Microdrama and vertical video analytics

Know which scene moved your audience.

Mondo's AI watches every episode, reads every comment, and lines both up against the numbers, so you can see what worked and why.

  • Dialogue
  • Visuals
  • Comments
  • Analytics
See how it works

Live with studios in the US and Argentina

Dialogue 0:42"You were never supposed to find out."
Top comment theme"part 10 PLEASE"
vs show average+2.4x comments per view

Illustrative example

What studios get today

Your dashboard sees a number. Your audience saw a story.

What the platform dashboard shows
E1
E2
E3
E4
E5
E6
E7
E8
E9
E10

Ten episodes, ten view counts. No reasons.

What Mondo shows
Premiere: algorithm push
E1
E2
E3
E4
The reveal
E5
E6
E7
E8
E9
The payoff
E10

Same chart, with the scene that caused each spike attached.

Four signals in, one read out

We read the show the way your writers' room does, at the scale of every episode.

  • Dialogue

    Every line transcribed and time-stamped

  • Visuals

    Frames scanned for scenes, characters, settings

  • Comments

    Every reply read and sorted into themes

  • Analytics

    Views, shares, saves, comments per platform

Mondo AI layer

Episode intelligence

  1. 1Transcribe the dialogue
  2. 2Tag scenes, characters, hooks, reveals
  3. 3Classify comments by theme and reaction
  4. 4Join every signal to its episode and arc

Built for episodic storytelling, not brand campaigns

  • Episode scorecard

    How each episode did against the show's own average

  • Moment map

    Spikes and drops tied to the scene that caused them

  • Audience themes

    What viewers argued about, asked for, and shared

  • Arc read

    How Act 2 is pacing against Act 1, and when it's too early to call

We read the comment section so you don't have to

Thousands of replies, sorted into what your audience actually thinks.

  • @k.marino way she knew the whole time
  • @dreeewpart 10 PLEASE i can't wait
  • @sol.vhe's lying, look at his face at 0:41
  • @nina_bsending this to my sister rn
  • @j0rdywatched it 3 times for the ending
  • + every other comment on every episode

Share of comments by theme

Theories34%
Need next episode26%
Character reads20%
Shares12%
Rewatch8%

Illustrative comments and themes

What it tells you

The cliffhanger landed. "Need the next episode" spiked in the replies right after Ep 9, and it outranked reactions to the scene itself.

Shares
Gossip. Recruits new viewers.
Comments
Debate. Proves they care.

We score both for every episode.

Case study: Telefe, Triángulo Amoroso, Argentina

A full microdrama season, read episode by episode.

125.9M

campaign impressions across promo and episodes

episodes and vlogs
24
platforms
5
new report since July 2026
Every arc

Public platform data. First-party account data is reported separately.

What we found

The finale got the comments. The premiere got the views.

Season 2 should copy what the finale did, not the opener.

A mid-arc spike means a cliffhanger worked.

We point to the exact episode that pulled people back.

Early numbers aren't a verdict.

New arcs are flagged as pacing data, so no one kills a show on week one.

Also runningA major US studio's TikTok microdrama slate. Case study available with partner approval.

Start with one show

Pilot it, run it every arc, then cover the slate.

Pilot

$4,500one report

  • One arc or one season, fully read
  • Dialogue, visuals, comments and analytics
  • 30-minute walkthrough call

See the output on a show you know.

Series, most popular

$2,500per month

  • New report every arc drop
  • Moment map and audience themes updated as the show grows
  • Monthly call with producers or execs

For a show that's actively releasing.

Slate

Customwhole studio

  • Every show in production
  • Comparisons across your shows
  • MondoTrends access for your team

For studios with more than one show running.

Every tier includesDialogue transcriptionVisual scene taggingComment themesPlatform analytics

Send us one show. We'll show you what your audience saw.

A 30-minute call is enough to walk through what the first report would look like for your series.