TasksBatchTools
Pricing
English
☰

Turn your lecture into
a blog post people will actually finish.

audien·to is an AI audio tool that turns a lecture or talk recording into a publishable blog post — hook, argument, takeaway — preserving the speaker's voice. Unlike a raw transcript that reads like a transcript, it restructures the spoken flow into something readable. Free tier, 67 languages, no signup, audio auto-deleted in 72 hours.

Drop the talk in. We'll find the hook, restructure the argument, and land a takeaway — keeping the parts that made the talk worth giving in the first place. You publish; we did the typing.

● Record
Drop audio here
or click to choose a file · up to 2h · auto-deleted in 72h
LANGUAGE
required · 67 supported
What you'll get
  • Title and hook
  • Argument body
  • Takeaway
  • Source timestamps
Speakers auto-tagged. Tweak any section after upload from the Options panel — no setup needed up front.
The guide

Why a talk and a blog post need different shapes

A talk is paced for a room: setup, callback, breath, joke. A blog post is paced for a phone screen: hook in the first line, argument in three sections, takeaway you remember on the train home.

The translation from one to the other isn't a transcript with nicer formatting. It's a re-architecture: same ideas, same voice, different shape. We do the structural work; the prose stays yours.

What a good talk-to-post conversion contains

  • A hook on screen one — the first 3 lines decide whether the reader scrolls. They earn the rest.
  • Argument, not retelling — the post advances a claim. It doesn't summarise what was said in the order it was said.
  • Voice intact — if the speaker said “gnarly,” the post says “gnarly.” Smoothing kills the read.
  • Takeaway you remember — one line a reader can quote in a Slack channel. Without one, the post is forgettable.
  • Source-checkable — every factual claim maps back to a timestamp, so nothing was fabricated.

How audien·to drafts yours

What lands in the doc

  • Title and hookheadline + 2–3 sentence opening that earns the rest of the read.
  • Argument body3–5 sections, each advancing one claim. Subheads are claims themselves, not labels.
  • Takeawayone line a reader can quote, plus a 2-sentence wrap that names the next step.
  • Source timestampsevery claim mapped back to its timestamp in the talk so you can verify before publishing.
  • Voice preservedthe speaker's idiom, jokes, and rhythm carry through. Corporate-flatten is the failure mode.

Writing from talk audio

  • Hook over recapopen with the line that hits, not the topic. The reader can wait 200 words for the agenda.
  • Subheads are claims“Why room treatment beats gear” > “The room argument.” The reader knows the takeaway from the title.
  • One thesis, not fivetalks meander; blog posts can't. Pick the strongest claim and let the others support it.
  • Voice over polishedit the structure, not the diction. The post should still sound like the speaker.
  • End with a do-somethinga takeaway that names a next action — try this, ask this, read this.

Knobs in the Options panel

  • LengthShort (~600 words) · Standard (~1200) · Long (~2500+).
  • ToneConversational · Punchy · Formal — match the audience, not always the speaker.
  • Hook styleProvocative claim · In-the-room scene · Stakes-first · Question.
  • StructureArgumentative (3-act) · Listicle · Narrative · How-to.
  • Output language23 supported. Useful when the talk is in one language and the post is in another.
Why this works

Why modern AI hears what older tools missed

When you upload audio, two AIs go to work. The first one listens. It learned from millions of hours of real speech — accented, overlapping, full of “ums” and brand names that didn’t exist five years ago — so it can hear “Klaviyo,” “Substack,” or “the GPT pipeline” without flinching. The words older tools used to silently mangle come back right.

It hears in context. Instead of guessing one sound at a time, it takes in the whole sentence and uses everything around a tricky word to figure out what was actually said. That’s how a brand-new product name still lands correctly: the words around it tell the AI what kind of sentence it’s in.

And it cleans as it goes. Disfluency — “uh, like, I think… yeah” — doesn’t drop the rest of the sentence on the floor. Punctuation and capitalization come built in, so what you read is prose, not a wall of lowercase. By the time it hands off, the transcript already looks like what a careful typist would have given you.

What happens once we have your words

A raw transcript is the floor, not the ceiling. The second AI reads the whole document the way a careful editor would. It groups related discussion into chapters even when nobody says “moving on.” It surfaces the quote you’d actually screenshot — not the longest sentence on the page. It separates a decision from a tangent, an action item from a passing wish.

That’s the jump older tools couldn’t make: they gave you words, we give you shape. A meeting becomes minutes with owners, dates, and resolved questions. A podcast becomes show notes whose chapters track the real narrative, not the nearest five-minute mark. A voice memo becomes a send-ready email in your voice — not a list of fragments to stitch back together.

Each tool on this page is one of those pairings — the same listening AI up front, the same writing AI behind it, shaped for one specific output. You don’t pick. You don’t tweak. A thirty-second upload comes back as the thing you actually wanted, ready to use.