Radar review (2026): Particle's podcast search engine for AI agents, scored honestly on coverage, quotes, alerts, pricing, and who it actually fits best.
Radar is a genuinely strong podcast search and intelligence tool: it transcribes and indexes 130,000+ shows and exposes timestamped quotes, entity data, and alerts through an API and MCP endpoint for AI agents. Scored as a research and monitoring platform, it is very good. Its limit for creators is scope — it finds and structures spoken audio but generates and publishes nothing, so it is one end of a workflow, not the workflow.
This review scores Radar, the podcast search engine Particle announced on August 26, 2026, for what it actually is: a research and intelligence layer over spoken audio. It transcribes podcasts at scale — Particle says more than 130,000 shows, adding roughly 20,000 episodes a day — and turns them into structured, searchable data with speaker labels, entity metadata, timestamped quotes and clips, and alerting, all reachable via an API and an MCP endpoint so AI agents can query audio they were previously blind to.
I grade it inside its real category — podcast search and monitoring — not as a content tool, because it isn't one. The scores below weight what decides whether Radar earns its keep: how much it covers, how good the search and entity intelligence are, how useful the quotes/clips and alerts feel, how well the API/agent access works, and what it costs. Where Radar is strong, it scores well; where a creator will hit its ceiling, the scores say so.
Two things anchor the verdict. First, this is an impressive piece of infrastructure — the largest transcribed podcast service Particle knows of, with real buyers (hedge funds are its highest-volume API customers). Second, the honest caveat for a creator: it makes audio findable and quotable, and then stops. It writes no posts, cuts no finished video, and publishes to no platform. That's not a flaw; it's the boundary of the product.
Radar is a podcast search engine and intelligence platform from Particle, the AI news startup founded by former Twitter engineers and led by co-founder and CEO Sara Beykpour. It transcribes podcast audio and layers on speaker labels and metadata that identify entities — people, companies, brands, topics — then extracts key quotes, highlights, and self-contained clips with timestamps. You can search across the whole index, track when an entity is mentioned, and get customizable alerts by email, Slack, or webhook. It also runs a dedicated podcast-ads search engine and surfaces sponsorship data, listener ratings, and brand-suitability signals. The point of it is machine access to spoken audio. Particle's pitch is that AI agents and search tools mostly can't see audio unless it's transcribed, so Radar exposes 130,000+ shows (covering the Apple Top 200 across 135 verticals) as structured data through an API and an MCP endpoint. Its highest-volume customers are hedge funds mining conversations for signals, alongside AI search platforms, data resellers, journalists, and researchers, and Particle has signaled plans to expand into YouTube videos and news clips.
Radar fits anyone whose job is finding and structuring what was said on podcasts: analysts and funds mining audio for signals, journalists and researchers hunting quotes, and developers piping podcast data into an AI agent through the API or MCP endpoint. For creators, it fits a narrower slot — a top-of-funnel research and monitoring tool. Set an alert on your niche, a competitor, or a topic you own, and Radar tells you what's being said across shows you'd never have time to listen through, which is a real edge for spotting trends and quotable moments. Where it fits poorly is anyone expecting it to make content: there's no writing, no video editing, no carousel or image generation, no brand-voice layer, and no scheduler or publishing. If your constraint is producing and distributing content rather than discovering what to make it about, Radar is one input, not the pipeline.
| Dimension | Score | Why |
|---|---|---|
| Podcast coverage & scale | 4.5 / 5 | More than 130,000 shows transcribed with ~20,000 episodes added daily, covering the Apple Top 200 across 135 verticals — Particle calls it the largest transcribed podcast service. |
| Search & entity intelligence | 4.3 / 5 | Speaker labels plus entity metadata for people, companies, brands, and topics make it a real search engine over audio, not just a transcript dump. |
| Quotes & clip extraction | 4.2 / 5 | Pulls key quotes, highlights, and self-contained clips with timestamps — the feature creators would use most. |
| Alerts & monitoring | 4.2 / 5 | Customizable entity-mention alerts by email, Slack, or webhook, with filtering by guest, topic, and podcast tier. |
| API & agent access (MCP) | 4.3 / 5 | An API and MCP endpoint expose the index to AI agents; hedge funds integrating directly are the highest-volume validation of it. |
| Pricing & value | 3.6 / 5 | Consumer at $29/mo per seat and Business at $399/mo (20 seats) are fair for a data tool; API is custom, and a creator pays for research, not output. |
| Transparency of claims | 3.6 / 5 | Scale and coverage figures are vendor-reported at launch; treat the largest-service and daily-add numbers as directional until independently confirmed. |
| Content-workflow scope | 1.5 / 5 | It generates no posts, no video, no images, and publishes nowhere — by design a research layer, not a content engine. |
Radar's launch pricing is straightforward: a Consumer plan at $29/month per seat, a Business plan at $399/month that includes 20 seats, and custom API pricing based on usage. For a data and intelligence product covering 130,000+ shows with live alerting and agent access, those are reasonable numbers — the Business tier in particular is priced for teams, and hedge funds paying for direct API integration tells you the underlying data is worth real money.
