OTT Discovery Layer

AI/ML Recommendations & Personalization for OTT Platforms

The biggest driver of churn is not poor content quality — it is poor content discovery. When viewers cannot find something worth watching in the first few minutes, they leave. And in the OTT market, they rarely come back.

Promwad builds AI-driven recommendation and search systems that connect the right viewer to the right content at the right moment, turning passive browsers into loyal, paying subscribers.

✓ Higher ARPU through personalized content discovery & targeted ads

✓ Lower churn with recommendations that keep viewers coming back

✓ More conversions from trial to paid subscription

Our Partners and Companies Employing Promwad Solutions

When Viewers Can't Find Content, You Pay for It

Generic carousels and alphabetical search were acceptable when catalogs were small. They are a liability today.

Research consistently shows that viewers who do not find engaging content within two to three minutes are highly likely to abandon the session entirely. Netflix attributes over 80% of content consumption to its recommendation engine. Amazon estimates that personalization drives more than a third of its total revenue.

For OTT platforms competing without algorithmic discovery, the consequences are visible in three places:

Watch time drops

Viewers who cannot find content quickly watch less, generate fewer ad impressions in AVOD models, and give your platform fewer opportunities to demonstrate its value.

Conversion suffers

Free trial users who never discover content that matches their taste have no reason to subscribe. A generic experience makes your catalog feel smaller than it actually is.

Churn accelerates

Subscribers who feel the platform no longer surprises them stop renewing. In niche content markets — sports, nature, faith-based, regional language — the audience is loyal when engaged.

Poor discovery is costing you subscribers today. Let's calculate what smarter recommendations could recover.

Talk to OTT experts

What Promwad Builds

We design and integrate AI/ML recommendation and search layers that plug into your existing OTT stack, whether you run a white-label platform, a custom backend, or a legacy architecture in transition.

Our recommendation systems are built around four core capabilities:

Vector search and semantic content discovery

Finds relevant content even when viewers don't know exactly what they're looking for — understanding context and intent, not just keywords. Critical for niche and multilingual catalogs.

User clustering and behavioral segmentation

Groups viewers by real behavior — what they watch, how long they stay, where they drop off — and tailors the experience to each segment individually.

Content graph models

Links catalog metadata, user profiles, and interaction data into a connected structure, surfacing relevant content across genres, formats, and languages beyond what collaborative filtering alone can achieve.

A/B testing frameworks

Built-in tooling to run controlled experiments on recommendation layouts and ranking logic, so you optimize for real engagement metrics — not proxies.

How It Works: The Recommendation Pipeline

The system operates across four interconnected stages:

Data ingestion

User events are captured in real time — play, pause, skip, search, session duration — alongside content metadata: genre, language, tags, recency

Model layer

Similarity search, behavioral clustering, and content graph models work together to map relationships across your catalog that no single algorithm can cover alone

Serving layer

Recommendations are returned via low-latency API calls that fit into your existing player and UI architecture without adding perceptible delay

Feedback loop

Watch-time signals and skip behavior continuously refine model performance, with A/B testing running in parallel to validate changes before full rollout

Our Tech Stack

We work with a vendor-neutral stack, selecting tools that fit your infrastructure, not the other way around:

Model development

PyTorch, TensorFlow

Vector / similarity search

Pinecone, Weaviate, Qdrant

Managed ML services

AWS SageMaker, GCP Vertex AI, Azure ML

Your catalog, your audience, your numbers. We'll help you build the discovery layer that turns browsers into subscribers.

Book 24 hours call

What This Means for Your Business

Recommendation quality has a direct line to revenue in every OTT monetization model.

AVOD

Longer sessions mean more ad impressions. Personalized content surfaces titles that keep viewers engaged past the first episode, the first ad break, and the first hour.

SVOD

Subscribers who consistently find content value churn at a lower rate. Industry data suggests that platforms with strong personalization see monthly churn rates 30 to 40 percent lower than those relying on static editorial carousels.

TVOD and hybrid models

Recommendation systems can identify the right moment to surface a premium purchase or upgrade prompt — based on viewing patterns that signal intent, not just demographics.

