Media & Entertainment · Content recommendation
Every stream session ends with a choice: stay or go. Banao builds recommendation models trained on your specific catalogue and watch history — not a generic model borrowed from another platform — so the title a viewer sees next earns the next hour of attention.
We wire the model into your CMS and OTT layer, handle cold-start for new users and thin-catalogue verticals, and give your editorial team controls so curated priorities still surface. The model does the ranking; editorial judgement stays with your team.
A recommendation system is not just a ranking model. It is the data pipeline, the cold-start logic, the editorial override layer, and the feedback loop — we build all of it.
Trained on your specific watch history and content graph — not a pre-trained base model fine-tuned on a sample. Genre, duration, audience segment, and time-of-day signals are learnable when the data is yours.
A new subscriber has no watch history. We build cold-start logic using onboarding signals, device context, and content similarity so the first session is not a blank rail.
Content heads need to prioritise a new release or suppress an expiring title. The model exposes controls so editorial priorities apply on top of the ranking without retraining.
Recommendations reach viewers in multiple places. We wire the same model into your homepage rails, post-play end-cards, and re-engagement email templates — one model, consistent signal.
We build an experimentation layer and a per-user ranking explanation so your product team can test variants and your legal team can audit the output.
Watch-through rate, drop-off point, and explicit ratings feed back into the model on a scheduled cadence. Accuracy improves from real audience signal, not re-labelling.
Metrics shown dotted (··) are being verified for our case-study metrics pack — published only once confirmed.
Times Internet
Times Internet runs some of India's largest digital properties. Banao built a recommendation layer that surfaces the next article or video based on the reader's reading pattern and the content graph — with an editorial override panel so programme editors control what the model promotes.
Banao runs Vikaas — its own AI demand-gen system — across its own pipeline before any client sees it. The same discipline applies here: a recommendation model has to earn its keep inside an operation that depends on it, not just pass a benchmark.
When we say a model improves session depth, we mean it did so in a system that mattered to us first. That is the standard we bring to your platform.
A poorly scoped recommendation build costs more than it returns. We would rather lose the project than build one that doesn't pay:
Recommendation looks straightforward until you look at your actual data. We start by auditing what you have, not quoting off a spec.
We audit your catalogue, watch history, and session data — assess coverage, sparsity, and cold-start exposure — and return a ranked opportunity list and a go/no-go on building a ranking model now. Yours to keep. Proceed and the Sprint is credited against the build.
Data pipeline first: events from your OTT layer, CMS metadata, and audience segments. Then the ranking model, cold-start layer, and editorial override controls. Deployed and wired into your front-end surfaces.
Live deployment with an A/B testing framework, a model dashboard for your product team, and a scheduled retraining cadence. Accuracy compounds from real watch signals, not a static training set.
How much watch history do you need to train the model?
Enough to establish patterns — typically several months of session-level events across a reasonable active user base. The Discovery Sprint audits your data and tells you whether you have enough, or what it would take to get there with staged collection or augmentation.
Can the model work across different content types — video, articles, audio?
Yes. The ranking model handles mixed content types when the engagement signals are consistent. We normalise events across formats and let the model learn type affinity from the user's own consumption pattern.
Our editorial team needs to control what surfaces. Is that possible?
Yes, and it is a non-negotiable part of the build. We expose an editorial override layer so content teams can pin, suppress, and boost titles on top of the model's ranking — a new release can still break through without retraining.
How do we know the model is actually improving watch-through?
We build an A/B testing framework in from the start so control and variant groups are measured against agreed metrics — completion rate, session depth, return frequency — before a full rollout. You see the number before you commit to it.
We already have a basic recommendation feature. Can you improve it rather than replace it?
Often yes. The Discovery Sprint assesses whether the existing system can be improved by better data signals or retraining, or whether the architecture genuinely needs a rebuild. We don't replace something that can be fixed.
Bring your catalogue size and a rough sense of your session volume. In 45 minutes we'll tell you what a recommendation model could realistically do — and what it would cost to find out.
Book a 45-min scoping call
