Prepare the run of show
Draft talking points, storylines, historical context, and player news for producer review.
Give your producers more to work with on matchday.
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
The product path
Use the products your workflow needs.
Start with one focused job or connect the products across the lifecycle.
What You Can Build
Workflows you can build with Machina.
Create live fan prompts
Polls, quizzes, and prompts shaped by the connected match context and the broadcaster's editorial rules.
Draft graphics copy
Draft lower thirds, statistical notes, and branded copy from the same approved sources used by the production workflow.
Prepare channel-specific social copy
Prepare pre-event, matchday, and post-match drafts for supported competitions, configured languages, and selected channels.
AI agents for broadcasters connect approved sports sources, editorial rules, and permitted models to produce match-day drafts such as run sheets, previews, recaps, polls, graphics copy, and social posts. Their value is the complete operating workflow — triggers, grounding, review, and delivery — rather than a claim that general AI tools cannot read data or use tools.
Why broadcasters automate content in the first place
A rights deal is priced on a calendar, and the calendar does not care that the same crew covers a Tuesday fixture and a Saturday derby, or that a second language doubles the desk. Coverage depth per fixture ends up being a staffing decision — and most of the calendar loses it. The long tail gets a scoreline and nothing else.
Automation moves that constraint. When a preview, a recap and a set of social cuts exist for every fixture, the question stops being which matches get covered and becomes which ones get human attention on top.
What a broadcast agent actually produces
Output is generated from the same grounded source the graphics and the script read, which is what keeps the number on screen and the number in the voiceover identical.
- Match previews and recaps, written when the lineup is confirmed and rewritten when it changes.
- Run sheets and rundown prep: talking points, stat blocks and storylines per fixture.
- Live fan engagement during the broadcast — polls, quizzes and second-screen prompts tied to what just happened.
- Graphics and lower-third copy drawn from the same facts as the script.
- Per-platform social copy and cutdown text, in every language you broadcast in.
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.
Last updated: 2026-07-29
Don't just take our word for it
Ask AI about broadcast automation
“How can broadcasters cover more events without adding headcount?”
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.
+2
Personalized Sports Podcast
A preference-aware sports briefing that moves from approved sources to a reviewed script and optional audio output.
+1
Shareable Stats Generator
Shareable, source-backed statistical story ideas formatted for the channels an editorial team selects.
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?
Strong general AI systems can read feeds and use tools. Machina focuses on integrating the complete sports workflow: typed sports context, approved sources, operating rules, evaluation, review, delivery, and an execution record. The difference is operational, not a claim about what a model can read.
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.
Bring a real matchday workflow.
See how your sources, editorial rules, and delivery channels could fit together.