How Query Quotient transformed AlphaSense's search infrastructure to handle millions of financial documents with sub-second response times.
AlphaSense, a leading market intelligence and search platform, was hitting serious performance bottlenecks as its corpus grew past 100 million financial documents. The existing Elasticsearch infrastructure was struggling with:
Complex financial document searches were taking 5-10 seconds, hurting user experience and productivity.
New documents took hours to become searchable, delaying time-critical financial intelligence.
Users struggled to find the right documents because scoring did not understand financial terminology.
Cluster failures during peak usage caused service disruptions and data loss concerns.
We addressed each problem with a targeted fix, validated through a performance audit and iterative A/B testing.
We redesigned the Elasticsearch cluster architecture with:
Custom analyzers and scoring built specifically for financial documents:
A high-throughput pipeline that makes documents searchable within minutes:
An engagement model built around measurement and handover:
The work delivered measurable gains in speed, reliability and scale:
“Query Quotient's expertise transformed our search capabilities. The 90% improvement in query performance has been a game-changer for our users, allowing them to find critical financial intelligence faster than ever before. Their team's deep understanding of both Elasticsearch and financial data requirements made all the difference.”
Fast, relevance-tuned retrieval over a 150M+ document corpus is exactly the foundation that RAG and AI research assistants depend on. If retrieval returns the wrong passages, no model can fix the answer.
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