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The execution layer for live sports broadcast

Turn connected match data and approved editorial sources into talking points, storylines, graphics copy, and social drafts. Producers review and refine the work while keeping control of what reaches air.

  • Match and lineup data
  • News and editorial sources
  • Owned video and audio
  • Brand and production rules
Explore with Factory

The story, ready for your production desk.

A Machina producer recap, using match data from the Sports Skills data SDK.

Put the story in your producer's hands.

No match recap is available here right now.

Explore Sports Skills

The product path

Use the products your workflow needs.

Start with one focused job or connect the products across the lifecycle.

  1. 01

    Machina Factory

    Shape the app or agent.

    Explore →
  2. 02

    Machina Studio

    Run projects and workflows.

    Explore →
  3. 03

    Machina Arena

    Compare configurations.

    Explore →
  4. 04

    Machina AI Router

    Serve requests through configured profiles.

    Explore →

What you can build

Workflows you can build with Machina.

  1. 01

    Run of Show Automation

    Complete broadcast prep, ready 7 days before every event. Talking points, storylines, historical context, player news. Your producers review and refine instead of research from scratch.

  2. 02

    Live Fan Engagement

    Polls, quizzes, and predictions that respond to match context. Not generic questions pulled from a library. Content that makes sense for what's happening right now.

  3. 03

    Graphics and Overlays

    Lower thirds, statistics, and branded content that update automatically. Your graphics team designs once, the system populates dynamically.

  4. 04

    Social Content at Scale

    Every competition, every platform, every match. Pre-event hype, live moments, post-match highlights. Scheduled and ready, with your brand voice.

  5. 05

    Chat Intelligence

    Know what your viewers are talking about in real-time. Surface trending topics for on-air talent. Identify issues before they escalate.

Machina is the governed runtime that connects live sports data → canonical context → agent workflow → approved destination → measured outcome → correction lineage. Not a chatbot. Not a content farm. Not a graphics tool.

Hot path / Cold path: the execution split

A broadcast deployment lives or dies on the split between what happens live and what happens in preparation. Most vendors collapse this; Machina makes it the architecture.

The hot path is the match window. Canonical event resolution in under 100ms. Policy-gated action with full trace. Source-cited output, reversible. Operator approval stays in the loop.

  • Hot path (live): event resolution <100ms, policy-gated actions, traced output, operator in loop
  • Cold path (pre/post): Run of Show ready 7 days out, multi-language batch, social calendar queued, rights and provenance baked in

Rights & compliance built in

Works with existing broadcast deals, not against them. Geo-blocking, DRM, VPN blocking, and concurrency control are enforced at platform level — per match, per territory, in code, not policy.

Registration walls, hotlink prevention, and domain pinning ship as configurable defaults, not after-the-fact add-ons.

  • Geo-blocking and DRM enforced per territory at platform level
  • Per-match, per-territory access rules in code
  • Registration walls, hotlink prevention, domain pinning
  • Complements existing broadcast arrangements

What decides whether it survives a live gallery

Generation quality is rarely what kills a broadcast deployment. Four operational constraints do, and each one is a build decision rather than a model decision.

  • Approval that fits the clock: review has to complete between lineup confirmation and kickoff, not after it.
  • One source of truth behind every surface — separate lookups for script, graphic and post drift, and the contradiction goes to air.
  • Brand voice that holds at volume: a tone that survives ten pieces and collapses at a thousand is a sample, not a tone.
  • A defined behaviour when data is missing. Lineups arrive late and feeds fail; the system needs a stated fallback instead of a model filling the gap from memory.

How to scope a first deployment

Pick one competition, one output type and one language, and run it in parallel with the existing process for a few match weeks. That gives you a comparison against something real rather than against a memory of how long things used to take.

Two numbers tell you where to widen: time from data availability to publishable draft, and the share of drafts that ship with no edit. They are honest in a way a subjective quality review is not — and they point at whether to expand by competition, by output type or by language.

Full guideAI Content Automation for Broadcasters: What to Automate FirstThe production constraints that decide whether a deployment survives a live gallery, and how to scope the first one.

Last updated: 2026-09-09

Don't just take our word for it

Ask AI about broadcast automation

“How can broadcasters cover more events without adding headcount?”

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Starting points for this workflow

Review example outcomes, inputs, and workflow steps, then adapt one to your sources and rules.

Matchday Studio

A reviewable matchday content set for production teams, from briefing notes to post-match drafts.

sportradar
heygen
machina
+2

Personalized Sports Podcast

A preference-aware sports briefing that moves from approved sources to a reviewed script and optional audio output.

sportradar
machina
elevenlabs
+1

Shareable Stats Generator

Shareable, source-backed statistical story ideas formatted for the channels an editorial team selects.

sportradar
machina
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Frequently Asked Questions

What is AI content automation for broadcasters?

It is the use of AI agents to produce match-day content — previews, recaps, run sheets, live polls, graphics copy and social posts — from live sports data, under editorial approval. Output is generated per fixture rather than written by hand only for the matches a desk has time to cover.

What should a broadcaster automate first?

Start with one output type, competition, and language. Evaluate output quality, approval effort, destination confirmation, and audience response over a window your team defines before expanding.

Does automated broadcast content still get editorial review?

It should, and the approval step has to fit the production clock. A review that only completes after kickoff is not a workflow. The common pattern is human approval on anything going to air, with lower-risk surfaces such as long-tail recaps moving to spot checks over time.

How does this differ from a generic AI writing tool?

Machina connects approved sports data, content creation, team review, and delivery in one workflow. Producers can prepare source-grounded drafts, review them with their team, and deliver approved content through configured channels.

Can it produce content in several languages?

The same output can be localized per market. Each language requires its own configuration, brand rules, and review process.

What breaks most often in production?

Missing or late data. Lineups land minutes before kickoff and feeds fail mid-match. Deployments that hold up are the ones with a defined behaviour for a gap — hold, degrade to a shorter format, or flag a human — rather than a model inventing the missing half.

How does Machina handle rights and compliance?

Geo-blocking, DRM, VPN blocking, and concurrency control are enforced at platform level per match and territory. Registration walls, hotlink prevention, and domain pinning are configurable defaults. The platform works with existing broadcast deals, not against them.

Bring a real matchday workflow.

See how your sources, editorial rules, and delivery channels could fit together.

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