Real-time Analytics
Syncly needed a more efficient way to run semantic analytics on voice-of-customer data as its user base and embedding volumes grew beyond what manual similarity calculations could support.
Reported impact
Syncly made similarity analytics more than 10x faster, reduced server and cloud-service pressure, and improved both service efficiency and customer insight delivery as it expanded internationally.
Syncly built its voice-of-customer analytics service around MongoDB Atlas and Atlas Vector Search so it could analyze chats, calls, meetings, and other customer-feedback signals with semantic context instead of brittle keyword-only methods. The platform collects structured and unstructured feedback, computes embeddings, and uses Atlas Vector Search to cluster that data by topic and similarity for downstream insights. Before adopting Atlas Vector Search, the team loaded embeddings into backend servers and manually computed similarity, which increased database and server load as adoption grew. By moving vector management into MongoDB Atlas, Syncly unified document and vector data on one platform and automated the indexing and query logic that previously required custom code. This reduces resource consumption while giving the startup a more scalable foundation for global expansion, faster feature delivery, and AI-driven customer-experience insights.
Customer feedback from chats, calls, meetings, and related channels is collected into MongoDB Atlas, embedded for semantic analysis, indexed with Atlas Vector Search, and then clustered and queried so Syncly can surface patterns and recommendations back to customer-facing teams.
Syncly uses AI to enable effective similarity analytics even in a mix of structured data and unstructured data such as images and videos.
“Another key benefit of Atlas Vector Search is that it is built on MongoDB Atlas, which allows us to store and manage both document and vector data in a unified platform.”
“Storing vector data in MongoDB Atlas allowed us to focus on developing the data collection and classification services that we had committed to.”
“We are experiencing more than 10 times faster performance with Atlas Vector Search index compared to manual processing.”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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