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 the context layer.
Samsung
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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Verizon
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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Tencent
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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PolyAI
High-performance customer service AI powered by RAG
PolyAI connects SurrealDB to Agent Studio for low-latency, customer-controlled RAG across voice AI experiences.
Read case study
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The context layer for AI agents. Unify data. Unlock intelligence. Scale anywhere.
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