What the research shows
80%
of what Netflix subscribers watch comes from its recommendation engine — reportedly saving over $1B a year in reduced churn
80–90%
efficiency gains studio executives expect from AI in VFX and 3D asset creation, per McKinsey's 2025 research
$60B
in industry revenue McKinsey projects could be redistributed within five years of AI reaching mass adoption in film and TV
The challenge
Great content still needs great operations behind it.
Metadata and tagging can't keep pace with the catalog
Every title needs accurate metadata, ratings, rights windows, and discovery tags before it can be recommended, localized, or monetized correctly. Doing that by hand doesn't scale with a growing catalog, and the gaps quietly suppress otherwise-good content from ever being found.
Localization and compliance review are manual bottlenecks
Subtitling, dubbing, and territory-specific compliance checks — ratings, restricted content, rights windows — queue up behind human reviewers, delaying global release schedules and creating inconsistent quality across markets.
Personalization gets treated as a moonshot data-science project
Recommendation-driven retention is one of the best-proven returns in the industry, but building it usually gets scoped as a large, standalone data-science initiative rather than a workflow you can put into production in weeks.
Use cases
Where AI agents move the numbers that matter.
Production workflows we design and build on the Microsoft platform your organization already runs.
Every title tagged, rated, and rights-mapped automatically as it enters the catalog
An agent that reads new content and existing catalog gaps, generates discovery metadata, applies content ratings and rights windows by territory, and flags missing or inconsistent tags — feeding your CMS and recommendation engine automatically instead of waiting on a manual tagging queue.
A recommendation engine sized to your catalog, not a hyperscaler's R&D budget
An agent that scores content against viewer engagement and churn signals to power personalized recommendations and programming decisions — built on the Microsoft stack, sized for your catalog rather than requiring a Netflix-scale data science team.
Subtitling, dubbing QA, and territory compliance reviewed in parallel, not in a queue
An agent that pre-checks localized subtitle and dub drafts for accuracy and flags territory-specific compliance issues before human reviewers finalize sign-off — compressing review queues that otherwise gate global release dates.
Ad inventory and rights obligations tracked without a spreadsheet army
An agent that reconciles ad delivery against contracted inventory, tracks content rights windows and royalty obligations across territories and platforms, and flags expirations or discrepancies before they become a compliance or revenue problem.
Why CommonLogic
We build for how media organizations actually operate.
Sized for your catalog, not built for a hyperscaler's.
You don't need Netflix's data science headcount to get a real recommendation and metadata system. We design agents scoped to what your catalog and team actually need, running inside Azure.
Content and rights data stay inside your Azure environment.
Catalog metadata, viewer data, and rights records are processed inside your Azure tenant, authenticated via Entra ID. No third-party AI vendor trains on your content or your audience data.
Editorial and programming judgment stays yours.
The agent tags, scores, and flags — your editorial, programming, and rights teams make the actual calls. Automation targets the manual work around those decisions, not the decisions themselves.
How we engage
Where we typically start with media organizations.
Every engagement begins with a clear problem. These are the services that apply most often in this industry.
Multi-Agent Workflow Design
For catalogs and networks with a specific workflow to automate — metadata tagging, localization review, rights tracking. We design the agent and deliver something your team uses from day one.
Learn more →Microsoft Ecosystem Integration
For media companies running their CMS, DAM, or rights systems inside Azure and Microsoft 365. We connect agents natively to what you already run — no middleware, no parallel system to check.
Learn more →Enterprise AI Strategy
For organizations evaluating AI across content operations and personalization. We map the highest-value opportunities against your catalog and produce a sequenced roadmap.
Learn more →Common questions
What media buyers ask before they engage.
If something isn't answered here, reach out — we respond quickly.
Do we need a data science team to run this?
+Will this replace our editorial or programming team?
+How does this handle rights and territory complexity?
+How fast can this be up and running?
+Ready to turn your catalog into recommendations that convert?
One working session is enough to identify the highest-value workflow to automate first.
Scoped to the Microsoft stack you already run.