Targeted advertising

When combined with behavioral segmentation, your ad inventory becomes more valuable. Advertisers pay more for audiences that are demonstrably engaged and accurately profiled.

Built to Integrate, Not Replace

We design our AI/ML layer to integrate with your existing architecture, without rebuilding your platform:

Microservices-ready API design that fits into your current OTT backend

Compatible with multi-CDN and multi-device delivery pipelines already in place

Supports multilingual catalogs and multi-region deployments

Works across Smart TV, Android TV, iOS, Android, and web players without duplicated logic

No requirement to move your infrastructure to a specific cloud provider

For platforms undergoing broader transformation — from monolith to microservices, or from client-side to server-side ad insertion — the recommendation layer can be introduced incrementally, alongside other modernization work.

Ready to see what personalization could do for your ARPU?

Get an expert estimate

Privacy & GDPR Compliance

Data minimization

Only the behavioral signals you actually need are collected and stored

Anonymized identifiers

User IDs in model training are never linked to personal data

Consent management

Consent hooks are integrated at the application layer from day one

Retention controls

Data retention windows are configurable per your policy and jurisdiction

Data residency

Behavioral data can stay within your own cloud — nothing sent to third-party providers

DPA & PIA support

We help you prepare the data processing agreement and privacy impact assessment before go-live

Case Study: Android TV & Mobile App for Wildlife Content Streaming

With personalised recommendations that turned trial viewers into paying subscribers.

Challenge

A wildlife content producer needed to break free from social media platforms and build their own distribution channel. Without dedicated apps, they had no way to monetise content reliably or create a branded experience across mobile and TV devices.

Solution

We delivered a white-label platform with custom Android TV and mobile apps (iOS & Android), supporting AVOD/SVOD models, Chromecast and AirPlay, and live streaming. The platform includes personalised recommendations based on viewing history and user preferences, targeted advertising, and audience analytics to optimise content strategy.

Result

Within seven months, the apps were downloaded over 35,000 times. Free users average 20 minutes per session, and 20% of trial users converted to paid subscriptions — 13% of them staying for more than six months.

Read the full story: Personalised Recommendations for Streaming Platform

Android TV Platform & Mobile App Development for Wildlife Content Producer

Trusted by Tech Leaders Across 25+ Countries

Promwad builds recommendation and personalization systems for OTT platforms serving global audiences — from niche content producers to large-scale media operators. Our clients include Sony and B1 Smart TV, and we have delivered video and discovery solutions with high uptime and large concurrent audiences across more than 25 countries.

When engagement and retention are on the line, our partners trust us to get the AI layer right.

Is Your Catalog Losing Viewers to Poor Discovery?

Promwad builds AI recommendation layers that cut churn and extend watch time — proven across OTT platforms.

Book a call within 24 hours — let's map out your recommendation layer.

form.antibot { display: none !important; } You must have JavaScript enabled to use this form.

Tell us about your project

We’ll review it carefully and get back to you with the best technical approach.

All information you share stays private and secure — NDA available upon request.

Prefer direct email?
Write to info@promwad.com

Name *

Work email *

Phone *

Country *

Company *

Message *

By clicking the button, you agree to our Privacy Policy.

Terms *

FAQ

What data do you need to train recommendation models?

The minimum viable input is watch history, session duration, and basic content metadata. Richer behavioral signals — search queries, ratings, explicit preferences, device context — improve model accuracy significantly. We can design a data collection strategy alongside the recommendation layer if you are starting from limited data.

How long before the recommendations start performing well?

Initial models can be deployed within weeks, but recommendation quality improves over time as behavioral data accumulates. We typically plan for a cold-start period where content-based and editorial signals carry more weight, with machine-learned ranking taking over progressively as user data grows.

Can this work with a small catalog?

Yes. Content graph models and semantic search are particularly effective for smaller catalogs because they surface relationships that behavioral data alone cannot capture. We adjust the modeling approach based on catalog size and available user data.

How do you handle GDPR and user data privacy?

We design data pipelines with privacy compliance built in from the start: anonymized user identifiers, data minimization, configurable retention policies, consent management hooks, and the option to keep all behavioral data processing within your own cloud environment. We support the documentation and technical implementation required for a compliant deployment across EU markets.