01 |KEY CHALLENGES
What they needed to solve
The technical and operational hurdles the team faced before adopting SurrealDB.
02 |SOLUTIONS
How SurrealDB helped
The architecture decisions and SurrealDB capabilities that addressed each challenge.
03 |RESULTS
The impact
Measurable outcomes delivered after moving to SurrealDB.
Faster iteration
Schemaless modelling removed bottlenecks and accelerated development.
Simplified queries
Query performance improved while reducing relational overhead.
Enhanced audience targeting
Relations are easy to track and analyse for precise targeting.
Full-text search ready
Prepared to integrate full-text search across content-rich apps.
MORE CASE STUDIES
See what other teams have shipped
From knowledge graphs to AI assistants - how enterprise teams are building on SurrealDB.
Unlocking insights with knowledge graphs
Samsung Ads uses SurrealDB to build dynamic, real-time knowledge graphs for smarter campaign execution - collapsing three legacy data stores into one.
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AI assistant empowering 10,000 technicians
Verizon uses SurrealDB to power a generative AI assistant for 10,000 field technicians, delivering instant access to documentation, outage updates, and workflows.
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Unified infrastructure monitoring
Tencent consolidated nine backend tools into one real-time monitoring platform powered by SurrealDB's multi-model context graph.
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High-performance customer service AI powered by RAG
PolyAI connects SurrealDB to Agent Studio for low-latency, customer-controlled RAG across voice AI experiences.
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AI-powered personalisation at massive scale
Saks Fifth Avenue uses SurrealDB's vector search and graph capabilities to deliver real-time, AI-powered personalisation across 5 million luxury customers and 45 million monthly product-recommendation queries.
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The unified data layer for AI. Unify data. Unlock intelligence. Scale anywhere.
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