Real-time predictive sports analytics

GameScript fuses AI predictions with human analysis using SurrealDB's multi-model engine for high-scale ingestion, low-latency queries, and personalised streams.

IndustryMedia and entertainment

GoalReal-time sports analytics

01 |KEY CHALLENGES

What they needed to solve

The technical and operational hurdles the team faced before adopting SurrealDB.

Rapidly shifting odds

Sports data changes moment by moment, requiring a dynamic data model.

Millions of concurrent events

The platform ingests massive write volumes during live games.

Time-sensitive annotations

AI predictions and analyst insights must appear instantly in one view.

Polyglot overhead

Multiple databases would slow development and increase complexity.

02 |SOLUTIONS

How SurrealDB helped

The architecture decisions and SurrealDB capabilities that addressed each challenge.

Unified multi-model power

SurrealDB models users, AI insights, and annotations without multiple systems.

Advanced querying and real-time features

SurrealQL blends joins, graph traversals, and live queries for instant content.

Developer velocity and simplicity

Studio and the embeddable Rust binary accelerated prototyping and rollout.

Integrated ML and scalable performance

In-database inference powers sub-100 ms latency at massive scale.

03 |RESULTS

The impact

Measurable outcomes delivered after moving to SurrealDB.

↑ SPEED

Lightning-fast launch

Delivered a production-grade platform in record time.

<100ms

Real-time at scale

Handles millions of writes with sub-100 ms latency.

UNIFIED

Single system simplicity

Eliminated separate vector, document, and graph stores.

↑ AGILITY

Flexible schema deployments

Rapid rollout of new sports markets with minimal overhead.

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