Radar 让播客可搜索，也可供 AI Agent 使用

Particle 是一家由前 Twitter 工程师创办的AI 新闻阅读器创业公司。该公司正将重心转向一个潜在利润更高的方向：为隐藏在播客中的口语对话建立索引，并让这些内容可以被发现。周三，该公司推出了播客搜索引擎 Radar。它不仅能转录播客音频，还能理解其含义，从而提取关键引语和内容亮点。

Particle 联合创始人兼 CEO Sara Beykpour 表示，这一解决方案具备商业潜力，已经吸引了对冲基金的兴趣，因为它们希望获取其 Agent 无法看见的数据。

“对冲基金是直接集成 API 的最高用量客户，”Beykpour 向 TechCrunch 表示。尽管记者和研究人员同样可以使用这些工具，其他付费能力较强的客户还包括 AI 搜索平台和数据转售商。（例如，面向 AI Agent 的搜索 API 提供商 Exa 就是 Radar 的合作伙伴之一。）

图片来源： Particle/Radar

这一想法源于 Particle 新闻阅读应用中最受欢迎的功能之一。该应用曾通过其 API获取有趣的播客片段，并将其与应用信息流中的相关新闻报道一同展示。

Particle 团队意识到了该产品的价值，但也发现它在某种程度上被困在新闻阅读器内部。随着围绕 AI Agent 的趋势开始升温，公司决定转型，专注于为其播客情报产品构建 API。

播客搜索引擎

图片来源： Particle/Radar

“我们的愿景，实际上是在这个 API 中汇集所有新媒体情报和全部音频情报。这个领域之所以有趣，原因之一在于，大多数 API Agent 和服务都会抓取 Web，它们专注于文本。我们提供的是音频这一层，”Beykpour 表示。“Agent 通常对音频视而不见；除非某些内容或某个人已经将音频转录出来，否则它们无法看见。”

借助 Radar，该公司已转录超过 13 万档播客，使其成为目前规模最大的播客转录服务。这包括 Apple 在 135 个垂直分类中的全部 Top 200 播客，Radar 的索引每天还会新增 2 万集节目。

播客转录文本包含说话人标签和丰富的元数据，因为 Radar 能够理解对话中讨论的实体，包括人物、公司、品牌、产品和话题。

图片来源： Particle/Radar

它还能追踪这些实体在各档播客中的提及情况，并在相关内容出现时即时发送提醒，或以每日、每周摘要的形式发送。

这些提醒可通过电子邮件、Slack 或 webhook 推送，并可通过筛选条件进行定制。例如，用户可以将 Radar 配置为仅在特定嘉宾出现并讨论某个特定话题时发送提醒。搜索也可以通过其他方式进一步缩小范围，例如仅限头部播客。

Radar 提醒

图片来源： Particle/Radar

Radar 可以提取相关且可独立理解的片段，并附带时间戳，让用户既能收听，也能阅读相关评论。

“我们已经预先挑选了值得关注的片段，因此，如果你无法听完整档播客，也不想阅读摘要，这是快速了解这档播客内容的最佳方式，”Beykpour 指出。

Radar 还可以追踪播客中提到的话题、被提及的人或事物及其出现时间、听众评分与评论、单集广告等信息。它甚至提供专门的播客广告搜索引擎，可以找出某家公司在哪些节目中投放广告，并追踪其随时间变化的趋势。除其他提供政治倾向分析、榜单排名数据、受众规模预估、赞助数据和品牌适配性分析的工具外，这项功能也具备额外的变现潜力。

播客情报

图片来源： Particle/Radar

尽管上述所有功能都可通过 Radar 的 Web 界面使用，但其真正的产品是 API 和 MCP，它们允许 AI Agent 和其他企业以编程方式调用同样的情报能力。

Radar 的定价为每席位每月 29 美元；企业版套餐为每月 399 美元，包含 20 个席位。API 用户则根据自身需求采用定制定价。

未来，Radar 计划将服务范围从播客扩展到其他形式的音频内容，例如 YouTube 视频和新闻片段。

Particle, the AI newsreader startup founded by former Twitter engineers, is shifting its focus to a potentially more lucrative idea: indexing the spoken conversations buried in podcasts and making them discoverable. On Wednesday, the company introduced Radar, a podcast search engine that not only transcribes podcast audio but also understands what it means, enabling it to pull out key quotes and highlights.

The solution has business potential, as it’s already attracted interest from hedge funds looking for data that their agents can’t see, explains Particle co-founder and CEO Sara Beykpour.

“Hedge funds have been the highest-volume customers that are directly integrating with the API,” Beykpour told TechCrunch. While journalists and researchers could also make use of the tools, other top-paying customers have included AI search platforms and data resellers. (The search API provider for AI agents, Exa, for instance, is among Radar’s partners.)

Image Credits: Particle/Radar

The idea itself stemmed from one of the Particle news-reading app’s most beloved features. The app had used its API to source interesting podcast clips that it then included alongside related news stories in the app’s feed.

Particle’s team realized the product’s value, but also that it was somewhat trapped in the news reader. As the movement around AI agents began to gain steam, the company decided to pivot and focus on building an API for its podcast intelligence product.

Podcast Search EngineImage Credits: Particle/Radar

“Our vision is really to have all new media intelligence and all audio intelligence in that API. One of the reasons why it’s an interesting space is that most API agents and services crawl the web and they’re focused on text. We are providing that layer with audio,” Beykpour said. “Agents are generally blind to audio; they can’t see it unless something or someone has transcribed it.”

With Radar, the company transcribes more than 130,000 podcasts, making it the largest transcribed podcast service in existence. This includes all the Apple Top 200 podcasts across its 135 verticals, with 20,000 episodes added to Radar’s index daily.

The podcast transcriptions include speaker labels and rich metadata, as Radar understands the entities — people, companies, brands, products, and topics — being discussed.

Image Credits: Particle/Radar

It’s also able to track mentions of these entities across podcasts and send alerts whenever they come up, either when the mention occurs or as a daily or weekly digest.

The alerts, which can be delivered via email, Slack, or webhook, can be customized with filters. These let users configure Radar to only send alerts when certain guests appear and discuss a particular topic, for instance. The search can also be narrowed in other ways, such as limiting it to top podcasts only.

Radar AlertsImage Credits: Particle/Radar

Radar can extract relevant self-contained clips, with timestamps, allowing users to both listen to and read the comments made.

“We’ve pre-chosen notable clips, so if you can’t listen to the whole podcast and you don’t want to read a summary, this is the best way to just get an idea of what’s happening in that podcast,” Beykpour noted.

Radar can also track the topics mentioned in the podcast, who or what was mentioned and when, listener ratings and reviews, the episode’s ads, and more. There’s even a dedicated podcast ads search engine that can find every episode where a given company advertises and track how it trends over time. This feature has additional monetization potential, alongside other tools offering political bias analysis, chart rankings data, audience size estimates, sponsorship data, and brand suitability.

Podcast Intelligence Image Credits: Particle/Radar

While all of this is available through Radar’s web interface, its real product is the API and MCP, which allows AI agents and other businesses to tap into this same intelligence programmatically.

Radar is priced at $29 a month per seat, with a $399-per-month plan for businesses that includes 20 seats. API users have custom pricing, based on their needs.

In the future, Radar plans to expand the service beyond podcasts to support other forms of audio, such as YouTube videos and news clips.