Case Studies | SaaS B2B

Finding the best candidates faster

CV Evaluation

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The proof of concept focused on three things.

Manual shortlisting at scale

Sorting through large numbers of CVs by hand is slow and inconsistent. The first focus was to prove that CVs and a job description could be uploaded and automatically prepared for AI evaluation.

Ranking with reasoning

A shortlist is only useful if recruiters can trust it. The second focus was to show that Amazon Bedrock could return the top candidates for a role, based on the job description, with an explanation for each choice.

Something the team could try

To judge the value, the client needed to use it themselves. The final focus was a simple front end so recruiters could run the service and see the results.

Solution

Cloud Combinator delivered the work through its AI and ML Project Accelerator, moving from understanding the problem to a working proof of concept in the client's account.

Top X

Candidates ranked per role, each with a reason for selection

500/mo

Jobs the design was costed for, each with around 1,000 applications (projected)

PoC

Proof of concept via the AI and ML Project Accelerator

By the numbers:

  • Top X - Candidates ranked per role, each with a reason for selection
  • 500/mo - Jobs the design was costed for, each with around 1,000 applications (projected)
  • PoC - Proof of concept via the AI and ML Project Accelerator
Changes

Cloud Combinator delivered a working proof of concept that met the success criteria: CVs and job descriptions uploaded and converted automatically, Amazon Bedrock returning the top candidates with an explanation for each, and a simple front end for the client to test it. The solution was deployed in the client's AWS environment with a demonstration and documentation.

  • Automated shortlisting — CVs and job descriptions are ingested and converted without manual effort, ready for AI evaluation.
  • Explainable rankings — Amazon Bedrock returns the strongest candidates for a role along with the reasoning, so recruiters can trust and act on the shortlist.
  • Recruiter-friendly — A simple Amplify front end let the client run the service and see results without technical overhead.
  • Owned and portable — Built as infrastructure as code and deployed in the client's own AWS account, ready to scale beyond the proof of concept.
  • Handover — Included a working demonstration, the required materials and documentation for the client to continue development.

With the concept proven, the client has a clear path to take AI-assisted shortlisting into production, integrating it with its recruitment workflow and scaling it across the high application volumes it handles every month.

AWS Stack

AWS Amplify

For the simple front end the client used to run and review evaluations.

AWS Lambda

For converting documents to plain text and orchestrating the evaluation.

Amazon S3

For storing uploaded CVs, job descriptions and results.

Amazon API Gateway

For upload and processing requests from the front end.

Amazon SQS

For throttled processing that keeps within service limits.

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