的，那不只是「你该做一个自己的 Joey」。我想说的是，如果你在公司里是 FDE，你就得一直 dogfood 自己的产品。光是教客户怎么上手是不够的。你们上线的任何新功能、推出的任何新产品，你在教客户的时候，都得带着你亲自去用、去搭、去努力把同样的产品发布出来所攒下的全部认识。所以，同样的墙我都得一堵一堵撞过去。我得搞清楚怎么处理延迟，得搞清楚怎么处理 turn-taking（轮流发言）、barge-in、打断处理。我觉得，对于那些想用我们 Voice Agent API 的客户，我现在更能感同身受，更知道该怎么跟他们配合了。因为我现在是真的用过它，用的是同一个 API，就是我的客户拿来搭东西的那一个；我能给他们更好的建议，因为我已经

[15:07] Matt

actually shipped something with it. So go build go build something. Go build whether it's a your own Joey, go build whether it's a voice agent, especially if it's a voice agent, build it on Assembly AI. Um, but if you're if you're actually working in an FTE capacity, try to build the exact product that your customers are building and do it on your own. Don't just wait for them to ask you for help. Don't just wait to remove all their blockers. You should be going out and building the exact same thing. And at the same time, you might actually be able to make your customer experience better simultaneously. So, um, if anyone has like questions on how to build Joey, if you want to check them out at the booth afterwards, our booth is just right over here. Um, if anyone has feedback on Joey since this just shipped this week, if you have a weird conversation with him, I'd love to know about it so we can keep making fixes and I can show you how we deploy that live. U, but overall, really excited to uh, be here at the conference and talk to more people about voice agents. So, thanks for uh, coming and listen to this talk.

真的用它发布过东西了。所以，去动手做点什么吧。不管是做一个你自己的 Joey，还是做一个 voice agent，都去做；尤其如果是 voice agent，那就在 AssemblyAI 上做。不过，如果你干的确实是 FDE 这份活，那就试着把客户正在做的那个产品原样做一遍，而且要自己动手做。别光等着他们来找你求助，别光等着去替他们扫清所有卡点。你应该主动出去，做一个一模一样的东西。而与此同时，你说不定还能顺带把自家的客户体验也做得更好。那么，如果有人想问 Joey 是怎么搭的，或者想待会儿到展位上看看他，我们的展位就在这边。另外，因为它这周才刚上线，如果有人对 Joey 有反馈，比如跟他聊出了什么奇怪的对话，我很想听听，这样我们可以继续修，我也可以给你看我们是怎么现场部署上线的。总之，真的很高兴能来这次大会，跟更多人聊聊 voice agent。谢谢大家来听这场演讲。

[16:00]

[applause] [music]

（掌声）（音乐）