Know what's landing, where you're losing people, and what to do next — across every mention of your title, creator, or episode, tied to the listens and downloads you already measure.
Track a title from first trailer to long-tail streaming buzz. Reviews, threads, and press in one timeline.
Follow a channel, drop, or creator across platforms. Comments, reactions, and cross-posts deduped into real reach.
Scan your own transcripts and the wider conversation. Who gets mentioned, and where it correlates with listens.
It's not enough to know people showed up. We measure where attention goes across all media, and how deep into your content it actually runs.
Where audiences choose to spend their time and what they talk about, across movies, videos, podcasts, and every platform. The conversation happening about a topic, everywhere.
How far into your content people actually get before they drop, and whether they come back. The other half of attention: not just that they showed up, but how much they stayed for.
Every number maps to a move you can make. Here's what you'll actually do with it.
See how far in people get — and the trend
Know how long attention holds, so you time the next move
See when listeners churn — and what re-hooks them
Pinpoint the drop in a video, second by second
Find the thumbnail and title more people click
See where a series sheds viewers between episodes
Per-scene engagement (start / pause / continue / drop-off) isn't something every platform will give you. We're straight about what you can actually get:
Real start / pause / continue / drop-off, per session — only where you control the player.
A retention curve — where the audience leaves, with rewind spikes. No raw events.
The platform keeps it. You get a total-hours number at best — never the curve. (See: how Netflix treats directors.)
Streamers like Netflix hand creators a total-hours number, twice a year — never the curve. The only way to see the curve for those titles is on content you control. So we make the owned-player path first-class.
View → click → purchase, with spend on one side and revenue on the other. The same place you watch attention is where you watch what it earns.
Ad spend mapped to the attention and sales it actually drove
Spot the campaign burning budget with no lift
Tie a spike in mentions back to the ad that caused it
Follow a view → click → purchase, end to end
Where people fall out between watching and buying
Revenue ÷ spend, attributed to the content that earned it
We start from the problem you're actually stuck on. The same mentions answer very different questions depending on whose chair you're in.
“Downloads tick up and you can’t tell why: which clip or post drove it, whether anyone finished the episode, or what to tell a sponsor.”
“A video pops off or dies and you can’t tell why: which thumbnail, hook, or platform moved it, or what your reach is really worth.”
“The discourse is loud, months late, and blurs the writing with the cast. You never hear whether the arc worked but the dialogue didn’t.”
“Critics and audience split by 40 points and you can’t tell what they rejected, or whether the film is gaining stature or fading.”
“Reviews grade the project, not you, and a pile-on spins up on Reddit and X for days before anyone tells you.”
Studios, labels, PR & marketing teams use the same signals at portfolio scale.
Bring your own source →6 live today · 16 more a single prompt away.
Scans episodes already in your system
Bring any export, mapped on upload
Threads, comments, subreddits
Search + comments
RSS / press coverage
Inbound newsletter mentions
Behind a feature flag (API cost)
Charts, reviews, ratings
Podcast + video reach
Sounds, duets, hashtags
Reviews & lists
Critic + audience scores
Stream chat & clips
Comments & embeds
Community servers
Posts, reels, tags
Top 10 charts & title buzz
Catalog, ratings & reviews
HBO/Max title reception
Franchise & release buzz
Series chatter & drops
Originals reception
Every connector follows the same contract: source_type, available(), fetchDocuments(), all feeding one normalized pipeline (match, dedup, sentiment, signals). So adding a new platform isn't a project, it's a prompt. The agent scaffolds the connector, wires the auth, and verifies it end to end against your pipeline.
// Name a source and the connector agent scaffolds a typed,
// pipeline-ready connector stub here.A working dashboard on real multi-source data: mentions over time, sentiment, spikes, and the mentions↔listens correlation.
Open the live dashboard →