Podcasting used to mean one skill: talking into a microphone. Now it means running a small production studio โ recording remote guests reliably, cleaning up audio, cutting clips for social, writing show notes, and sometimes generating a synthetic voice for intros or ads. AI tools have taken over a lot of that workload, but they specialize narrowly, so most working podcasters end up stitching together three or four tools rather than finding one that does everything. Here's how the pieces actually fit together.
What to look for, by stage of the workflow
It helps to think about podcast production as a pipeline rather than a single job:
- Recording โ capturing clean, high-quality audio (and video, if you publish clips) from remote guests without relying on their internet connection quality.
- Editing and repurposing โ turning a raw long-form recording into a polished episode, and ideally into text-based edits rather than fiddly waveform editing.
- Audio cleanup โ removing background noise, mouth clicks, filler words, and uneven levels without an audio engineer.
- Clipping for social โ finding the 30-90 second moments worth cutting into vertical video for TikTok, Reels, and Shorts.
- Voice and narration โ synthetic voice for intros, ad reads, or multilingual dubs, when you don't want to (or can't) re-record.
- Transcripts and show notes โ turning the episode into searchable text and a written summary for your show notes page.
Almost no tool covers all six well; know which stage you're solving for before you evaluate a product against the wrong job.
Recording and editing: Riverside and Descript
Riverside solves the recording problem specifically: it records each participant's audio and video locally in the browser and uploads afterward, rather than streaming live like a typical video call, which means a guest's spotty Wi-Fi doesn't degrade your master recording. That's the single biggest reason podcasters switch to it โ a genuinely bad call can still produce a clean recording. It's added AI editing features over time (transcription-based editing, filler-word removal, auto-generated clips), but the recording reliability is still the core reason to use it.
Descript comes at this from the opposite direction: it's primarily an editor, built around the idea that editing audio/video should feel like editing a text document โ cut a sentence from the transcript and the audio cuts with it. That transcript-based editing model is a real productivity shift if you're used to scrubbing a waveform, and its Overdub/voice-clone feature lets you fix a flubbed word by literally typing the correction, which is remarkable when it works well and slightly uncanny when it doesn't. Descript also has its own recording feature, but it's a live, streamed recording rather than Riverside's local-capture-and-upload approach, so it's more exposed to a guest's connection quality. Many podcasters actually use both โ Riverside to record, Descript to edit โ since the two are solving different halves of the same problem. Our Riverside vs. Descript comparison walks through when it makes sense to use one instead of both.
Cleaning up the audio: Cleanvoice AI and Adobe Podcast
Even a good recording setup captures room echo, keyboard clacks, "um"s, and mouth sounds that a listener notices even if they can't name them. Cleanvoice AI is built specifically for this โ upload an episode and it strips filler words, stutters, long pauses, and mouth noise automatically, batch-processing episodes without you touching a waveform. It's a narrow tool that does one job well; it's not a recorder or a full editor, so it fits into a pipeline rather than replacing one.
Adobe Podcast (built around its Enhance Speech feature) solves a different, more specific problem: audio that sounds like it was recorded on a bad laptop mic in a room with hard walls, cleaned up to sound close to studio quality. It's genuinely effective for salvaging a guest's poor-quality recording โ the kind of thing that used to require an audio engineer with real EQ and de-reverb skills. It's less useful if your problem is filler words or pacing rather than raw audio quality; those are different jobs solved by different tools. If you're not sure which cleanup problem you actually have, Cleanvoice AI vs. Adobe Podcast breaks down which one addresses which kind of audio problem.
Turning episodes into social clips: Opus Clip, CapCut, and Kapwing
Long-form audio doesn't market itself anymore โ most discovery now happens through short vertical clips on social platforms, which means clipping has become its own skill and its own tooling category.
Opus Clip is built specifically for this: feed it a long-form video (or a video podcast recording) and it identifies likely highlight moments, reframes them to vertical, adds captions, and scores each clip on projected engagement. The moment-selection AI is genuinely useful for podcasters who don't have time to scrub through a 90-minute episode looking for the good 45 seconds โ it gets you a shortlist to review rather than starting from zero. It's a purpose-built tool, though, so if you need general video editing beyond clip extraction, you'll outgrow it fast.
