One search bar, sixteen channels: how MediaFind finds anything
Type “a rocket on the launchpad,” “every clip that names Acme,” or “the part about budget cuts” into the same box and the right moments come back. They come back because behind that one bar are sixteen independent retrievers — each good at a different kind of question — blended into a single ranked list. Here's every one of them.
Most search products have one notion of a match: the document contains your words, or its embedding is near your query. MediaFind has sixteen, because a media library isn't one kind of haystack. The thing you're looking for might be a sentence somebody said, a logo that flashed on screen for two frames, a face, a kitchen, the word “QUARTERLY” on a slide, or a dog barking. No single retriever is good at all of those — so MediaFind doesn't use one.
Instead, search is a channel registry. Each channel is a self-contained retriever that takes your query, searches its own slice of the index, and returns a ranked list of moments. A dispatcher decides which channels to run for a given query, runs them in parallel, and fuses their lists into one. Adding a new way to find things — entities, scenes, logos — means adding a channel, not rewriting search.
The nine default channels
Type a plain query and hit search, and nine channels run: the ones that read what was said and written, plus the ones that look for who's in the shot, what's happening and where it is — so a plain query reaches all of that without you opting in to anything. (On the free tier it's eight: People is a Pro channel and sits out.)
1 · Transcript — what was said
The flagship. A bi-encoder embeds your query and retrieves the nearest spoken segments by meaning; a cross-encoder then reranks the top candidates for precision. “The part about budget cuts” finds a clip that says “we're trimming spend” — no shared words required. If the embedding search comes back with nothing above its relevance floor — stale stored embeddings are one way that happens — it falls back to literal keyword matching, so words that were actually said can still be found. You can also scope it to a single speaker (“only what Dana said”).
2 · Visual — what's on screen
CLIP embeds sampled video frames and your query into the same space, so “a rocket blasting off” matches the footage even if nobody narrates it. This is image understanding, not text — it finds scenes by their look. (It needs the visual model; without it the channel honestly returns nothing rather than guessing.)
3 · OCR — text on screen
Every frame is read at index time, so printed text is searchable: lower-thirds, slide titles, product labels, street signs, chyrons. A frame found by its caption reads as an honest “on-screen text” hit, distinct from a generic visual match. Deep dive →
4 · Summary — which file is about this
Coarser than the rest: each file gets a short summary at index time, and this channel searches those. It answers “which video is about the merger,” not “which second.” Because summaries are file-level and a speaker filter is segment-level, this channel sits out speaker-scoped searches.
5 · People — who's in it
A Pro channel, ticked by default in the search bar for Pro users. It returns nothing unless your query names a person MediaFind knows — a face cluster you've named, or a recognized public figure. Name one and you get every clip they appear in, linked back to their face. Free-tier searches leave it out. How recognition stays private →
6 · Entity — exact names
The one thing semantic search is worst at. Embeddings blur “Acme Corp,” “Acme Inc,” and “a generic widget company” together, so when you want every clip that names exactly Acme, you need a literal index. The entity channel matches transcript and on-screen text against names MediaFind already trusts — keyless, exact, high-precision. Deep dive →
7 · Details — the facts about the file
What's attached to a file rather than inside it: the filename and folder, your tags and collections, its auto-assigned category, chapter titles, the URL it came from, and any notes you've written on it. Like entity, it's a literal match, not a semantic one — so the note you left on a clip last month is findable from the same search bar, without switching modes.
8 · Action — what's happening
Zero-shot CLIP, but for verbs: “running,” “a handshake,” “someone cooking.” It matches the actions tagged at index time, and a CLIP pass reaches actions outside that vocabulary too. It's free and keyless, and it reads what a normal index already stores, so it rides the default — a plain query can land on what someone is doing, not just on what was said. How zero-shot recognition works →
9 · Scene — where it is
The setting of a frame — kitchen, beach, office, courtroom, stadium, forest. Each frame is checked against a curated vocabulary of places. A small classifier trained on CLIP embeddings makes the call for every place it was trained on, and a place only “fires” when it clears that place's own calibrated threshold; zero-shot CLIP covers any other place — or the whole list, when the classifier can't run — and there a scene has to clearly beat generic non-place anchors to fire. You can also free-text any place (“rooftop bar”) and it searches frames directly, beyond the fixed list. Both halves score the frame embeddings MediaFind already stored, with no separate detector pass, which is why it rides the default.
The opt-in channels
These stay off until you switch them on — usually by picking that mode in the search bar. Two are Pro features; the other two need a detector to have run over your library before they have anything to say.
10 · Logo — which brands appear
Brand-logo detection by name or by a sample image, again via keyless zero-shot CLIP reusing the frame embeddings. This one is a Pro channel and never folds into the default mix. Recognition deep dive →
11 · Audio — non-speech sound
Sounds that aren't words: applause, a dog barking, music, a doorbell. It's opt-in and stays quiet (returns nothing) until a detector has populated the audio-event index for your library. Spotting which clips reuse the same track is a separate on-device feature, not something this channel searches.
12 · Object — things, and how many
Detected objects with bounding boxes and counts, which powers count-filtered queries like “three or more people.” Like audio, it's opt-in and waits until an object detector has run over your frames.
13 · Shadow — what's missing
The odd one out. Every other channel searches the files you have; this one surfaces media that's probably missing from your library, inferred from corroborating evidence in the files you do have. Those come back as their own cards, never merged with a real file's result. It's a Pro channel and stays empty until a shadow scan has run over your library — one is queued automatically after indexing.
Three refinement modes
The last three live in the registry but are never part of the default blend — they're standalone modes you select on their own, to slice an existing search a different way.
| Mode | What it matches |
|---|---|
| 14 · Color | The dominant palette of a frame — find shots by their look (“mostly teal,” “warm amber tones”) |
| 15 · Emotion | The affect of what's said — the angry exchange, the excited pitch — over the transcript |
| 16 · Phonetic | Sounds-like recall — catches names and words the transcript spelled differently than you typed |
How the channels become one list
Running many retrievers is only half the design; the other half is blending them well. Three things make the fan-out work:
- Parallel, isolated execution. Each selected channel runs on its own thread with its own database connection, so a five-channel search isn't five times slower than one. The query is encoded once and the read-only vector shared across the text channels — encoding is the dominant per-query cost, so this removes the redundant passes.
- Reciprocal-rank fusion. Channels return scores that aren't comparable — a CLIP cosine and a cross-encoder logit live on different scales — so MediaFind fuses by rank, not raw score. A moment that several channels each rank highly rises to the top.
- Corroboration, deduplicated. If the visual, on-screen-text, and logo channels all surface the same two-second moment, it appears once — ranked higher for the agreement — and the result lists which channels matched it. One moment, not three rows.
Why a registry, and not one big retriever
The channel design is what lets MediaFind grow sideways. Scene detection, the entity index, count-filtered object search — each shipped as a new channel slotted behind the same dispatch-and-fuse contract. It's also what keeps the product honest about its dependencies: a channel whose model isn't installed degrades — to a simpler fallback where it has one, to returning nothing where it doesn't — while every channel that doesn't need that model keeps working. At worst you lose ways to search, never the search.
And it's why the single search bar can answer such different questions. You're not choosing a mode for every query — you're aiming a fleet of specialists at your library at once, and reading back the handful of moments they agree on.
Said, shown, written, who's in it, what's happening, where, which brand, what sound. Eight kinds of question, sixteen channels, one ranked list — all on your Mac, with nothing uploaded.
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