This case-studies page is a trust surface, not a vanity page. The goal is to show how a structured screening workflow performs under real applicant volume pressure, what changed operationally, and why the final human review stage still mattered.
From one public application link to a focused finalist pool
A published CipherIQ example shows how one LinkedIn-distributed application link drove more than 1,300 inbound CVs and shifted the team from manual top-of-funnel screening to a structured finalist review in days.
1,300+
Inbound applicants
225
Structured shortlist candidates
96
Finalists surfaced for live review
Day 5
Offers sent by
Problem
Hiring pressure
A high-visibility hiring push created more applicant volume than a team could review manually in a short window. The risk was not just delay; it was inconsistency, recruiter overload, and weak first-round auditability.
Why it mattered
The example matters because it shows how structured candidate screening can improve throughput and review quality without claiming that hiring decisions became fully automated.
Process
- 1. Candidates entered through one public application link.
- 2. CVs were parsed into structured candidate records for faster first-pass review.
- 3. Qualified candidates were contacted through workflow automation and moved into AI interview steps.
- 4. Interview outputs and scorecards narrowed the field before human 1-on-1 review.
Outcome
- The workflow compressed top-of-funnel screening into a tighter operational window.
- Human review concentrated on a smaller finalist pool rather than the full applicant surge.
- The recruiting team gained clearer evidence trails for shortlist decisions.
What this example tells a buyer
One case narrative can still be useful if it is honest about what it does and does not prove.
What the example shows
The featured case demonstrates how CipherIQ can help compress top-of-funnel screening work into a more structured, reviewable process.
- Large applicant spikes can be handled with a clearer workflow and shortlist record.
- AI interviews and scorecards can narrow first-round review without removing human decision ownership.
- Operational speed matters most when paired with inspectable evidence.
More examples coming
We are keeping this hub honest: one detailed public example is better than a page full of invented logos or unsupported results claims.
- Future case studies can expand by industry, role family, and hiring scenario.
- Additional examples should follow the same evidence-first style as this one.
- Until then, the workflow, scoring, and FAQ pages provide the broader operating context.
Read the supporting guides
These pages explain the workflow, the forensic interview model, and the common buyer questions behind the case study.
How CipherIQ Works
See the full hiring workflow from application intake to scored, reviewable shortlist.
What Is a Forensic AI Interview?
Understand the category, the evidence model, and how audit-ready AI interviews differ from standard video screening.
CipherIQ FAQ
Read common questions about forensic AI interviews, privacy-aware hiring, scoring, integrity, and review workflows.
Want to compare this against your own hiring flow?
The fastest next step is a live walkthrough of how CipherIQ would handle your own role volumes, screening logic, and review process.