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Media, Gaming & Sports

Video Metadata Intelligence API

Most video libraries are searchable by title and almost nothing else. The content inside the file is the part people want to find, and it is usually invisible.

Streaming platforms
Broadcasters
Archive holders
Education platforms
Sports and news publishers

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

An archive of thirty thousand hours typically carries metadata created for a different purpose: a title, a transmission date, a rights note, perhaps a one-line synopsis written by somebody who had not watched it. Search returns almost nothing useful, clips cannot be found, and repurposing an old programme means somebody scrubbing through it.

Manual logging solves this properly and costs what it costs. Rates for detailed shot-level logging make full retrospective coverage of a large archive economically impossible, so it gets done for the recent and valuable material and never for the tail — which is exactly where the undiscovered value sits.

Automated metadata has become genuinely good at the mechanical layers: shot boundaries, speech transcription, speaker turns, on-screen text, topic segmentation, chapter points. It remains unreliable at editorial judgement, names it has never seen, and anything requiring knowledge outside the file. A build that is honest about that division — machine for the mechanical, human for the editorial — produces something usable; one that pretends otherwise produces metadata nobody trusts.

Who buys this

Streaming platforms, Broadcasters, Archive holders, Education platforms, Sports and news publishers

Category: Media, Gaming & Sports

What the API returns

What comes back

Shot and scene boundaries with timecodes
Speech transcription with word-level timing and speaker turn segmentation
Subtitle and caption files in standard delivery formats
Topic segmentation and chapter points with suggested titles
On-screen text extraction, including lower thirds and captions burnt into the picture
Scene-level and programme-level summaries with the timecodes they derive from
Keyword and entity tags with confidence values and source timecodes
Quality flags where audio, language or picture conditions make output unreliable

Endpoints

Method	Path	Returns
POST	/v1/jobs	Submit a video asset for analysis
GET	/v1/jobs/{id}	Job status and progress
GET	/v1/assets/{id}/metadata	Full structured metadata for an analysed asset
GET	/v1/assets/{id}/chapters	Chapter points with suggested titles
GET	/v1/assets/{id}/subtitles	Subtitle output in the requested format
POST	/v1/assets/{id}/corrections	Submit human corrections back into the record

Request and response

illustrative

Request

{
  "asset": { "id": "prog_20714", "uri": "s3://your-bucket/prog_20714.mxf",
             "duration_s": 3480 },
  "language_hint": "en-GB",
  "outputs": ["scenes", "transcript", "chapters", "subtitles", "summary", "entities"],
  "subtitle_format": "vtt"
}

Response

{
  "asset_id": "prog_20714",
  "status": "complete",
  "duration_s": 3480,
  "language_detected": "en-GB",
  "scenes": [
    { "from_s": 0.0,    "to_s": 94.6,   "shots": 12, "type": "studio" },
    { "from_s": 94.6,   "to_s": 612.3,  "shots": 48, "type": "location" }
  ],
  "chapters": [
    { "from_s": 0.0,   "title": "Introduction and panel setup", "confidence": 0.88 },
    { "from_s": 612.3, "title": "Coastal erosion at Dunwich",   "confidence": 0.74 },
    { "from_s": 1904.0,"title": "Funding debate",               "confidence": 0.61 }
  ],
  "transcript": { "words": 7421, "speakers_detected": 4,
                  "mean_confidence": 0.93 },
  "entities": [
    { "label": "Dunwich", "type": "place", "confidence": 0.95,
      "timecodes_s": [618.2, 701.4, 884.9] },
    { "label": "coastal defence levy", "type": "topic", "confidence": 0.71,
      "timecodes_s": [1912.6] }
  ],
  "summary": "A studio panel discusses coastal erosion, followed by a location report from Dunwich and a closing debate on local funding.",
  "quality_flags": [
    { "from_s": 2104.0, "to_s": 2188.0, "flag": "heavy_wind_noise",
      "effect": "transcript confidence below 0.6 in this region" }
  ]
}

