Media & Entertainment · Content metadata automation
Your content catalogue is untagged, and every search confirms it
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.
The first call is free · 45 minutes · no obligation
What we build
What a Banao metadata automation deployment includes
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.
Scene and genre classification
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.
Face and talent recognition
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.
Topic and entity extraction
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.
Rights and clearance flagging
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.
Bulk archive backfill
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.
MAM and CMS write-back
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.
Receipts
Where this is already running
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.
Automated tagging across digital content properties at ingest
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.
Dogfooding
We run our own operation on the AI we sell
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.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When content metadata automation is the wrong call
Not every content backlog needs an AI build. We will tell you before you scope one:
- Small libraries: under a few hundred hours, a coordinator with a structured spreadsheet tags faster and costs less than a trained model. We will say so.
- No usable audio or transcript: if footage has no speech track, no captions, and no on-screen text, the model works only on visuals — accuracy drops sharply and week one becomes a source-quality project.
- Taxonomy that changes every quarter: if what a genre or topic means shifts frequently, the model's label drift outpaces its value. A stable, agreed taxonomy is a prerequisite, not a nice-to-have.
How we start
How we start — prove the cost before we build the fix
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.
- 01
AI Discovery Sprint
2 weeks · fixed price
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.
- 02
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.
- 03
Production and continuous improvement
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.
FAQ
Frequently asked questions
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.
Get started
Show us your content taxonomy and we will find where tagging breaks
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 Discovery Sprint