Industries · Media & Entertainment
Banao builds and deploys AI across the media workflow — metadata tagging, automated editing, recommendation, compliance, and archive search — for broadcasters, streamers, and digital publishers.
Every capability below runs inside a real content pipeline, wired to your MAM, CMS, and playout. We ship deployed systems, not demo reels.
Times Internet— AI tagging and recommendation running across its digital properties.
Each of these has a cost attached — a backlog, a turnaround time, churn, or a compliance fine. We start where the number is measurable.
Models that watch, listen to, and read your footage — tagging scenes, faces, logos, and topics at ingest — so editors and rights teams stop tagging by hand.
Shot detection and reel cutting that turns long-form footage into social clips, match highlights, and promos, with a human approval step before publish.
Recommendation models tuned to your catalogue and watch data, so the next title a viewer sees keeps them on your platform instead of a competitor's.
Automated checks for ad-insertion timing, profanity, logo bugs, and regional cut rules — flagged before air, not after a regulator's notice.
OCR, speech-to-text, and vision over decades of tape and film, turning a dark archive into a searchable, clip-level library your producers can query.
Speech-to-text, translation, and timing models that draft subtitles and dub scripts across languages, with linguists reviewing the cut, not typing it.
Numbers shown dotted (··) are still being verified for our case-study pack. The work is live; we won't publish a metric we haven't checked.
Times Internet
Times Internet runs some of India's largest digital news and entertainment properties. Banao builds AI that tags articles and video at ingest and feeds recommendation models, so editors stop hand-sorting and readers reach the next piece worth their time.
A national broadcast network
A broadcast network reviewed every regional feed by hand for ad timing, cut rules, and captions. Banao deployed automated compliance flags and a subtitle-drafting pipeline, so the review team confirms exceptions instead of scanning every minute of air.
Banao runs a ~300-person engineering company on its own AI before a client ever sees it. InterviewGod screens our hires. Vikaas runs our demand generation.
There is a difference between a vendor who has read about content AI and one whose own week depends on it. By the time a system reaches your pipeline, it has already survived ours.
Plenty of vendors will sell you a model regardless. We would rather tell you when not to build — it is why heads of content take our second call.
You have been pitched AI by half the vendors at the last trade show. We start by proving the cost of the problem, not by quoting a build.
On-site or remote. You leave with a ranked list of AI opportunities across your content pipeline, baseline ROI maths, and a go/no-go on each — yours to keep either way. Proceed, and the Sprint cost is credited against the build.
Data and rights plumbing first, then the model. We wire into your MAM, CMS, and playout systems — legacy formats included — and ship the ingestion pipeline as a deliverable.
Deployment with an editor approval step and a dashboard, plus change management for your content team. The models sharpen as more of your catalogue flows through.
Our archive is on tape and old file formats. Can AI touch it?
Yes — that is a common starting point. Banao runs OCR, speech-to-text, and vision over legacy tape transfers and mixed file formats, then indexes the output at clip level. The model needs a digitized signal, not a modern container.
Will AI editing or tagging replace our editors?
No. The pipeline does the repetitive pass — first-cut tags, rough clips, draft subtitles — and routes the result to your team for the editorial call. Output volume goes up; the judgement stays with people.
We have rights and licensing constraints on our content. Does that block this?
It shapes the build, not blocks it. We keep processing inside your environment, log every asset the models touch, and respect territory and window rules in the metadata. Rights handling is part of the scope from week one.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, and you keep the ROI model whether or not you continue. Worst case you have a free assessment of your content operation; best case you have your board business case.
How fast can something reach our pipeline?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout. Banao's ~300-engineer bench means work starts in weeks, not the months a new hire would take to ramp.
Bring your worst bottleneck — a metadata backlog, a subtitling queue, or a dark archive. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a 45-min scoping call
