Software for content that has to reach an audience at scale
Media & Entertainment
A media platform succeeds by being watched by more people than it was built for, which is exactly when most of them fail. This practice builds streaming and delivery platforms, rights and metadata systems, recommendation engines and the audience analytics behind every renewal decision.
01Start from where the audience feels it
Name the pressure the platform is under.
Media buyers usually name one of four pressures. A delivery platform that can't scale, rights management stuck in spreadsheets, recommendations that don't fit the audience, or analytics too fragmented to trust, each names a different pressure. Naming it narrows the route to the fitting practice.
Viewership spikes and content libraries have outgrown the platform serving them.Application modernization rebuilds the delivery platform in stages so it scales with the audience, not against it.
Licensing windows, metadata and rights sit in spreadsheets nobody keeps current.Custom engineering builds the rights and metadata system a catalogue this size actually needs.
Viewers scroll longer than they watch, and the engine isn't improving that.Recommendation and personalization models are evaluated against real engagement data before they ship.
Viewing, engagement and revenue data cannot be joined into one picture.Data modernization joins audience, engagement and revenue signals into one pipeline analytics can trust.
Streaming & Delivery Platforms
Content delivery platforms rebuilt to scale with the audience, not against it.
Content & Rights Management
Metadata, licensing and rights systems built for a real catalogue.
Recommendation Engines
Personalization models evaluated against real engagement before they ship.
Audience Analytics
Viewing, engagement and revenue data joined into one trusted pipeline.
02Engineered for the audience you don't have yet
Built to survive the spike, not just the demo.
Media engagements run a delivery process where load, caching and failover are architecture decisions made before launch, not patched in after a viral moment breaks the platform. Every release is tested against traffic patterns closer to a real audience than a staging environment ever sees.
Frame the traffic and scale surface
Expected peak viewership, catalogue size and regional reach are mapped before architecture begins, so scale is a design decision instead of a retrofit.
A capacity map before the buildDesign for the traffic you don't expect
Caching, failover and content delivery routing are built to absorb a spike well beyond forecast, because the biggest audience always arrives without warning.
Architecture built to absorb spikesValidate personalization against engagement data
Recommendation models are evaluated against real viewing behaviour before launch, and a model that only performs in a demo does not ship.
Recommendations tested against real behaviourShip behind a load-tested rollout
Every release is rehearsed against simulated peak traffic first, so the audience never becomes the first people to find a breaking point.
A release rehearsed under load
03Services for media and entertainment, with the delivery standards behind each
Find the media service you came for, and the standard it streams under.
A media buyer usually types the name of a platform into a search box, not a practice area. This ledger maps each platform to the deliverable behind it, the streaming and rights standards it must honour, and the practice responsible. The four standards below decide how content is packaged, protected and measured before an audience arrives.
| What you need | What we build | Standards and integrations | Where it lives |
|---|---|---|---|
| OTT streaming platform | Video ingest, transcoding, packaging and playback across web, mobile and connected TV, rebuilt in stages | HLS, MPEG-DASH, CMAF, H.264/HEVC/AV1, multi-origin CDN | Application Modernization |
| Digital rights and content protection | Licence delivery and playback enforcement so a title is only watchable where the rights allow | Widevine, FairPlay, PlayReady, Common Encryption (CENC), DRM licence servers | Custom Software Development |
| Content and rights management system | Metadata, licensing windows and territory rules enforced inside the catalogue system rather than a spreadsheet | EIDR identifiers, IMF (SMPTE ST 2067), EBUCore metadata, ISAN | Custom Software Development |
| Recommendation engine | Models trained on viewing behaviour and evaluated against real engagement before release | Feature stores, offline and online A/B evaluation, MLflow, Kafka event streams | Machine Learning Development |
| Audience analytics pipeline | Viewing, engagement, subscription and revenue events joined into one governed reporting pipeline | Conviva and Mux QoE data, Kafka, dbt, Snowflake or BigQuery | Data Engineering |
| Live event delivery and load readiness | Load-tested delivery routing, caching and failover rehearsed against simulated peak audiences | Low-latency HLS, WebRTC, SRT contribution, k6 or Gatling load testing | Performance Testing |
HLS and MPEG-DASH
The two adaptive streaming formats nearly every player expects. Packaging both through CMAF keeps one set of segments serving web, mobile and connected TV without duplicating storage.
