---

name: juicebox-performance-tuning description: 'Optimize Juicebox performance.

Trigger: "juicebox performance", "optimize juicebox".

' allowed-tools: Read, Write, Edit, Grep version: 1.16.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• recruiting
• juicebox compatibility: Designed for Claude Code

---

Juicebox Performance Tuning

Overview

Juicebox's AI analysis API handles dataset uploads, analysis queue wait times, and result pagination. Large dataset uploads (100K+ rows) can block the analysis pipeline, while queue contention during peak hours increases wait times. Result sets from broad queries return thousands of profiles requiring efficient pagination. Caching search results, batching enrichment calls, and managing upload chunking reduces end-to-end analysis time by 40-60% and keeps interactive searches responsive.

Caching Strategy

const cache = new Map<string, { data: any; expiry: number }>();
const TTL = { search: 300_000, profile: 600_000, analysis: 900_000 };

async function cached(key: string, ttlKey: keyof typeof TTL, fn: () => Promise<any>) {
  const entry = cache.get(key);
  if (entry && entry.expiry > Date.now()) return entry.data;
  const data = await fn();
  cache.set(key, { data, expiry: Date.now() + TTL[ttlKey] });
  return data;
}
// Analysis results are expensive — cache 15 min. Searches expire at 5 min.

Batch Operations

async function enrichBatch(client: any, profileIds: string[], batchSize = 50) {
  const results = [];
  for (let i = 0; i < profileIds.length; i += batchSize) {
    const batch = profileIds.slice(i, i + batchSize);
    const res = await client.enrichBatch({ profile_ids: batch, fields: ['skills_map', 'contact'] });
    results.push(...res.profiles);
    if (i + batchSize < profileIds.length) await new Promise(r => setTimeout(r, 300));
  }
  return results;
}

Connection Pooling

import { Agent } from 'https';
const agent = new Agent({ keepAlive: true, maxSockets: 8, maxFreeSockets: 4, timeout: 60_000 });
// Longer timeout for dataset uploads and analysis queue responses

Rate Limit Management

async function withRateLimit(fn: () => Promise<any>): Promise<any> {
  try { return await fn(); }
  catch (err: any) {
    if (err.status === 429) {
      const backoff = parseInt(err.headers?.['retry-after'] || '10') * 1000;
      await new Promise(r => setTimeout(r, backoff));
      return fn();
    }
    throw err;
  }
}

Monitoring

const metrics = { searches: 0, enrichments: 0, cacheHits: 0, queueWaitMs: 0, errors: 0 };
function track(op: 'search' | 'enrich', startMs: number, cached: boolean) {
  metrics[op === 'search' ? 'searches' : 'enrichments']++;
  metrics.queueWaitMs += Date.now() - startMs;
  if (cached) metrics.cacheHits++;
}

Performance Checklist

• Use specific filters (location, skills, title) to narrow search scope
• Cache search results with 5-min TTL to avoid redundant queries
• Batch profile enrichment in groups of 50 with 300ms delays
• Chunk large dataset uploads into 10K-row segments
• Cache analysis results for 15 min (expensive to recompute)
• Set 60s timeout for upload and analysis endpoints
• Monitor queue wait times and schedule uploads during off-peak
• Paginate results with limit=20 and cursor for interactive UIs

Error Handling

Issue	Cause	Fix
Analysis queue timeout	Peak hour contention	Schedule large analyses off-peak, increase client timeout
429 on bulk enrichment	Too many concurrent enrichment calls	Batch to 50 profiles with 300ms interval
Upload failure on large dataset	Payload exceeds limit or connection drop	Chunk into 10K-row segments, retry failed chunks
Slow broad search	Unfiltered query returning thousands of results	Add location/skills/title filters, set limit=20

Prerequisites

• An approved performance baseline, synthetic sandbox fixture, bounded test budget, source/destination allowlists, suppression controls, and a rollback owner.

Instructions

1. Benchmark cache, batching, and pagination changes against synthetic fixtures only; reject live contact export and unapproved destinations.
2. Collect aggregate latency, error, and quota measurements; verify suppression, data minimization, and contacts_exported=0 before comparison.
3. Run one bounded canary, halt on scope, policy, quota, or retention drift, and restore the prior tuning configuration if it fails.
4. Keep only the redacted benchmark receipt and delete test artifacts after the approved window.

Output

Produce a performance receipt with environment, baseline and aggregate measurements, tuning settings, suppression/no-export assertions, canary outcome, owner approval, retention/deletion proof, and rollback reference. Exclude queries, records, and credentials.

Examples

env=staging; fixture=synthetic; p95_delta=-22%; quota=within-budget; suppression=pass; contacts_exported=0; rollback=available supports an approval decision.

Resources

• Juicebox API Docs
• Juicebox Performance Guide

Next Steps

See juicebox-reference-architecture.