# OTT Platform — Case study > A leading OTT platform built a search engine over its own catalog. 2,500+ files indexed by faces, plot, scene, music, dialogue, and on-screen action. A Director SDK editing pipeline on top compiles, captions, reformats, and ships clips the moment a search returns a hit. --- ## Hero A leading OTT platform built a search engine over its own catalog. 2,500+ files indexed by faces, plot, scene, music, dialogue, and on-screen action. A Director SDK editing pipeline on top compiles, captions, reformats, and ships clips the moment a search returns a hit. - **2,500+** files indexed - **11** custom indexes live - **12x** editorial throughput Status: In production --- ## The story **The catalog wasn't the moat. The indexes on top of it were.** The platform had a decade of premium content. An enviable library, on paper. In practice, it was just files. The editorial team knew which scenes made fans cry, which lines became memes, which faces audiences kept coming back for — but none of that lived in a system. It sat in heads and spreadsheets. > "We didn't build a clip tool. We built a search engine that understands our show." Their leadership saw clearly that the next year wouldn't be won by who had the most content. **It would be won by who indexed it best.** AI made every catalog operationally identical; only the layer of context on top differentiated. So the editorial team got to work on what they alone could do: designing indexes that captured how *they* understood their own content. --- ## The indexes they built **Eleven indexes. Each one was the team's opinion, encoded.** | # | Index | Sample query | |---|---|---| | 01 | Identity — character & actor faces | "every close-up of the lead, season 2" | | 02 | Narrative — plot & subplot beats | "betrayal-arc scenes across the season" | | 03 | Dialogue — spoken-word + byte search | "the line about second chances" | | 04 | Location — locations & backgrounds | "all rooftop scenes after dark" | | 05 | Music — music cues & needle drops | "every needle-drop in the finale" | | 06 | Emotion — emotional beats | "the tearjerker moments, season 3" | | 07 | Action — action, violence, gunshots | "chase scenes longer than 90 seconds" | | 08 | Cast graph — all actors in a title | "scenes with leads A and B together" | | 09 | Brand — on-screen brands & props | "all branded-prop appearances" | | 10 | Scene — exact-scene retrieval | "the scene right before credits" | | 11 | Virality — shareability signal | "likeliest-to-go-viral moments" | A new index can be added in a week and retrofitted across the catalog automatically. --- ## The editing pipeline **Search returns hits. The pipeline ships the reel.** The intelligence isn't just in finding the moment. It's in everything that runs the second a search returns: compile, caption, smart-vertical, brand, publish. One Director SDK pipeline, configured per output. 1. **Compile the moment.** Search hits become a sequenced cut. Director picks the right in/out points, stitches scene-to-scene, balances pacing. 2. **Caption + subtitles.** Dynamic, word-by-word captions in the show's voice. Localised subtitle tracks for every language the platform serves. 3. **Smart vertical reframe.** Track the subject across the frame. 9:16 cuts that keep the right face in centre, never the back of someone's head. 4. **Brand & publish.** Brand kit, end-card, deep-link to free trial, platform-native export. Reels, Shorts, TikTok, all from one source pipeline. Some teams have automated this end-to-end. Faceless reel channels run 24/7 on the same indexes, with no editor in the loop. The search engine picks the moments, the pipeline ships the cuts, the channel grows in its sleep. --- ## By the numbers What an indexed catalog plus pipeline actually change: - **11** custom indexes live. Every dimension the editorial team cares about, queryable in plain language. - **2,500+** files retro-indexed. Full catalog brought online behind every new index, automatically. - **12x** editorial throughput. A season's social + recap output ships in a week, on the same pipeline. - **24/7** faceless channels run. Full automation for sub-brands. Search picks, pipeline publishes, no human in the loop. --- ## Quote > "The indexes are the product. Every clip, every recap, every recommendation we ship sits on top of opinions our team had about our own content. VideoDB just made those opinions queryable, and the pipeline does the rest." — Editorial lead, leading OTT platform (name on request) --- ## Why leaders choose VideoDB **AI is only as powerful as the indexes you give it.** Generative AI flattened the output layer. What still differentiates is the context the model gets to reason over. For a media business, that context is the indexes you've built on your own content. The richer your indexes, the better every downstream AI workflow performs. VideoDB is the framework that lets a team build that layer continuously: new index this month, retrofitted across the back catalog by Friday, powering production workflows by Monday. That's the AI advantage smart leaders pick — a compounding intelligence layer on top of content only they own. --- ## Closing Give your editorial team an AI co-worker. Not another tool to learn. CTAs: [Talk to us](/company#contact) · [Back to Media Solutions](/programmable-media)