Nagent AI
Industry Solution

Agentic AI for
OTT & Streaming

Autonomous agents that personalise content discovery, predict and prevent subscriber churn, automate metadata and marketing operations, and optimise monetisation โ€” at the scale streaming demands.

Why Nagent
๐ŸŽฏ

Hyper-Personalised Discovery

Recommendation agents go beyond "you watched" to serve content based on mood, context, and intent.

๐Ÿ”ฎ

Churn Prevention at Scale

Agents identify at-risk subscribers weeks before their cancel intent becomes an action.

๐Ÿ’ฐ

Monetisation Optimised

Paywall timing, upgrade offers, and ad targeting agents maximise revenue per user.

Compliance-ready
GDPRCCPACOPPAOFCOM
๐Ÿ“ฑOTT-native AI
๐Ÿ”’GDPR / CCPA
โšกReal-time recommendations
๐ŸŒMulti-territory
๐Ÿ“ŠFirst-party data
The Shift

From AI Assistants to Autonomous Systems

Copilots assist. Agents execute. The difference is who does the work after the AI responds.

Before โ€” AI Copilots

โŒSurfaces insights, humans still execute
โŒOne-shot responses to prompts
โŒNo memory across interactions
โŒManual approval at every checkpoint
โŒStatic โ€” no improvement over time

After โ€” Autonomous Agents

โœ…Agents execute workflows end to end
โœ…Multi-step reasoning and tool use
โœ…Persistent memory via Agent Smriti
โœ…Human-in-the-loop only where required
โœ…KARMIC loop โ€” agents improve every run
Segments

Agentic AI Across OTT & Streaming

๐Ÿ“บ SVOD Platforms

Subscription video-on-demand platforms with monthly/annual subscriber models.

Key challenges

Churn driven by content gaps between major releases
Subscriber acquisition cost requiring high LTV to justify
Personalisation accuracy declining without continuous signal

Agent use cases

Churn prediction agent
Content gap alert system
Personalised homepage agent
Subscriber win-back agent
Release campaign planner
Cohort LTV optimiser
By Department

Solutions Across
Departments

Every department has agents. Every agent has a defined input, execution, and outcome.