Widevine, FairPlay and PlayReady
The DRM systems studios and rights holders require for premium content. Supporting all three from one CENC-encrypted asset is what makes a rights deal deliverable on every device.
Multi-CDN delivery
Content delivery networks sit between the origin and the audience. Routing across more than one, with health-based failover, is how a launch survives a regional CDN incident.
EIDR and IMF
Entertainment identifiers and the Interoperable Master Format give every title and version a stable identity. Rights, metadata and delivery all key off them, which stops catalogue conflicts before publication.
04Evidence and proof
Platforms that held when the audience actually showed up.
Media proof has to show the platform surviving real traffic, not a demo. These previews show that standard while written client approvals are collected.
Explore case studies
A content platform that held through its biggest launch week.
Lumen Stream expected a launch spike and got several times the forecast within a day. The delivery architecture absorbed it without a single stream failing to start.
- Peak traffic vs forecast
- Several times over
- Failed stream starts
- Zero
A recommendation engine that turned browsing into watching.
A streaming platform's audience kept scrolling longer than they watched. A retrained recommendation model, evaluated against real engagement data, lifted session length within one quarter.
- Scroll time
- Down
- Session length
- Up within a quarter
They rehearsed our launch day traffic before we did, and it showed when the real thing hit.
Sample · pending client approval
05Four doors into media engineering
Enter through the practice matching the strain.
A platform under load, an unmanaged rights catalogue, an underperforming recommendation engine and fragmented audience data each need a different starting point. The doors below route each situation to its practice, and every one carries the same scale-first discipline.
- Application Modernization
Scale the delivery platform
Streaming and content delivery infrastructure rebuilt in stages to carry real audience growth.
- Custom Software Development
Build the rights and metadata system
Custom engineering for licensing, metadata and rights workflows a real catalogue actually needs.
- AI & Automation
Rebuild the recommendation engine
Personalization models retrained and evaluated against real engagement before any claim ships.
- Data Modernization
Join audience data into one view
Viewing, engagement and revenue signals joined into a pipeline analytics teams can trust.
06Straight answers
Asked before a media platform launch.
01Can you handle sudden traffic spikes on a streaming platform?
Yes, that is the specific engineering problem this practice is built around. Caching, failover and delivery routing are load-tested against simulated peaks before launch, so a spike is rehearsed rather than a surprise.
02Do you build content and rights management systems?
Yes, including metadata, licensing windows and territory rules that a spreadsheet cannot enforce reliably. Custom engineering builds the rules directly into the catalogue system, so a conflict is caught before publication, not after.
03How do you build recommendation engines that actually work?
Models are trained and evaluated against real engagement data, not a generic benchmark. We measure what viewers actually watch after a recommendation, and a model that only wins in testing does not reach production.
04Can you join our audience data into one view?
Yes, across viewing, engagement, subscription and revenue systems that usually disagree. One governed pipeline gives every team the same numbers, so a renewal decision does not depend on whose report you read.
05What does media platform engineering cost?
It follows the scale and catalogue complexity a scoping engagement uncovers, since a niche platform and a mass-audience service carry very different load requirements. The estimate reflects your actual traffic, not an assumed average.
06Can you migrate a legacy content management system?
Yes, usually by running the new system alongside the old one until content, metadata and rights data are proven equivalent. Nothing cuts over until the migration is tested against the real catalogue.
07Do you handle digital rights and licensing rules?
Territory, window and platform-specific licensing rules are built into the rights system as enforced logic, not documentation someone has to remember. A title that should not be visible somewhere simply is not.
08Can you take over an existing media platform?
Yes, starting with an architecture and traffic-capacity assessment rather than assumptions. The handover includes a stabilisation plan so the platform keeps serving its current audience while new capability gets built underneath.