OTT & Media · Engineering, no AI
A pay-TV platform modernised while it stayed live
The front end of a white-label video platform, revamped feature by feature inside the vendor’s own team: new modules, a faster interface and a quieter API.
- Client
- A media technology leader: pay-TV and OTT solution provider
- Segment
- OTT & Media, legacy modernisation
- Platform reach
- About 500+ video streaming providers as potential users
- Role
- Technology partner, embedded in the client’s team
- OTT front end
- EPG
- nPVR
- Sentry
Before
What was true before
The client’s white-label platform lets content owners and distributors launch branded video services. Part of it needed revamping: existing functionality reworked, new features added, and performance improved without stopping the platform its customers already ran on.
- Changing a platform other businesses already run their services on
- Device models behaving differently by release date and even release year
- Splitting the work cleanly with the client’s in-house design and back-end teams
After
What we did
Oxagile owned OTT front-end development, the design of new features and performance optimisation, alongside the client’s in-house design and back-end specialists. Performance was tuned per device model and release year, an approach the case describes as industry-first for the initial customer.
Build log
How it was built
- 01
Integrated Sentry for app performance analytics
- 02
Added channel media cards to content discovery and an EPG module to the guide
- 03
Revamped the EPG for web and made network PVR run across all supported platforms
- 04
Improved settings, filtering and content search
- 05
Refined the live-mode guide and built a UI for content locked within the platform
- 06
Cut component re-rendering and API requests, tuned per device model and release year
Measured
Results
As reported in the published case.
- reported
- Component re-renders cut by 50%
- reported
- API requests to the back end cut to 1 in 3
- A modernised front end with instrumented performance is the base that AI features, such as recommendations and contextual search, are added onto. This engagement did not include them.
This engagement did not include AI. It is here as evidence of the engineering that AI work sits on.
Engagement
Where this work sits
Next step
Yours would be the next one.
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