01๐Ÿ“ฑ Product & Personalisation

3 agent solutions

02๐Ÿ”ฎ Subscriber Retention

3 agent solutions

03โœ๏ธ Content & Marketing

3 agent solutions

04๐Ÿ’ฐ Monetisation

3 agent solutions

05โš™๏ธ Operations & Compliance

3 agent solutions

06๐Ÿ“Š Data & Analytics

3 agent solutions

๐Ÿ“ฑ

Product & Personalisation

3 agent solutions

01 / 06

Recommendation Engine Agent

Input

User viewing history, content metadata, contextual signals

Agent Output

Ranked content recommendations for homepage, continue watching, and next episode

Search Enhancement Agent

Input

User search query, catalogue metadata, viewing context

Agent Output

Intent-matched search results with mood and theme filtering

Content Discovery Agent

Input

User profile, underexposed catalogue, editorial calendar

Agent Output

Personalised content surfacing to expose underutilised catalogue

๐Ÿ”ฎ

Subscriber Retention

3 agent solutions

02 / 06

Churn Prediction Agent

Input

Engagement signals, viewing frequency, content completion, payment data

Agent Output

Churn risk score with intervention recommendation and timing

Winback Campaign Agent

Input

Lapsed subscriber data, content releases, offer eligibility

Agent Output

Personalised re-engagement offer timed to relevant content release

Pause & Cancel Flow Agent

Input

Cancel intent signal, subscriber profile, available retention offers

Agent Output

Contextual retention offer in the cancel flow with best available incentive

โœ๏ธ

Content & Marketing

3 agent solutions

03 / 06

Metadata Enrichment Agent

Input

Raw content asset, genre, mood, theme, technical specs

Agent Output

Complete metadata set for search, recommendation, and accessibility

Release Campaign Agent

Input

Title release date, target audience, content brief

Agent Output

Multi-channel campaign assets: email, push, social, and in-app

Content Performance Agent

Input

View data, completion rate, ratings, social buzz

Agent Output

Weekly content performance dashboard with renewal recommendation signals

๐Ÿ’ฐ

Monetisation

3 agent solutions

04 / 06

Paywall Optimiser

Input

Free user behaviour, content engagement, upgrade event data

Agent Output

Optimal paywall placement and offer timing per user segment

Upgrade Offer Agent

Input

Free tier usage patterns, premium content consumption, trigger events

Agent Output

Personalised premium offer with right incentive at peak intent moment

Advertising Audience Agent

Input

First-party viewing data, campaign brief, targeting requirements

Agent Output

Custom audience segment with predicted performance benchmarks

โš™๏ธ

Operations & Compliance

3 agent solutions

05 / 06

Content Rights Monitor

Input

Rights database, territorial windows, expiry dates

Agent Output

Rights expiry alerts + takedown instructions by territory

AVOD Compliance Agent

Input

Ad pod configuration, content type, COPPA/GDPR rules

Agent Output

Compliant ad insertion rules per content and audience segment

Accessibility Agent

Input

Video content, existing captions, audio description requirements

Agent Output

Accessibility gap report + automated caption and description drafts

๐Ÿ“Š

Data & Analytics

3 agent solutions

06 / 06

Subscriber Cohort Analyser

Input

Acquisition date, content consumption, churn events

Agent Output

LTV, engagement curve, and churn rate by acquisition cohort

Content ROI Agent

Input

Licensing cost, view data, subscriber attribution, churn contribution

Agent Output

Per-title ROI with renewal vs cancellation recommendation

A/B Test Analyser

Input

Experiment results, segment data, significance thresholds

Agent Output

Winner declaration with rollout recommendation and estimated impact

Live Agents

Agents You Can Try Right Now

Real agents. Real inputs. Real outputs.

๐ŸŽฏ

Content Recommendation Agent

Serves personalised content recommendations across homepage, browse, and post-playback โ€” incorporating viewing history, mood signals, and editorial priorities.

Takes as input

User viewing history
Content metadata
Editorial calendar and priorities

Produces

Ranked recommendation set
Homepage configuration
Next-episode prediction
Request Demo โ†’
๐Ÿ”ฎ

Churn Prediction Agent

Monitors viewing frequency, content completion, and account signals to score each subscriber's cancellation risk โ€” triggering retention interventions at the optimal time.

Takes as input

Viewing engagement signals
Content completion rates
Billing and account data

Produces

Churn risk score
Intervention recommendation
Optimal timing signal
Request Demo โ†’
โœ๏ธ

Metadata Enrichment Agent

Generates complete metadata for new and library content โ€” genres, mood tags, accessibility descriptions, and search keywords โ€” reducing time-to-searchable by 80%.

Takes as input

Raw content asset or synopsis
Genre and theme requirements
Accessibility standards

Produces

Complete metadata set
Search keywords
Accessibility descriptions
Request Demo โ†’
๐Ÿ’ฐ

Upgrade Offer Agent

Identifies free-tier users at peak intent moments โ€” mid-premium-content or post-trial expiry โ€” and serves the personalised offer most likely to convert.

Takes as input

User behaviour data
Premium content engagement signals
Available offer types

Produces

Personalised upgrade offer
Optimal display timing
A/B test variant
Request Demo โ†’
๐Ÿ“ฃ

Release Campaign Agent

Builds multi-channel marketing campaigns for new title releases โ€” email, push, in-app, and social โ€” personalised by subscriber segment and viewing history.

Takes as input

Title release brief
Target audience segments
Channel and budget

Produces

Campaign assets per channel
Audience targeting plan
Publishing schedule
Request Demo โ†’
๐Ÿ“Š

Content ROI Agent

Measures per-title ROI by combining licensing cost, viewer engagement, subscriber acquisition contribution, and churn prevention value.

Takes as input

Title licensing cost
View and engagement data
Subscriber attribution data

Produces

Per-title ROI metric
Renewal vs cancel signal
Comparative content scorecard
Request Demo โ†’
Resources

Research & Insights

No industry-specific posts found yet.

