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Case Studies/AI Recruitment Platform Reducing Time-to-Hire from 34 to 12 Days for a 6,000-Employee Retailer
HR / RetailStratton Retail Group

AI Recruitment Platform Reducing Time-to-Hire from 34 to 12 Days for a 6,000-Employee Retailer

AI Recruitment Platform Reducing Time-to-Hire from 34 to 12 Days for a 6,000-Employee Retailer

Challenge

Stratton Retail Group hired 4,800 employees annually across 120 locations using a legacy ATS with manual CV screening. Hiring managers spent 6.2 hours per vacancy reviewing candidates, and time-to-hire averaged 34 days. During peak trading periods, vacancy backlogs exceeded 600 roles, costing an estimated £1.4M in lost sales.

Solution

We built an AI-assisted recruitment platform with automated job distribution, intelligent candidate screening, video interview workflows, and a mobile-first hiring manager application, supported by real-time pipeline analytics.

Results

Time-to-hire reduced from 34 to 12 days. CV review time reduced from 6.2 to 1.4 hours per vacancy. Candidate quality increased from 3.1 to 4.2/5. Peak vacancy backlog eliminated. Cost per hire reduced by 28%.

The Cost of Slow Hiring in Retail

Retail hiring is a throughput problem. At Stratton, demand for staff wasn’t the issue, process speed was.

Operational reality:

  • 4,800 hires annually
  • 120 retail sites
  • 600+ vacancies during peak periods
  • 34-day average time-to-hire

Financial impact:

  • ~£180/day revenue loss per vacant role
  • 600 vacancies = £108,000/day lost
  • Peak backlog = ~£1.4M lost sales

The constraint was not candidate supply. It was decision latency inside the hiring pipeline.

HR recruitment platform showing candidate pipeline with AI screening scores, interview scheduling and hiring manager workflow

System Design Approach

This was not just an ATS replacement. It was a pipeline acceleration system, designed to:

  • reduce friction for candidates
  • eliminate manual screening bottlenecks
  • compress hiring decision cycles
  • give real-time visibility across 120 sites

Platform Architecture

1. Candidate Application Layer

Designed for conversion at scale.

Key features:

  • Mobile-first, no account creation
  • CV optional (structured application alternative)
  • LinkedIn one-click apply
  • 4-minute average completion time

Result: higher application volume + lower drop-off

2. Multi-Board Distribution Engine

Single input → multi-channel output.

Integrated boards:

  • Indeed
  • Reed
  • Totaljobs
  • CV-Library
  • +10 additional boards via aggregation API

Capabilities:

  • Automated publishing
  • Performance tracking per source
  • Vacancy-level analytics

Eliminates manual posting and improves channel ROI visibility

3. AI Screening Engine

The core acceleration layer.

Function:

  • Scores candidates against role profiles
  • Evaluates:

Decisioning:

  • Above threshold → auto-progress
  • Below threshold → filtered or manual review

Governance:

  • Quarterly bias audits
  • Outcome monitoring vs hiring demographics

Result: removes 70–80% of manual screening workload

4. Video Interview Layer

Built for high-volume hiring.

Integration: Spark Hire

Workflow:

  • 3-question structured video screen
  • Asynchronous submission
  • Review before live interview

Impact:

  • 58% reduction in live interview volume
  • Faster shortlist creation
  • Standardised candidate evaluation

Hiring Manager App

A React Native application used by 340 hiring managers.

Core capabilities:

  • Push alerts for high-scoring candidates
  • CV + video review in one interface
  • One-tap shortlist / reject
  • Integrated interview scheduling
  • Offer generation from templates
  • Real-time pipeline visibility per store

Key shift:From reactive hiring → real-time decision making

Compliance & Risk Management

Enterprise hiring requires governance.

Implemented controls:

  • Right-to-work verification via Yoti
  • Full audit trail for hiring decisions
  • GDPR-compliant data lifecycle (auto deletion at 6 months)

Ensures scalability without regulatory risk

Measured Outcomes

Time-to-hireBefore: 34 daysAfter: 12 daysImpact: -65%

CV review time per vacancyBefore: 6.2 hoursAfter: 1.4 hoursImpact: -77%

Candidate quality scoreBefore: 3.1 / 5After: 4.2 / 5Impact: +35% improvement

Vacancy backlogBefore: 600+ roles during peakAfter: EliminatedImpact: full operational recovery

Cost per hireReduction: 28%

Business Impact

Before:

  • Hiring bottleneck constrained revenue
  • Manual processes slowed decision-making
  • Peak demand periods created operational stress
  • No visibility across hiring pipeline

After:

  • Hiring aligned with business demand cycles
  • Faster staffing → higher sales coverage
  • Reduced manager workload
  • Real-time control across 120 locations

Why This Worked

  1. Optimised for throughput, not just UXDesigned the system around speed of decision-making
  2. Automated the highest-friction stepCV screening replaced with intelligent filtering
  3. Distributed decision-making to the edgeHiring managers empowered via mobile app
  4. Reduced candidate frictionFaster applications → more qualified pipeline
  5. Embedded compliance into the systemScalable without introducing legal risk

The Key Insight

Hiring problems at scale are not talent problems. They are systems problems. Stratton didn’t need more recruiters. They needed a pipeline that could move at the speed of demand.

Final Outcome

The platform transformed recruitment from:

  • manual → automated
  • slow → real-time
  • reactive → demand-aligned

Result: A hiring system that scales with business growth, instead of limiting it.

Scaling Hiring Without Slowing Down?

Intagleo Systems builds recruitment platforms that combine automation, AI, and real-time analytics to help organisations hire faster, smarter, and at scale.

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