Case Study

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Automating Candidate Screening: 60% Faster Hiring for a Fortune 500 Retailer

The Fortune 500 Retailer's recruiters were spending most of their day on resume reviews, qualification cross-checks, and interview scheduling. RTS Labs built an AI system designed around the recruiter — automating screening with clear, explainable recommendations and handling interview coordination autonomously. The result: 60% faster screening, 75% less scheduling overhead, and a 15% lift in interview-to-hire rates.

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Case Study at a Glance
Client

Fortune 500 Retailer

Industry

Use Case

Hiring Workflow Automation

Tech Stack

Python / FastAPI

LangGraph (agent orchestration)

Azure OpenAI (GPT-4o)

Greenhouse + Microsoft Graph APIs

Time to Production
From brief to live deployment
0 weeks
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1. The Challenge

In high-volume retail, hiring speed and quality determine success. The retailer’s recruiters were spending most of their day buried in administrative work — reviewing resumes, cross-checking qualifications, and juggling endless interview scheduling. The manual process slowed hiring and made it harder to engage top talent before competitors did.

The team didn’t need a more elaborate ATS or another dashboard. They needed the administrative work to disappear so they could spend their time where it matters: in conversation with candidates.

Admin Drag on the Recruiter Day

Recruiters spent most of their working hours on resume reviews and qualification cross-checks — administrative steps that pulled them away from candidate conversations and slowed the entire pipeline.

Per recruiter, every week, on admin
0 hrs

Losing Top Talent to Faster Competitors

By the time the retailer’s manual screening reached a strong candidate, faster competitors had already engaged. The cost wasn’t unfilled roles — it was the wrong people in the right roles, because the best ones moved on.

From application to first interview
0 days

Manual Scheduling Overhead

Interview coordination consumed hours of recruiter time per role — back-and-forth emails, calendar Tetris across hiring managers, and reschedules that should have been instant.

Of recruiter time spent on scheduling
0 %

2. The Engineer Approach

RTS Labs worked alongside the client to reimagine the recruiter’s workflow. Instead of layering in technology for its own sake, we built an AI system designed around people — the recruiters themselves. The solution automatically reviews every application, highlights the top matches, and provides clear explanations for each recommendation. In the next phase, an AI scheduling assistant handled interviews and calendar coordination seamlessly.

  • Data Ingestion & Application Processing

    Every application flowed into the system through a direct integration with the client's applicant tracking system. Incoming resumes — PDFs, Word files, and raw ATS fields — were parsed into a consistent candidate record, with skills, experience, and education normalized into a structured schema. A preprocessing layer de-duplicated repeat applicants, set aside details not relevant to screening, and flagged incomplete records before anything reached the agent, so the model only ever evaluated clean, comparable data.

  • Screening Agent & Explainability Layer

    At the core was an LLM-driven screening agent built on a LangGraph orchestration flow. For each role, the agent compared a candidate's normalized profile against the job's must-have and nice-to-have requirements, producing a match score and a ranked shortlist. Rather than return an opaque number, every recommendation came with a plain-language explanation — which requirements were met, which were partially met, and the evidence from the resume behind each judgment — so recruiters could trust and defend the ranking.

  • Guardrails & Human-in-the-Loop

    The agent was designed to assist, not decide. Recommendations below a defined confidence threshold were routed for mandatory recruiter review, and recruiters could override any score with a logged reason that fed back into quality monitoring. Every decision was captured in an audit trail, and the team monitored outcomes across candidate groups to watch for adverse impact — keeping a human firmly in control of who advanced.

  • AI Scheduling Assistant & Production Rollout

    Once screening was stable, a second phase added an AI scheduling assistant that connected to recruiter and hiring-manager calendars, proposed interview slots, and handled candidate confirmations and reschedules automatically. The full system was deployed behind the client's existing ATS, with monitoring on throughput, latency, and model behavior. RTS rolled it out role by role — starting with high-volume requisitions — so the team could validate results before expanding across the pipeline.

The biggest decision was making the agent explain itself. We could have shipped a black-box score, but recruiters won't act on a ranking they can't see into — so we built the explainability layer first and the scoring around it. That's what turned the model from a curiosity into a tool the team used every day.
Solution Lead, RTS Labs

3. Results & Impact

Faster Candidate Screening
0 %
Better Interview-to-Hire Rate
0 %
Less Scheduling Overhead
0 %
From brief to live deployment
0 wks

Before RTS Labs

  • Admin Drag

    Recruiters spent most of the day on resume reviews and qualification checks

  • Manual Screening

    Manual cross-checking of qualifications, candidate by candidate

  • Inbox Overload

    Interview scheduling juggled across recruiter inboxes

  • Lost Candidates

    Top talent often signed with competitors before the retailer could engage

After RTS Labs

  • Strategic Hiring

    Recruiters focus on candidate conversations, not inbox management

  • AI-Powered Vetting

    Every application is auto-reviewed with a clear, explainable recommendation

  • Hands-Free Coordination

    An AI scheduling assistant handles interview coordination end-to-end

  • Measurable ROI

    60% faster screening, 75% less scheduling overhead, 15% better interview-to-hire

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