Case study
Consistent Search Results Across Every Source for a Big 4 Firm
How SquareShift refactored Elasticsearch queries and reworked result scoring, aligning search relevance across every content source for a Big 4 professional-services firm's enterprise search.
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Client
One of the 'Big 4' global professional-services firms, spanning assurance, advisory, tax, and strategy consulting across 150+ countries.
Industry
Professional Services
Services
Stack
Elasticsearch · Semantic Search · Query Relevance Tuning
How did SquareShift align search relevance across three content sources?
The challenge
Enterprise search returned inconsistent results depending on which content source it searched, and results didn't line up across devices and platforms. Scoring logic didn't account for how recent or how often content was used, and semantic accuracy across internal documents, third-party content, and custom search flows was weak.
What we delivered
SquareShift's engineers worked with the firm's search architects to refactor the underlying Elasticsearch queries and rework how results were scored. Query parsing was improved for lower latency, and scoring was aligned with how recent and how often content is used, so ranking stayed consistent.
The outcome
Search logic, filters, and result scoring are now aligned across every content source, giving users a consistent experience no matter which device or platform they search from. For search relevance work like this, see our AI-powered search.
Relevance tuning that only touches one content source just moves the inconsistency somewhere else. The fix had to be at the scoring layer, where every source gets judged by the same rules.
Data & Analytics Practice Lead, SquareShift
