By industry

AI & Automation for Sports & Media

Q: What does a first engagement cost and how long does it take?

Two weeks to model match-day traffic and test what you have, then two to four to fix what surfaces — and that sequencing matters because the fixture list is fixed and cannot be moved for an engineering estimate. A real-time portal built from scratch is ten to fourteen weeks and should start well outside the season if there is any choice about it.

Q: How real is real-time, honestly?

Sub-two-second from feed receipt to client is achievable and worth committing to. Beating the broadcast is not, and you would not want to. The number that matters is consistency rather than the minimum — fans tolerate a two-second delay and lose trust in a feed that is sometimes instant and sometimes thirty seconds behind, so we design for a predictable budget rather than a best case.

Q: Our traffic is zero six days a week. Is cloud the right answer?

For this shape, strongly yes. A 40x peak-to-baseline ratio is close to the ideal case for elastic infrastructure, and paying for match-day capacity continuously is the most common waste we find in this sector. The important detail is that the scale-up is scheduled against the fixture list, not reactive — reactive scaling arrives after kick-off.

Q: Can you handle an unexpected traffic event?

Better than most sectors, because the baseline architecture already assumes spikes. An unscheduled surge — a transfer announcement, a controversy — still needs real headroom above the scheduled peak plus a rehearsed degradation plan. We size that explicitly rather than assuming the match-day configuration covers it.

Q: Can AI generate our match reports?

Draft them from the data feed, for an editor to review and publish. Statistical summaries and routine reports work well and the time saved is real. Anything requiring judgement about a controversial decision, an injury or a personnel story should not be generated, and readers detect generated sports writing quickly. The version that works is drafting the factual scaffold and letting a writer do the part that is actually writing.

Q: We have rights restrictions on our data. Does that constrain the build?

It constrains distribution more than architecture, and it needs designing in rather than bolting on. Territory restrictions, embargo windows and downstream redistribution limits become access-control and caching decisions — and caching is exactly where rights leaks happen, because a cached response does not re-check entitlement unless you designed it to. We treat rights as an entitlement problem at query time, the same way we treat permissions on any other data.

Every fan refreshing at once, and nobody waiting

Discuss your requirements

We design around

DPDP Act 2023
GDPR (international audiences)
Broadcast and data rights obligations
Age-appropriate design for youth audiences

< 2 sec
Live data latency to client: 40x
Match-day peak versus off-day baseline: 64%
Off-day infrastructure cost reduction

What we hear

The problems that bring people to us

If several of these describe your week, there is almost certainly something worth automating.

Traffic arrives in minutes on match day and collapses the platform
Live data reaching fans seconds behind the broadcast
Paying for match-day capacity every day of the week
Content and highlights published manually, hours late
Fan data split across ticketing, merchandise and the app

What we build

Where automation pays off in sports & media

Real-time score and statistics delivery

Live data fanned out to hundreds of thousands of clients with a latency budget stated and measured, rather than a page that fans refresh manually and mistrust.

Match-day capacity, off-day cost

Infrastructure that scales for the fixture and scales back afterwards, with the scale-up rehearsed against the fixture list rather than triggered reactively.

Edge caching and fan-out architecture

The read path designed so a million identical requests do not become a million database queries. In this sector the cache strategy is the architecture.

Content and highlight automation

Match reports, statistical summaries and clip metadata drafted from the data feed for an editor to review and publish, cutting the gap between the whistle and the post.

Unified fan record

One view of a supporter across ticketing, merchandise, app and streaming, which is the prerequisite for any sensible commercial or communication decision.

A load shape unlike anything else

Most platforms worry about growth. Sports platforms worry about Saturday.

Traffic is near zero for days, then goes up fortyfold in the minutes around kick-off, holds through the fixture, spikes again at the final whistle, and drops away. Every user wants the same handful of numbers, updated constantly, and they will refresh aggressively if the page looks stale.

That shape has one enormous advantage over retail or ticketing peaks: it is on a published calendar. You know the date, the time and roughly the interest level weeks ahead. An unprepared match day is a scheduling failure rather than an unpredictable event.

Cache strategy is the architecture

Almost every fan in the same territory wants an identical response. If a hundred thousand concurrent requests reach the application, the design has already failed.

The work is in the read path. What is cacheable and for how long, how invalidation propagates when the score changes, how to avoid a stampede when a popular entry expires mid-match, and how personalised elements are layered on without making the whole response uncacheable.

Get that right and match day is unremarkable on modest infrastructure. Get it wrong and no amount of capacity saves you, because the bottleneck is contention rather than compute.

Latency you can commit to

Fans compare your feed against a broadcast, a rival app and the person next to them. Consistency matters more than the best case.

We define the budget end to end — feed ingestion, processing, fan-out, client render — and measure each segment separately, because a delay is almost always in one place and attributing it after the fact is difficult without that instrumentation. Then we publish the number internally and alert on it, so a regression surfaces in testing rather than during a fixture.

The uncomfortable part worth stating early: you will not beat the broadcast, the feed provider is often the largest single component of the budget, and you cannot engineer around a slow provider. Where that is the constraint, the honest recommendation is a commercial conversation with them rather than an engineering project.

Off-season is where the money is

Sustained infrastructure for a peak that happens four hours a week is the biggest recoverable cost in this sector, and it is common because scaling down feels risky and nobody wants to be the person who under-provisioned.

Scheduled capacity against the fixture list solves it. Scale up ahead of kick-off with headroom, hold through the fixture, scale back afterwards, and rehearse the scale-up so it is proven rather than hoped for. That is NoOps and Infrastructure Management territory, and it typically pays for the engagement out of the saving alone.

The fan record

Most sporting organisations know who bought a ticket, who bought a shirt and who opened the app, and cannot tell that these are the same person. Every commercial decision downstream is therefore made on partial data.

Unifying that is data platform work with an identity resolution problem attached, and it is worth doing before any personalisation or sponsorship-inventory conversation, because both depend on knowing the audience rather than estimating it.

Where the work overlaps

The build is Product Engineering, the match-day operation is NoOps, and the peak-rehearsal discipline is the same one described under retail and eCommerce and events and ticketing — three sectors, one engineering problem, different calendars.

FAQ

Questions we get asked

What does a first engagement cost and how long does it take?

Two weeks to model match-day traffic and test what you have, then two to four to fix what surfaces — and that sequencing matters because the fixture list is fixed and cannot be moved for an engineering estimate. A real-time portal built from scratch is ten to fourteen weeks and should start well outside the season if there is any choice about it.

How real is real-time, honestly?

Sub-two-second from feed receipt to client is achievable and worth committing to. Beating the broadcast is not, and you would not want to. The number that matters is consistency rather than the minimum — fans tolerate a two-second delay and lose trust in a feed that is sometimes instant and sometimes thirty seconds behind, so we design for a predictable budget rather than a best case.

Our traffic is zero six days a week. Is cloud the right answer?

For this shape, strongly yes. A 40x peak-to-baseline ratio is close to the ideal case for elastic infrastructure, and paying for match-day capacity continuously is the most common waste we find in this sector. The important detail is that the scale-up is scheduled against the fixture list, not reactive — reactive scaling arrives after kick-off.

Can you handle an unexpected traffic event?

Better than most sectors, because the baseline architecture already assumes spikes. An unscheduled surge — a transfer announcement, a controversy — still needs real headroom above the scheduled peak plus a rehearsed degradation plan. We size that explicitly rather than assuming the match-day configuration covers it.

Can AI generate our match reports?