Turning fragmented applicant data into useful intelligence
Reveal gives specialist recruitment agencies a shared view of candidates, campaigns, conversations and hiring activity. Its customers collect data through job boards, landing pages, email, social campaigns and agency-specific workflows—each with different fields and relationships.
The product’s early data model prioritised flexibility, but enterprise usage exposed expensive joins, duplicated records and slow global search. Large agencies expected to move from a candidate to every related conversation and opportunity without waiting for separate indexes to catch up.
A search-ready candidate model
Reveal redesigned its collections around a materialised candidate view in SelectiveDB. Normalised source data remains available for auditability, while the search view contains the fields, permissions and relationships recruiters use most often.
SelectiveDB combines keyword search, semantic matching and structured filters without exporting records to another search engine. Selective Cloud manages indexing capacity as agencies import new campaigns and run high-volume searches across their teams.
Enterprise search without enterprise delay
Global search became 92% faster, with P95 latency reduced to 85 milliseconds across 2.8 million candidate profiles. The platform now handles 4.2 million requests each day.
The simplified model also reduced manual source mapping by 60% and removed a class of production issues caused by overloaded relationship queries. Reveal’s engineers can ship workflow improvements without maintaining a parallel search stack.