Live on set, from the first take

AI production assistant for unscripted TV.

Faster stories, ready-made clips, and the drivers of what works. IMR follows the shoot as it happens and logs the story from transcripts and scene context, so your teams review instead of logging.

Live/Day 12/Villa LIVE 21:04:25
Feeds · 6 of 6
CAM 1
CONF
CAM 3 · SPEAKING
CAM 4
CAM 5
CAM 6 · EMPTY
Transcript · CAM 3Auto · EN
  1. 21:03:58
    Mia I'm just saying, nobody told me the rules changed.
  2. 21:04:09
    Luka They didn't change. You weren't here yesterday.
  3. 21:04:21
    Ana Luka. Can I borrow you for a second?
Story logEvery 2 to 5 min
20:56 to 21:00

Group game at the fire pit. Mia jokes about yesterday's vote. Ana goes quiet.

20:52 to 20:56

Kitchen. Luka and Jan plan a surprise for the weekend.

Today · 14 h recorded · 6 feeds Highlights marked by IMR

Illustrative interface. Cast, dialogue and data are invented.

01·The problem

Every show is logged and clipped by hand. Audience data never reaches the storyline.

01 · Live production

Logged in spreadsheets

Story editors watch every camera, transcribe what happens and type it into a log. Pre-cuts, rough cuts and final cuts are all decided by hand.

02 · Social media

Watched twice

The social team watches the whole show again to find the moments, then cuts, captions and reframes each one for vertical video.

03 · Audience

Read too late

Ratings, streaming and social reactions arrive after broadcast with few indicators. None of it shapes the episodes still being cut.

log_day12_FINAL_v3.xlsx Today, by hand
TC inTC outWhoWhat
20:52:1020:55:48Luka, Jankitchen, planning something?? check cam 2
20:56:0221:00:30allfire pit game, mia joke re vote (good?)
21:04:Ana, Lukahallway, ana takes luka aside. sth about the vote?? check cam 3 + 4

02·How it works

One loop. It speeds up production and gets smarter with every episode.

Four layers, each built on the one before. What IMR learns from each episode, and from every clip it posts, feeds the next.

01 Tested live For story editors

Index

Turns hours of footage into a searchable story log, while the shoot is still running.

  • Transcripts split into scenes and meaningful units
  • Live summaries every 2 to 5 minutes
  • Plot threads followed across episodes
  • Search by dialogue, cast or description
  • Pre-cuts built from the summaries Next
02 In use For social teams

Create

Turns raw footage into vertical short-form edits the social team approves instead of cutting.

  • The strongest scenes and sequences, found for you
  • Reframed to 9:16, following who matters
  • Highlights by cast member
  • Edits built on story patterns IMR finds
Setup → punchlineClaim → contradictionBefore → afterLine → reactionsRunning gags
03 In build For editorial leads

Analyze

Gives a full, data-based picture of every episode, scene by scene.

  • Virality and tune-out risk per scene
  • Screen time and scenes per cast member, cut rate
  • Picture checks: brightness, black and frozen frames
  • Ratings, streaming and social data side by side

Scores are not yet validated against ratings.

03 In build Analyze · posted clips

Every posted clip reports back

When a clip IMR cut goes live, its reach, likes and comments come back to the scene it was cut from. IMR already knows who is in it, which thread it belongs to and which pattern it used, so the numbers explain something.

  • Reach, likes, shares and comments for every clip
  • Comments read against the scene’s transcript and threads
  • Results compared by pattern, cast member and thread

Turning these results into the next edits is the Drive phase.

04 Next For editorial leads

Drive

Will find what drives engagement and feed it back into the next episodes and social edits.

  • Tests story factors against ratings, streaming and social data
  • Recommends storylines to push next
  • What audiences responded to shapes the next pre-cuts and clips

Not yet proven. It needs validation against real audience outcomes, and that is our next phase.

All interfaces on this page are illustrative. Cast, comments and figures are invented.

03·Why it works

IMR reads context.

Transcripts, scene context and many per-scene indicators combine into one story model. So IMR understands who is in conflict, what is building and why a moment matters.

Transcript

Ana · "We need to talk. Not here."

Scene context

Hallway, night · Ana and Luka in frame · minutes after Mia’s challenge

Per-scene indicators

Screen shareCut rateWho speaks to whomThread history
Ep 24 · 21:04:33
Who is in conflict
Ana and Luka, over the vote two episodes ago.
What is building
Their third private talk this week, and the first since Mia arrived.
Why it matters
It pays off Ana’s confessional from episode 22. Worth a pre-cut and a Claim → contradiction clip.

04·Where we are

What works today, and what comes next.

Tested live

Live story editing

Summaries every 2 to 5 minutes from the show’s live audio.

  • Used by story editors to pick storylines
  • Next: editors ask for the storylines they want
  • Next: pre-cuts built from the summaries

EvidenceLive production test

In use

Social clips

Clip candidates reframed to 9:16, following who matters in the scene.

  • Highlights by cast member, ready to post
  • Also works on footage shot only for social
  • Approve instead of cut

EvidenceIn use at a major broadcaster

Running

Full season

A full season of a reality show is being processed.

  • Several languages on one platform
  • Per-scene, per-cast and per-episode data collected
  • Ratings data ingest built

EvidenceSeason in progress

05·Contact

Let’s talk.

Pilots, investment or joining the team. One address reaches us.

hello@inmediasres.ai

IMR In Medias Res · © 2026