AI Solutions for Media & Entertainment
Tag the library. Reach the right viewer. Spend less per title.
- Reduction in Content Production Costs
- 20–40%Reduction in Content Production Costs
- Improvement in Audience Engagement
- 30–60%Improvement in Audience Engagement
- Increase in Content Personalisation
- 25–35%Increase in Content Personalisation
- Faster Content Discovery & Tagging
- 50–70%Faster Content Discovery & Tagging
Industry Challenge
There is more content than a viewer can sort through. Audiences split across screens and languages. Making, tagging and clearing each title costs real money, and piracy takes a share of what is left.
AI Opportunities
- Cut the cost of tagging and clip work
- Show each viewer the next title worth watching
- See which topics are rising this week
- Place ads where they earn, not where they fit
- Screen comments and clips as they arrive
- See which subscribers are about to leave
Our AI Solutions
AI Content Creation
Draft scripts, blurbs, thumbnails and social cuts.
Video Intelligence
Tag scenes, faces and objects, and pull the highlights.
Audio Enhancement
Clean the sound, lift the voice, dub the track.
Audience Intelligence
See what a viewer watches, skips and drops.
Content Moderation
Screen for unsafe clips and copyright matches.
Ad & Revenue Optimisation
Place ads by break, title and viewer, and price the slot.
Top AI Applications
- Personalised Content Recommendation
- AI Highlights & Auto-Summarisation
- Automated Video Editing
- Content Localisation & Dubbing
- Real-Time Live Captioning
- Audience Sentiment Analysis
- Piracy Detection & Prevention
- Predictive Content Performance
Why Media & Entertainment Is Ready for AI
Media firms face two problems, and AI answers them very differently. Recommendation changes what a viewer finds. Tagging, transcription and rough-cut work change what each title costs to prepare.
For most operators outside the big platforms, the second is the faster win. Metadata and language work is costly, repetitive and directly cuttable. Recommendation gains depend on how big your library and your audience are.
What We Need From You
You almost certainly have most of this already. Gaps are workable — they change the sequence, not the feasibility.
- Your catalogue with whatever metadata exists today
- Viewing or consumption history at user level
- Media files for tagging, transcripts and highlights
- Subscription and churn records
- Ad inventory and performance data where you sell ads
How an Engagement Runs
- 1
Audit the catalogue
Metadata quality sets the ceiling. We check coverage before promising what recommendations can do.
- 2
Automate metadata
Vision and audio models tag scenes, objects, faces and mood, with an editorial pass on the layer that needs judgement.
- 3
Recommend and test
Recommendations go live against a holdout, so watch-time lift is measured and not assumed.
- 4
Extend to production
Highlights, captions and language versions follow, since they reuse the same media pipeline.
Streaming Platform
Challenge
Watch time was low, the library was thin on tags, and churn was high.
Our Solution
We built a recommender, tagged the library and scored churn risk.
- Increase in Watch Time
- 45%Increase in Watch Time
- Reduction in Churn Rate
- 35%Reduction in Churn Rate
- Increase in Ad Revenue
- 28%Increase in Ad Revenue
Expected Impact
Higher Engagement
The right title reaches the right viewer at the right time.
Operational Efficiency
Less tagging by hand, fewer repeat passes over one file.
New Revenue Streams
Ad slots priced on what the viewer is worth.
Cost Optimisation
Lower cost per title to make, store and ship.
Stronger Brand Loyalty
Viewers who come back without being chased by an offer.
Media & Entertainment AI — Common Questions
Yes, and they matter more. On a big library the problem is search. On a small one it is order and retention. Attribute models work well without the viewing volume the large platforms rely on.
It is faster and more even, but different. Vision and audio models tag objects, scenes, faces and mood well. Editorial nuance still needs a person. Most teams tag by model first, then review the layer that matters.
Yes. Perceptual fingerprints survive re-encoding, cropping and small edits, and are matched against the sources you watch.
No. It does the mechanical passes: rough cuts, highlights, transcripts, format variants. Editors then spend their time on the choices that make the work distinct.
Ready to Reimagine Your Content Strategy?
Reach more viewers, in more languages, without doubling the work.
Book a Free Consultation