CapCut is the more general-purpose alternative โ a full short-form video editor with AI-assisted features (auto-captions, background removal, some auto-reframing) bolted onto a much broader editing toolkit. It doesn't have Opus Clip's specific "find the best moment in a long recording" intelligence, but it gives you far more manual control once you've picked your clip, plus a much larger effects and template library aimed at the platforms you're publishing to. Podcasters who want AI to do the moment-finding lean Opus Clip; podcasters who want to do their own editing but want the caption/reframe grunt work automated lean CapCut. See Opus Clip vs. CapCut for the fuller feature comparison.
Kapwing sits in a similar general-editor space to CapCut but is more browser-based and collaborative, which matters if you've got a team member handling clips rather than doing it solo โ shared projects and comments work more like a Google Docs workflow than a desktop app. It also has solid auto-transcription and subtitle tooling built in, useful since burned-in captions are close to mandatory for social clip performance now. InVideo AI leans further into full generation โ describe what you want and it will assemble a video from stock and AI-generated footage โ which is more useful for making promotional or explainer content around your podcast than for clipping the podcast itself. Our Kapwing vs. InVideo AI comparison covers that distinction: Kapwing for editing and clipping existing footage, InVideo AI for generating new promotional video from a script or prompt.
Synthetic voice: ElevenLabs and Murf AI
Not every podcaster needs a synthetic voice tool, but for the ones who do โ multilingual dubbing, consistent ad-read voiceovers, fixing a flubbed line without re-recording an entire segment โ ElevenLabs is the strongest option for realism. Its voice cloning and text-to-speech output is close enough to natural speech that listeners often can't tell, and it supports enough languages to make dubbing an episode into another market realistic rather than theoretical. The tradeoff is that quality control and usage policies matter a lot here โ cloning a real person's voice, even your own, raises consent and disclosure questions worth thinking through before you ship an episode using it.
Murf AI trades some of that top-end realism for a more studio-oriented workflow โ script-to-voiceover with more granular control over pacing, emphasis, and pronunciation, aimed more at corporate/explainer voiceover work than podcast-specific use. For podcasters, it's a reasonable choice for consistent, polished intro/outro voiceovers or ad reads where you want full script control rather than voice-cloning realism.
Transcripts and show notes: Otter AI and Notion AI
Every episode should produce a transcript โ for accessibility, for SEO, and because guests and clip editors both need to search "where did we talk about X" instead of scrubbing audio. Otter AI is built for exactly that: live or post-recording transcription with speaker labels and decent accuracy on conversational audio, plus the ability to pull out summaries and action items, which is more useful for a recurring interview show than you'd expect โ it turns an hour of talking into something skimmable.
Once you have a transcript, turning it into publishable show notes is still a writing task, and that's where a general writing assistant like Notion AI earns its place in the pipeline โ summarizing a raw transcript into structured show notes, pulling out timestamps and quotable lines, and keeping episode planning docs in the same workspace you're already using for the rest of the show. It's not podcast-specific, which is exactly the point: if your show notes, guest outreach, and episode planning already live in Notion, adding the AI layer there beats bouncing to a fifth specialized tool.
Our take
If you're just starting out, prioritize recording reliability first: Riverside will save you more headaches than any editing tool if you record remote guests regularly. Add Descript once you're editing enough that transcript-based cutting saves real time, and use Cleanvoice AI or Adobe Podcast โ not both, usually โ depending on whether your problem is filler words or raw audio quality. For clipping, Opus Clip is the fastest path to a shortlist of good moments; CapCut or Kapwing make more sense if you or a teammate want to do more editing by hand. Only reach for ElevenLabs or Murf AI if you have a specific voiceover need โ most shows don't need synthetic voice at all, and it's easy to over-invest in a feature nobody asked for. And don't skip transcription: Otter AI plus a writing tool like Notion AI for show notes is a small time investment that pays off in discoverability every single episode.
The pattern across all of this: no single tool wants to be your entire podcast stack, and the ones that claim to usually do two or three jobs adequately rather than one job well. Build the pipeline stage by stage, and swap a piece out only when it's actually the bottleneck.
