The Challenge

1,000+ CVs a week, across domains no one person could fully cover.

A leading workforce platform was growing fast; and with it, the number of CVs landing in front of human reviewers each week. Manual screening had become a major operational bottleneck: time-intensive, hard to scale, and increasingly difficult to do consistently across a wide range of roles and domains.

The organisation processes more than 1,000 CVs per week, spanning many professional fields, each with its own educational backgrounds, certifications, and domain-specific requirements. In practice, it is extremely difficult for any one reviewer to have deep knowledge of all these disciplines; making consistent, high-quality assessment a real challenge.

The questions on the table:

  • How can CV screening be automated without sacrificing; or even improving; quality?
  • Can AI recognise relevant education and certifications across many domains?
  • Can the solution be integrated into existing systems without disrupting operations?
Our Approach

Two days to validate. Less than two months to deploy.

We started with a focused two-day feasibility study to assess both technical and functional viability. The outcomes were presented to the client and used as the foundation for the final implementation; built and deployed in under two months.

01 · STUDY

Analyse formats

Mapped the variety of CV formats and the role requirements they had to be measured against.

02 · STUDY

Define criteria

Worked with the client to define must-have and nice-to-have criteria per role, with clear evaluation rules.

03 · STUDY

Test on real CVs

Tested AI models on real CVs across multiple domains to validate accuracy before committing to a build.

04 · BUILD

Embed in workflow

Designed and shipped a seamless integration with the platform's existing systems; no disruption to operations.

A tool that reads CVs the way a senior reviewer would.

The deployed tool automatically evaluates every CV against predefined must-have and nice-to-have criteria. It understands education paths, certifications, and domain-specific qualifications; and not just whether requirements are met, but how well they are met. For every score, it highlights exactly where in the CV the supporting evidence was found.

CV · Candidate sample
A. Janssen
DATA ENGINEER · 7 YR
Education
MSc Computer Science, TU Delft (2018)
Cert.
AWS Solutions Architect · dbt Analytics Engineer
Stack
Python · SQL · Airflow · Snowflake
Domain
Financial services, telecoms reporting
Languages
NL (native) · EN (fluent)
AI assessment
Must
Bachelor or higher in CS / quantitative field
→ MSc Computer Science, TU Delft
5/5
Must
Production experience with workflow orchestration
→ Airflow (3 yr, current role)
5/5
Nice
dbt or analytics-engineering certification
→ dbt Analytics Engineer (2024)
4/4
Nice
Domain experience: insurance
→ Adjacent (financial services), not insurance specific
2/4
The Impact

Faster, more consistent, and; measurably; higher quality.

The tool now processes over 1,000 CVs per week with consistent and transparent results. By leveraging AI's ability to interpret qualifications across many fields, the organisation no longer depends on individual human expertise alone.

−50%
Reduction in operational effort related to CV screening
1,000+
CVs per week, scored consistently against role criteria
< 2 mo
From feasibility study to fully integrated deployment
↑ Quality
More consistent, objective evaluations across diverse domains

Why this matters.

This case demonstrates that AI is not only a tool for efficiency; it is a tool for better decision-making. When applied thoughtfully, AI can outperform manual processes in both speed and quality, especially in complex, knowledge-heavy tasks like CV assessment. At Alva Minds, we build practical AI solutions that improve how work gets done; without adding complexity.

Buried in manual review?

We design and ship AI assessment tools that improve speed and quality; embedded in the systems your teams already use.

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