For a creator, the value question is different. You'd be paying for research and monitoring, not for output. $29/month to watch your niche and pull quotable moments can pay for itself if it consistently feeds your content calendar — but it's a top-of-funnel spend, and you still need a production tool downstream. That makes Radar and a content engine like Kompozy (from $99/month) complementary line items, not competing ones: one finds the moment, the other turns it into published posts.
So the honest pricing read: Radar is fairly priced for what it is. Judge it against other podcast-intelligence and monitoring tools, not against content platforms — and budget for the fact that whatever Radar surfaces still has to be made and shipped somewhere else.
| Use case | Fit | Why |
|---|---|---|
| Monitoring mentions of your brand or a competitor on podcasts | Strong | Entity alerts by email, Slack, or webhook are exactly this job. |
| Spotting trending topics and quotes across your niche | Strong | Search across 130,000+ shows surfaces what's being said before it hits the feed. |
| Feeding structured podcast data to an AI agent | Strong | The API and MCP endpoint are purpose-built for agent access. |
| Researching sponsorship and podcast-ad opportunities | OK | The podcast-ads search engine and sponsorship data help, though it's aimed more at analysts than creators. |
| Turning a found quote into a captioned Short or carousel | Weak | Radar extracts the clip and quote but generates no finished video, image, or post. |
| Repurposing your own podcast episode into multi-format content | Weak | It indexes audio for search; it does not clip, caption, or publish your episodes for you. |
| Enforcing one brand voice across outputs | Weak | There is no Persona Brief or brand-governance layer — it's a research tool. |
| Scheduling and publishing across platforms | Weak | No scheduler and no publishing of any kind. |
To be fair, Kompozy is not a replacement for Radar, and this review won't pretend it is. If your need is to search across other people's podcasts, monitor mentions, or hand structured audio to an AI agent, Radar is the better tool — Kompozy has no podcast index and doesn't try to. They sit at opposite ends of a workflow.
Where Kompozy matters is everything after the find. Radar tells you what was said and where; it stops at a quote, a clip, or a trend signal. Kompozy is a content generation and multi-platform publishing engine: bring in that quote or your own episode and it becomes a captioned Persona Short, a brand-exact carousel via HyperFrames, quote graphics, native text posts, a blog, and a newsletter, all held to one Persona Brief, then scheduled and published across the eight social platforms plus blog and email. The honest framing is complementary — Radar as the discovery layer, Kompozy as the production-and-distribution layer that turns what Radar surfaces into content that actually ships.
Radar is a podcast search engine and intelligence platform announced August 26, 2026. It transcribes and indexes more than 130,000 podcasts and exposes them as structured, searchable data through an API and an MCP endpoint, with speaker labels, entity metadata, and timestamped quotes and clips, so people and AI agents can find what was said on air.
As a research and monitoring tool, it can be — setting alerts and searching 130,000+ shows helps you spot trends and quotable moments in your niche. But it produces no content and publishes nothing, so a creator needs a production tool downstream. Judge it as a top-of-funnel input, not a content platform.
At launch Particle listed a Consumer plan at $29/month per seat and a Business plan at $399/month including 20 seats, with API pricing quoted custom based on usage. Confirm current pricing on Particle's own pages before relying on it.
No. Radar finds, transcribes, and structures podcast audio — it writes no posts, edits no video, generates no images, and publishes to no platform. To turn what it surfaces into finished, multi-platform content, pair it with a generation-and-publishing engine like Kompozy.
Particle has said hedge funds are its highest-volume API customers, mining spoken conversations for signals their agents can't otherwise see, alongside AI search platforms, data resellers, journalists, and researchers. Creators are a secondary but real audience for trend-spotting and monitoring.
They do different jobs and the best answer is often both. Radar searches and monitors other people's podcasts and feeds audio data to AI agents; Kompozy turns a source — including a quote Radar surfaced or your own episode — into finished posts in a governed brand voice and publishes them across platforms. For discovery, Radar; for producing and shipping content, Kompozy.
At launch it focuses on podcasts, covering the Apple Top 200 across 135 verticals. Particle has signaled plans to expand beyond podcasts into YouTube videos and news clips, but treat that as a roadmap intention rather than a shipped feature.
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