Media and telecommunications
Platforms that hold up at peak audience, with AI that keeps people watching.
Why media and telecom platforms are judged at their busiest moment
Media and telecom platforms are judged at their busiest moment: a live event, a release, a viral post. Audiences leave when a stream stalls or a feed feels irrelevant, and the cost of data, cloud and AI grows with every new viewer.
Audience peaks
A live event or a viral post multiplies traffic in minutes.
Attention is short
Audiences leave when a stream stalls or a feed feels irrelevant.
Costs that grow with viewers
Data, cloud and AI costs rise with every new user.
What changes
What changes with the right engineering: platforms that hold up at peak audience, recommendations that keep people watching, content understood by machines as well as by editors, and cloud and AI costs that track revenue.
Platforms that hold up at peak
Designed and tested for the busiest moment, and watched in production.
Recommendations that keep people watching
Personalisation trained on your own audience and content data.
Content machines can read
Video, speech and text indexed, searchable and easier to moderate.
Costs that track revenue
Cloud and AI spend measured and cut where it does not earn its place.
Where we work
What we build
- High-concurrency back ends, microservices and APIs
- Native iOS and Android and cross-platform apps (React Native, Flutter)
- Recommendation and personalisation engines
What we build
- Streaming and audience web and mobile portals
- Video recognition, OCR and speech AI to index and search content
- Arabic-first language models for regional content
Typical integrations
- Content and media asset systems
- Audience data platforms and analytics
What we build
- Connected device software and enterprise mobile platforms
- Self-service portals and transactional conversational AI for customers
- AI contact centres with speech-reasoning voice agents
Typical integrations
- CRM and service platforms such as Salesforce and ServiceNow
What we build
- Audience classification and targeting models
- Embedded analytics and self-service data products for advertisers
- Privacy controls: consent, masking and tokenisation of personal data
Typical integrations
- Data warehouses and lakehouses such as Snowflake and Databricks
What we build, connect and secure
Platforms built for peak load
Back ends designed and load-tested for the busiest moment, with chaos testing to prove they recover.
Content and video AI
NLP, sentiment and deep semantic analysis, and video recognition and OCR, to tag, search and moderate content.
Personalisation
Classification, personalisation and recommendation engines trained on your own audience data.
Real-time data
High-velocity ingestion and streaming pipelines that feed recommendations, dashboards and billing as events happen.
SRE and FinOps
Observability, AIOps monitoring and predictive alerting, with cloud and AI token spend measured and optimised.
Multi-cloud and edge
Workloads spread across AWS, Azure, GCP or Alibaba Cloud and the edge, without lock-in to one provider.
Subscriptions and payments
Payment gateways and subscription billing, reconciled with the ERP.
Partner APIs
Content, advertising and measurement partners connected through REST, webhooks and an API gateway.
Audience privacy
Personal data minimised, masked or tokenised, and products designed for GDPR, CCPA and the MENA personal data protection laws.
AI governance
Guardrails and algorithmic risk audits for recommendation and moderation models, in line with the EU AI Act.
Resilience
Telecom and media operators under EU NIS2 get architecture and testing built for continuity.
How we work
We build the high-concurrency back ends, mobile apps and real-time data pipelines these platforms run on, and add AI where it earns its place. Production is monitored and run by site reliability engineers, with spend measured as closely as uptime.
Measure the peak
We load-test the platform as it is and find where it breaks.
Build for scale
Back ends, apps and data pipelines are rebuilt where the measurements say they must be.
Run it
Site reliability engineers monitor production under agreed service levels.
Technologies and standards
The platforms and models media and telecom work is built on, as the documents behind this page name them.
- Mobile
- Flutter
- React Native
- AI and machine learning
- ALLaM
- Falcon
- Jais
- Data and analytics
- Apache Flink
- Apache Kafka
- Databricks
- Snowflake
- Cloud
- Alibaba Cloud
- Amazon Web Services
- Microsoft Azure
- Google Cloud
- Standards and regulations
- CCPA
- EU AI Act
- GDPR
- NIS2
Track record
Selected enterprise brands represent historical and ongoing systems, platforms and digital products architected or delivered by XSIS, KONG GROUP, our CEO and our principal engineering leadership across their careers. All third-party trademarks are the property of their respective owners and are named strictly for factual portfolio identification.
How to start
What happens next
Agreeing a time
Our team emails you to agree a time for the session.
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.
AI-native product teams
Managed, cross-functional pods of senior engineers, equipped with agentic coding and QA harnesses.
Fixed-outcome projects and round-the-clock managed services
Fixed-scope, milestone-driven delivery with named delivery leads, and round-the-clock application managed services under SLA governance.
Questions and answers
How do you make sure a platform holds up at peak audience?
We design for the peak from the start, then prove it: load and chaos tests at and beyond the expected audience, and observability that warns before users notice. In production, site reliability engineers watch the platform and respond under agreed service levels.
Can you build recommendations on our own data?
Yes. We build classification, personalisation and recommendation engines on your audience and content data, inside your environment, and measure them against the outcomes you care about.
Do your language models work in Arabic?
Yes. We fine-tune Arabic-first and regional dialect models such as Jais, Falcon and ALLaM, and host them in a private or sovereign cloud when content or audience data must stay in the country.
How do you control cloud and AI costs as the audience grows?
Spend is measured per service and per feature. Semantic caching and routing routine AI tasks to smaller models cut inference costs, and capacity follows demand.
Can you work with our existing broadcast and content systems?
Yes. We map the content, audience and billing systems you already run and connect to them through their APIs, events or files. New platforms are built around them, and a replacement is planned only when it pays for itself.
Start with a conversation, not a contract.
Tell us what you need to build, connect or secure. An architect goes through it with you, then recommends where to start.