Browse all blog posts โ†’
Integrations

Works with Your Existing Stack

Nagent connects to the systems your bank already runs โ€” no rip-and-replace.

Streaming Infrastructure

Brightcove
JW Player
Bitmovin
AWS MediaLive
Mux

Analytics & Data

Amplitude
Mixpanel
Segment
Snowflake
Databricks

Marketing & CRM

Braze
Iterable
Klaviyo
Salesforce Marketing Cloud

Advertising

Google Ad Manager
FreeWheel
The Trade Desk
Xandr

+ 800 more via Composio and REST API ยท Browse all integrations โ†’

Implementation

From Pilot to Production in 8 Weeks

A structured delivery process built around your compliance requirements and existing infrastructure.

01

Platform & Content Audit

Week 1โ€“2

Audit recommendation performance, churn patterns, metadata quality, and monetisation conversion rates. Identify three highest-ROI agent deployments.

02

Data & System Integration

Week 3โ€“5

Connect to viewing event streams, content catalogue, CRM, and ad serving infrastructure. Configure personalisation rules and brand content guidelines.

03

A/B Test Phase

Week 6โ€“8

Recommendation and retention agents run in A/B mode against the control experience. Statistical significance required before full rollout.

04

Full Deployment

Month 3+

Agents personalise at full scale across the subscriber base. KARMIC loop improves recommendation models and churn prediction each month from outcome data.

?AARI Score

Free Assessment

5 minutes

๐Ÿ“Š
AARI โ€” Agentic AI Readiness Index

Is your streaming organisation ready for Agentic AI?

Take a 5-minute assessment to benchmark your AI maturity across strategy, data, talent, and infrastructure. You'll receive a personalised readiness score and a prioritised action plan โ€” free.

Strategy & leadership alignment
Data & integration readiness
Talent & change capability
Governance & compliance posture
Measurable Outcomes

Business Impact

0%

Subscriber churn reduction

Churn prediction and personalised retention

0%

Increase in content discovery

Personalised recommendation accuracy

0%

Upgrade conversion improvement

Paywall timing and offer personalisation

0%

Faster metadata time-to-searchable

Automated metadata enrichment

โ€œOur churn rate dropped 28% in six months. The recommendation agent improved content discovery scores by 34% โ€” subscribers are finding things to watch that they love, so they stay. The metadata agent alone saved 2,000 hours of editorial work.โ€

SW

Sophie Williams

VP Product & Data

Regional OTT Platform ยท Streaming ยท 1.2M subscribers

Playbook

The OTT & Streaming AI Playbook

Recommendation systems that actually retain subscribers
Churn prediction: from risk score to intervention architecture
Metadata at scale: the operational transformation case
Paywall and monetisation optimisation with AI
Building your streaming AI roadmap: personalisation, retention, and content operations
๐Ÿ“˜

Get the Playbook โ€” Free

PDF download ยท No spam

โฌก HelixBriefExecutorOutputReviewAgent Studio
Build it yourself

Design your own agents โ€” no code required

Use Agent Studio to describe your workflow in plain English. Helix, your AI system designer, will define the agent architecture, select models, configure prompts, and wire tools โ€” ready to run immediately.

Design multi-agent systems from a plain-English brief
Simulate and evaluate before going live
Connect to your existing tools and data
Self-improving via KARMIC โ€” no retraining needed
PlanBuildTestRunLearnAgentic AI Lab
Partner with us

We design, build, and deploy the agents for you

The Agentic AI Lab is Nagent's hands-on delivery service. Our team works with yours to identify the highest-impact workflows, build production-ready agent systems, and deploy them inside your environment โ€” with full knowledge transfer.

Workshop to identify high-ROI banking workflows
Custom agents built and tested for your processes
Deployed within your cloud or on-premise environment
Ongoing optimisation and performance monitoring
FAQ

Common Questions

Ready to deploy Agentic AI in OTT & Streaming?

Talk to our solutions team. We'll map the right agents to your workflows and operating model.