Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
A B2B sales intelligence platform manages a GDPR-compliant database of over 200 million business contacts serving 1.5 million users.
The company sought to evolve from a static data provider into a smart assistant, but its legacy search infrastructure struggled with growing data...
“Prospect Playlists eliminate the need for manual lead searching by continuously surfacing high-value contacts and companies that match your targeting criteria. You can create multiple playlists for different ideal customer profiles (ICPs), industries, or sales motions, ensuring each list stays relevant and filled with the right prospects.”
B2B sales intelligence platform for contact discovery and lead enrichment.
Search AI platform for enterprise search, observability, and security solutions.
Lusha's Sales lead recommendations is part of this use case:
Related implementations across industries and use cases
Sifting 400M records stalled sales. AI agents now query 40 sources instantly to push prioritized leads directly into CRM.
Single-source data gaps stalled global staffing. AI agents now aggregate 10+ providers to map the market in 2 weeks, not 2 months.
Data silos across 300+ products made navigating fiscal regulations slow. A unified AI assistant now securely answers complex user queries.
Processing trillion-token datasets took months. A native vector engine cut deduplication costs 5x and doubled processing speed.
Adding one attribute to a 300M-item catalog took days of rebuilds. Unifying vector and product data unlocked real-time hybrid search.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
A B2B sales intelligence platform manages a GDPR-compliant database of over 200 million business contacts serving 1.5 million users.
The company sought to evolve from a static data provider into a smart assistant, but its legacy search infrastructure struggled with growing data...
“Prospect Playlists eliminate the need for manual lead searching by continuously surfacing high-value contacts and companies that match your targeting criteria. You can create multiple playlists for different ideal customer profiles (ICPs), industries, or sales motions, ensuring each list stays relevant and filled with the right prospects.”
B2B sales intelligence platform for contact discovery and lead enrichment.
Search AI platform for enterprise search, observability, and security solutions.
Lusha's Sales lead recommendations is part of this use case:
Related implementations across industries and use cases
Sifting 400M records stalled sales. AI agents now query 40 sources instantly to push prioritized leads directly into CRM.
Single-source data gaps stalled global staffing. AI agents now aggregate 10+ providers to map the market in 2 weeks, not 2 months.
Data silos across 300+ products made navigating fiscal regulations slow. A unified AI assistant now securely answers complex user queries.
Processing trillion-token datasets took months. A native vector engine cut deduplication costs 5x and doubled processing speed.
Adding one attribute to a 300M-item catalog took days of rebuilds. Unifying vector and product data unlocked real-time hybrid search.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.