Media & Entertainment · Content metadata automation
Banao builds content metadata automation that runs at ingest — classifying genres, scenes, faces, topics, and rights flags as each file lands in your MAM, so editors stop doing it by hand.
The model writes back into your existing CMS or MAM, handles broadcast, streaming, and archive formats, and runs on your current file store without a separate digitization step first.
Times Internet— AI tagging running across its digital content properties at ingest.
Metadata is infrastructure. A deployment that only tags titles or top-level genres adds little. We go to clip level, write into your schema, and handle the archive backlog alongside new ingest.
A multi-modal model that reads video, audio, and transcript together to assign genre, sub-genre, mood, and content rating at clip level — not just at title level — so search and recommendation work on the actual content, not the title card.
Index every known talent and presenter appearance across your library. Rights holders, editors, and clip teams find a face in thousands of hours of footage without watching a second of it.
NLP over transcripts, closed captions, and on-screen text pulls named entities, topics, brands, and locations — the structured metadata that drives downstream recommendation and archive search.
Detect music, logo appearances, and face clearances at clip level and surface them against your rights database before a clip goes live — not after the clearance team calls.
Run the same ingest pipeline over your existing library in overnight batches, so historic content reaches the same tag depth as new material without a separate archival project.
Metadata writes directly into your existing MAM, CMS, and playout systems. The taxonomy maps to your schema. No middleware layer and no manual copy-paste from an AI dashboard into your workflow.
Numbers shown dotted (··) are still being verified for our case-study pack. The work is live; we will not publish a metric we have not checked.
Times Internet
Times Internet runs some of India's largest digital news and entertainment properties. Banao deploys AI that classifies and tags articles, video, and audio at the point of ingest, so editorial teams retrieve and surface content instead of filing it.
Banao operates a ~300-person engineering company on its own AI products. InterviewGod screens our hires. Vikaas runs our demand generation. Every system goes through our own operation before it reaches a client's pipeline.
Content metadata automation is the same pattern: the models handling classification and extraction have to survive production use-cases with real edge cases before we sign off on a client build.
Not every content backlog needs an AI build. We will tell you before you scope one:
A metadata project that starts with model selection before it maps your taxonomy and your MAM schema tends to rebuild twice. We look at your actual content flow first.
We audit a sample of your existing content, map your current taxonomy and MAM schema, and run a feasibility pass on your hardest classification cases. You receive a ranked list of metadata automation opportunities, accuracy estimates, and ROI maths — yours to keep. Proceed, and the Sprint cost is credited against the build.
Model training on your content and your label rules, integration into your MAM and CMS write-back, and a bulk-backfill plan for the existing archive. The taxonomy mapping and the integration are deliverables, not assumptions.
Deployment with an editor review step, a tagging-quality dashboard, and a feedback loop so editor corrections sharpen the model over each ingest cycle rather than drifting.
Our MAM has a custom taxonomy. Can you map to it?
Yes — taxonomy mapping is part of the Discovery Sprint scope. We work from your existing schema, not a generic one, and the model writes to your field names and vocabulary. If your taxonomy has gaps, we will surface them in the Sprint before the build starts.
How much labelled content do you need to train the model?
It depends on the number of categories and the visual or audio complexity of each. A genre classifier typically needs a few thousand labelled clips. The Sprint establishes the exact requirement for your catalogue and whether your existing metadata can seed the training set.
Can the model handle multiple languages?
Yes. Speech-to-text and NLP components support major South Asian, European, and Middle Eastern languages. For less common languages, the Sprint identifies whether a pre-trained base model is sufficient or whether additional data collection is needed.
Will adding a tagging step slow our ingest pipeline?
Designed correctly, no. The model runs asynchronously alongside ingest and writes back within minutes of file arrival. For live or near-live workflows we set latency targets during the Sprint and architect to them before we build.
How do we measure whether the tagging is accurate enough to trust?
We agree precision and recall thresholds for each tag category with your editorial team before the build starts. The production dashboard tracks those thresholds per category so quality does not drift invisibly after launch.
In 45 minutes we can walk through your ingest flow, your MAM schema, and your worst-case classification examples — and tell you where automation pays back and where it does not.
Book a 45-min scoping call
