Every mention of a title, creator, or episode — matched, deduped, scored for sentiment, and correlated with the listens and downloads you already measure.
🧪 Live demo · figures are illustrative mock data
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 is being mentioned, 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 — movies, videos, podcasts, 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.
How far into each piece they get
How long buzz survives after a peak
Kaplan–Meier cohort retention
Per-second drop-off — where they bail
Which creative wins the click
Ep-1 → ep-N audience
Depth metrics for streaming, video & podcast platforms (Netflix, Prime Video, YouTube, Spotify…) light up as you connect each source — same one-prompt path as below.
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() — 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 →