The streaming wars have made one thing clear: content libraries alone do not retain subscribers. Netflix, Spotify, and YouTube retain users through the quality of their recommendation and discovery systems, not just the quantity of their content. For challenger platforms, broadcasters, and digital publishers, the question is no longer whether to invest in content intelligence; it is how quickly they can close the capability gap.
Our media and entertainment-focused digital engineering practice builds content recommendation engines, audience analytics platforms, and AI content metadata tagging systems that are commercially viable to build and demonstrably effective at the engagement metrics that drive subscription and advertizing revenue.
Collaborative filtering, content-based, and hybrid recommendation models are personalized to individual viewing and listening behavior.
Real-time audience segmentation, cohort analysis, and behavioral analytics, integrated with ad tech and editorial systems.
AI-driven tagging, classification, and enrichment of content libraries, enabling better discovery and rights management.
Scalable video delivery architecture, adaptive bitrate optimization, and CDN strategy for live and on-demand streaming.
Programmatic integration, audience targeting, and yield optimization for ad-supported streaming and digital publishing.
Contract data extraction, rights expiry monitoring, and territory availability management for content operations teams.
Automated digital rights management and content protection to secure your assets without slowing down distribution.
Leveraging neural networks to generate high-accuracy, multi-language captions and sync them in real-time.
Deploying predictive models to identify churn risks and optimize subscriber lifetime value through personalized engagement.
Personalized content recommendation engine deployed for an SVOD platform, measured over a 60-day A/B test.
Subscriber risk model identifying churn-likely accounts 14 days in advance for targeted retention campaigns.
AI content tagging pipeline processing 50,000 assets for a broadcaster migrating to a new CMS.
Audience intelligence platform enabling first-party data activation for a digital publisher transitioning off third-party cookies.
Beyond audience-facing products, we automate operational workflows that consume disproportionate time from the editorial and operations teams.
A content recommendation platform uses collaborative filtering, content-based signals, and hybrid models trained on viewing, listening, and interaction history to surface the next piece of content most likely to retain a given viewer. Beyond the next-play recommendation, mature systems also inform editorial curation, notifications, and re-engagement campaigns.
Our audience intelligence platform is built on first-party behavioral and declared data collected on your own properties (viewing history, search queries, dwell time, ratings, playlist behavior, and subscription events). It does not depend on third-party cookies. For publishers transitioning from third-party data, we support contextual modeling and first-party audience segmentation that can be activated directly in your DSP or ad server.
Processing speed depends on the volume and format of the library, but for standard video and audio assets, our AI metadata pipeline can process thousands of assets per hour at scale. A 50,000-asset library enrichment (including genre classification, theme tagging, entity extraction, age ratings, and rights territory annotation) typically completes in a few weeks. Reach out to us at info@technoscore.com for a custom quote.
Yes. Our streaming churn prediction software analyzes engagement signals (declining session frequency, skipped content, abandoned streams, reduced search activity, etc) to produce a daily churn probability score for every active subscriber. With sufficient historical data, the model identifies high-risk subscribers several days before they would typically churn.
Our media and entertainment systems integrate with the major video platforms, including Brightcove, JW Player, Mux, and AWS MediaLive/MediaPackage. On the ad tech side, we connect with Google Ad Manager, The Trade Desk, Magnite, etc. For analytics, we use tools such as Amplitude and Adobe Analytics. Content and distribution workflows are connected via Contentful, Adobe AEM, and custom REST APIs.
Yes. Our content operations tooling includes AI-powered moderation for UGC platforms, detecting policy violations across video, audio, images, and text using computer vision and NLP models. The system classifies content against configurable policy rulesets (nudity, hate speech, graphic violence, copyright signals) and routes flagged content to a human review queue with confidence scores and policy category attached.
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
