Lakhs of hours of content with thin metadata. Search is keyword-only when it should be semantic and scene-aware.
Indian OTTs see new users with sparse signals. Default collaborative-filtering models stall on cold-start cohorts.
Dubbing at scale used to be a 6-month process per language. AI-assisted dubbing collapses it — without losing performance quality.
Content rights are tangled across territories, windows, and platforms. Spreadsheets-as-truth doesn't scale.
Scene-aware tags, named entities (actors, locations, IP), shot-level metadata, content-safety flags. Editorial review built in.
Hybrid models — collaborative + content-based + cohort-aware — tuned for cold-start and multilingual catalogues.
Scripted-translation + voice-cloning across 12 Indian languages, with QC checkpoints and human voice-direction on the loop.
Contract intake → rights extraction → window/territory model → conflict detection. Audit-ready and finance-friendly.
Scene-aware tagging across a 100K-asset catalogue. Editorial review on flagged scenes. Search recall up 3.1×.
Multilingual dubbing across 5 Indian languages, with human voice-direction on the loop. 8× faster turnaround per asset, no quality regression.
Good enough that a human voice-director is editing rather than recording from scratch. Per-language voice models retrained on target talent. Final cut is always human-approved.
Yes — we've integrated with Brightcove, JW Player, custom MediaCentral installs, and India-built OTT CMSes. Two-way sync via API or message queue.
Augment, almost always. We sit alongside your existing recsys, score in parallel, and surface lift on a designated test cohort. Promote when the lift is real.