Case study
Tuning Enterprise Search Relevance for Big 4 Firm
How SquareShift refactored Elasticsearch queries and unified result scoring for a Big 4 professional-services firm.
Relevance ranking breaks quietly across devices before anyone notices. The fix is refactoring the scoring logic, not bolting on another filter.
Platform & Software Engineering Practice Lead, SquareShift
One of the 'Big 4' global professional-services firms, spanning assurance, advisory, tax, and strategy consulting across 150+ countries.
Its enterprise search aggregated internal documents, third-party content, and custom search flows that needed consistent relevance and performance.
- Unified relevance — Search logic, filters, and result scoring aligned across every content source.
- Faster query parsing — Elasticsearch queries refactored to cut latency.
- Consistent experience — Search results aligned across devices and platforms.
How SquareShift delivered it.
The challenge
The firm was building an enterprise search platform to aggregate internal documents, third-party content, and custom search flows across a global professional-services business. Queries returned inconsistent results depending on the content source, semantic accuracy was weak, and results didn’t line up across devices and platforms.
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. The team improved query parsing for lower latency and aligned scoring logic with content freshness and frequency, so ranking behaved consistently regardless of source or device.
The payoff
Search logic, filters, and result scoring are now aligned across every content source the platform indexes, with query parsing tuned for lower latency. Users get a consistent, personalized search experience across devices instead of inconsistent rankings from one platform to the next.
Where this work sits