Every streaming platform has the same problem. A viewer browses for four minutes, skips three titles, and leaves. The behavioral signal that could have saved the session sits in an event stream your recommendation engine doesn't read until a batch job runs overnight. By then, the viewer opened a competitor.
NEXTGRES acts on viewing behavior the moment it happens — mid-session, mid-browse, mid-skip. Not after a nightly retrain. Not after a pipeline rebuild. Right now.
Why Streaming & Media Teams Choose NEXTGRES
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Increase watch time by acting in-session. A skip, a pause, a browse without selection — NEXTGRES detects the signal and adjusts recommendations before the viewer disengages. What they did at 9:04 PM shapes what they see at 9:05 PM.
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Reduce churn with personalization that keeps up. Viewer preferences shift constantly — new seasons, mood changes, shared accounts. NEXTGRES reflects those shifts in real time because the models and the data live in the same system. No stale recommendations based on last month's behavior.
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No new pipelines. No ML team. No engineering dependency. NEXTGRES connects read-only to your existing content catalog, playback events, and user profiles. Your product team controls personalization directly — define audiences, preview experiences, and understand every recommendation without filing a ticket.
How It Works
1. Connect to Your Existing Stack
NEXTGRES connects read-only to your content metadata, playback events, user profiles, and engagement signals — whether you run on a custom stack, a cloud video platform, or a legacy CMS. No schema migration. No warehouse. No rebuild.
2. Detect Viewing Signals in Real Time
Plays, skips, pauses, completion rates, browse-without-selection, binge patterns, time-of-day preferences — NEXTGRES detects these signals mid-session, not after a batch process runs overnight. The model updates as the viewer watches.
3. Personalize Every Touchpoint Instantly
Homepage rails, recommendation carousels, continue-watching logic, search rankings, push notifications — all driven by what the viewer just did, not what they did last week. Your product team previews every experience before it ships and can explain why any recommendation was made.
Your Competitors Retrain Nightly. You'll Personalize Mid-Session.
Traditional recommendation engines batch-process viewing data overnight and retrain weekly. By the time the model updates, viewer preferences have already shifted. NEXTGRES connects to your existing data and delivers personalized recommendations in the same session. Your product team controls it. No analyst in the loop.