Juicebox Reference Architecture
Overview
Production architecture for AI-powered candidate analysis integrations with Juicebox. Designed for recruiting teams needing automated dataset ingestion from job descriptions, intelligent candidate scoring and ranking, result caching for repeated searches, and seamless export to ATS platforms like Greenhouse and Lever. Key design drivers: search result freshness, candidate deduplication across sources, outreach sequencing, and analysis pipeline throughput for high-volume hiring.
Architecture Diagram
Recruiter Dashboard ──→ Search Service ──→ Cache (Redis) ──→ Juicebox API
↓ /search
Queue (Bull) ──→ Analysis Worker /profiles
↓ /outreach
ATS Export Service ──→ Greenhouse/Lever
↓
Webhook Handler ←── Juicebox Events
Service Layer
class CandidateSearchService {
constructor(private juicebox: JuiceboxClient, private cache: CacheLayer) {}
async findAndRank(criteria: SearchCriteria): Promise<RankedCandidate[]> {
const cacheKey = `search:${this.hashCriteria(criteria)}`;
const cached = await this.cache.get(cacheKey);
if (cached) return cached;
const results = await this.juicebox.search(criteria);
const ranked = results.profiles.map(p => ({ ...p, score: this.scoreCandidate(p, criteria) }))
.sort((a, b) => b.score - a.score);
await this.cache.set(cacheKey, ranked, CACHE_CONFIG.searchResults.ttl);
return ranked;
}
async exportToATS(candidates: string[], jobId: string, ats: 'greenhouse' | 'lever'): Promise<ExportResult> {
const deduped = await this.deduplicateAgainstATS(candidates, jobId, ats);
return this.juicebox.export({ profiles: deduped, destination: ats, job_id: jobId });
}
}
