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HR / Recruiting

AI-Accelerated Talent Acquisition

A Fortune 500 technology company (15,000+ employees, 2,200 applications/week)

75% reduction in screening time, 40+ hours saved per hire

KEY RESULTS

75%
Reduction in time spent on resume screening. Recruiters now review AI-ranked shortlists, not raw applicant pools.
$1,360
Cost-per-hire, down from $4,700. Savings from efficiency + reduced agency spend.
28 days
Time-to-hire, down from 45 days. Faster screening + automated scheduling.
88%
Hiring manager satisfaction, up from 65%. Better candidates reaching interview stage.

Before & After

✗ Before

  • •Time to screen one resume: 23 seconds (cursory review)
  • •Time-to-hire: 45 days average
  • •Cost-per-hire: $4,700
  • •Recruiter time per hire: 65+ hours
  • •Hiring manager satisfaction: 65%
  • •Offer acceptance rate: 72%

✓ After

  • •Time to screen one resume: AI processes in 0.3 seconds
  • •Time-to-hire: 28 days average (38% reduction)
  • •Cost-per-hire: $1,360 (71% reduction)
  • •Recruiter time per hire: 24 hours
  • •Hiring manager satisfaction: 88%
  • •Offer acceptance rate: 84%

The Challenge

This tech company receives 2,200 applications weekly across 150+ open roles. Their 12-person recruiting team was drowning—spending 3+ hours daily on resume screening alone. The result: superficial 23-second resume scans, inconsistent evaluation criteria across recruiters, and top candidates lost to faster-moving competitors.

PAIN POINTS

  • ×Recruiters screened 180+ resumes daily—cognitively exhausting work leading to inconsistent decisions
  • ×45-day time-to-hire meant losing top candidates to competitors who moved in 2 weeks
  • ×Hiring manager satisfaction at 65%—too many unqualified candidates reaching interview stage
  • ×Recruiter turnover: 40% annually. New recruiters took 6 months to learn which candidates hiring managers actually wanted
  • ×No feedback loop: recruiters never learned which of their screened candidates ultimately succeeded in roles

Technology Stack

  • →Resume parsing engine extracting 50+ structured fields from any format
  • →Embedding model for semantic matching against job requirements
  • →Calibration system learning from hiring manager feedback
  • →Automated scheduling with calendar integration (Google, Outlook)
  • →Candidate communication automation with personalization

Implementation Approach

1

Phase 1: Resume Intelligence (Weeks 1-2)

Built parsing engine that extracts structured data from resumes regardless of format (PDF, Word, even images). Captures: skills, experience duration, company quality signals, education, certifications. Normalizes job titles ('Software Engineer' = 'SWE' = 'Developer'). Processes 2,200 resumes in 11 minutes.

2

Phase 2: Semantic Matching Model (Weeks 3-4)

Trained embedding model on 50K historical applications with outcome labels (hired, rejected at screen, rejected at interview, etc.). Model scores candidates on fit for specific roles—not just keyword matching but semantic understanding. 'Built distributed systems at Netflix' matches to 'experience with large-scale architecture' even without keyword overlap.

3

Phase 3: Calibration Loop (Weeks 5-6)

Implemented feedback system where hiring manager interview decisions train the model. When a manager rejects a candidate the AI ranked highly, system learns to adjust. After 200 feedback cycles, AI recommendations matched hiring manager preferences 89% of the time.

4

Phase 4: Scheduling & Communication Automation (Weeks 7-9)

Automated interview scheduling with multi-calendar coordination. System finds optimal times across candidate and interviewer availability. Reduced scheduling time from 4 hours per candidate to 8 minutes. Personalized candidate communications: status updates, interview prep materials, follow-ups.

Results Breakdown

75%

Reduction in time spent on resume screening. Recruiters now review AI-ranked shortlists, not raw applicant pools.

$1,360

Cost-per-hire, down from $4,700. Savings from efficiency + reduced agency spend.

28 days

Time-to-hire, down from 45 days. Faster screening + automated scheduling.

88%

Hiring manager satisfaction, up from 65%. Better candidates reaching interview stage.

Key Learnings

  • 1.Bias mitigation is non-negotiable. We audit AI decisions monthly for demographic disparities. Found and fixed issues with name-based signals in early versions.
  • 2.Calibration beats accuracy. An AI that matches hiring manager preferences is more valuable than one that predicts 'objective' quality.
  • 3.Speed is a feature. Candidates receiving same-day responses had 23% higher offer acceptance rates.
  • 4.Don't try to replace recruiter judgment—augment it. Recruiters make final calls; AI handles the grind.
  • 5.The feedback loop is the moat. After 10,000 hiring decisions, our model knows what 'good' looks like for 150+ distinct roles.

Industry Context

Hilton reduced time-to-fill by 90% using AI recruiting tools. Industry data shows AI screening can cut time-to-hire by 70% and cost-per-hire by 30%. Companies using AI report 67% improvement in hire success rates. The constraint isn't technology—it's getting hiring managers to provide consistent, timely feedback to train the models.

TIMELINE

45 days from kickoff to production

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