Where the data comes from

Your video files	The source material, read from your storage — nothing useful happens without access to the actual media
Existing catalogue records	Titles, synopses, cast lists and transmission data, used to anchor names the model would otherwise guess
Your house style and taxonomy	Chapter naming conventions, topic vocabulary and subtitle standards, so output matches what your systems already expect
Speech and language models	Self-hosted or licensed; licence terms differ sharply on commercial use and on whether output may be redistributed
Existing subtitle files	Where they exist, they are both a quality benchmark and training material for your specific content and accents
Human corrections	Corrections made by your loggers, fed back so the same error is not repeated across the archive

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Shot boundary detection on frame differencing plus histogram analysis, with scene grouping above it — shots are a mechanical fact, scenes are an inference
Transcription with domain vocabulary injected from your own catalogue, which is what fixes proper nouns and programme-specific terms
Speaker diarisation reported as turn segmentation rather than identification, unless you hold a voice reference set and the legal basis to use it
Topic segmentation from transcript structure and visual change together, since either alone puts chapter points in the wrong places
Summaries constrained to content present in the transcript, with every claim traceable to a timecode; unsupported summaries are withheld rather than returned
Confidence reported per field and per region, so a reviewer knows which eighty seconds to check instead of the whole programme
Benchmarking against your existing human-logged material to produce a real accuracy figure for your content, not a published model score
Correction feedback loop with monitoring of error rates by genre, language and audio condition

What it changes for the business

Searchable archive

Material that could not previously be found becomes discoverable, which is where archive value actually gets realised.

Subtitle cost

Machine subtitles with human correction cost substantially less per hour than origination from scratch.

Clip and repurposing workflows

Timecoded topics and entities make finding a usable clip a query rather than an afternoon.

Recommendation inputs

Scene and topic metadata gives a recommender something to work with on titles that have no viewing history.

How it fits into your product

How it embeds

Asynchronous job submission with webhook callbacks, since a feature-length file is minutes of processing rather than milliseconds
Output written directly into your MAM or catalogue schema instead of a format your systems then have to translate
Subtitle delivery in the formats your playout and web players already consume
Deployment inside your own environment, which is normal when the media cannot leave your storage
Correction endpoints so human edits are captured as the authoritative record
Backfill tooling for processing an existing archive at a controlled rate and cost

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 12 weeks

Discovery and data review

Weeks 1–2

We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.

Architecture and prototype

Weeks 3–4

You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.

Production build

Weeks 5–10

Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.

Integration and handover

Weeks 11–12

We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

How accurate is the transcription?

It depends heavily on your material, and we would measure it rather than quote a number. Clean studio speech in a well-covered language is very good. Heavy accents, overlapping speech, location noise, archive audio and specialist vocabulary all degrade it, sometimes severely. The API flags low-confidence regions so correction effort goes where it is needed.

Can the summaries be published without review?

For internal search and discovery, generally yes. For anything customer-facing we would recommend review. Summaries are constrained to the transcript and validated against it, which prevents invention, but they still make editorial choices about emphasis that a publisher usually wants a person to approve.

Will it identify people on screen?

Not by default, and we would want a clear conversation before building it. Facial recognition carries specific legal requirements in several jurisdictions and is error-prone across demographics. Speaker turns and on-screen text names are usually what the workflow actually needs, and they carry far less risk.

What about languages outside the major ones?

Quality falls off, and for some languages and dialects it falls off to the point of being unusable. We would test on your actual material during the prototype and tell you which parts of your archive this is worth running against and which parts still need a human.

Can it replace our logging team?

No. It removes the mechanical work — timecoding, transcription, shot detection, first-pass tagging — and leaves the editorial judgement, which is the part your loggers are actually valuable for. Teams that redeploy rather than replace get far better metadata out of this.

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