AI recruitment platform for faster candidate evaluation

• Client type: HR and hiring teams handling growing candidate volumes and repeated screening work
• Scope: AI-assisted CV parsing, semantic job matching, automated ranking, shortlist delivery in Teams, and recruiter feedback-based recommendation improvement
• Core result: A more structured first-pass recruitment workflow, with the source deck modeling up to an 80 to 90 percent reduction in screening workload

Growing recruitment volume created several hiring bottlenecks.

The source deck describes a familiar pattern: the HR inbox fills with CVs, candidate data has to be parsed manually, and matching candidates to open roles takes hours each day. That slows down early screening and makes it harder for hiring teams to respond consistently.

The operational problems were specific:

• incoming CVs created too much repetitive manual handling
• parsing, data entry, and first-pass matching took hours each day
• strong candidates could be lost because response times were too slow
• job-to-candidate matching depended too heavily on subjective interpretation
• hiring managers had weak visibility into recruitment pipeline status

The result was slower hiring cycles, higher workload for HR teams, lower fill-rate, and a weaker candidate experience.

iQberry framed this as a decision-support and workflow problem inside recruitment rather than a simple CV search tool.

The goal was to convert unstructured candidate and role information into something recruiters can work with quickly and consistently. That meant extracting structured skills and experience data from CVs, interpreting job requirements semantically rather than by keyword alone, and delivering ranked candidate insight directly into the tools teams already use.

The source material also positions recruiter feedback as part of the design. Instead of a fixed rules engine, the recommendation logic is intended to improve over time based on how recruiters respond to the suggested matches.

This keeps AI focused on accelerating first-pass evaluation while leaving final hiring judgement with the people running the process.

iQberry designed an AI-enabled recruitment platform that transforms CVs and job descriptions into structured hiring insight.

The source deck describes a Microsoft-based operating environment using Outlook, Teams, and SharePoint, with OpenAI models providing the AI layer and Power Apps supporting workflow automation.

The proposed platform covers:

• AI extraction of skills, seniority, and relevant expertise from CVs
• semantic matching that interprets the real needs of each role
• automated ranking and shortlist creation based on role fit
• delivery of shortlists directly into Teams
• recommendation improvement informed by recruiter feedback

In practical terms, the platform is designed to reduce time spent on repetitive screening work while making first-pass hiring decisions more consistent and easier to review.

The strongest outcomes in the source material are modeled business-case figures rather than published client KPIs.

According to the deck, the platform is expected to reduce screening workload by 80 to 90 percent and create an illustrative annual productivity gain of GBP 15,000 to GBP 30,000 for a hiring flow handling about 200 CVs per month. The same material also points to reduced mis-hire cost exposure, less human error, and better knowledge retention in recruitment decisions.

Operationally, the value comes from making early hiring work more structured:

• less manual parsing and data entry
• faster first-pass matching between candidates and role requirements
• quicker shortlist delivery to recruiters and hiring managers
• clearer candidate ranking logic than ad hoc inbox review
• stronger consistency when recruitment volume increases

Because the source is a presentation deck rather than a reported delivery retrospective, these results are framed as scenario-based gains and expected operational improvements, not as verified post-implementation customer outcomes.

Recruitment slows down quickly when candidate evaluation depends on inbox triage, repeated manual parsing, and inconsistent first-pass judgement.

This case matters because it shows a practical use of AI automation in a workflow where speed, consistency, and visibility all affect business outcomes. Faster screening is useful, but the bigger advantage is giving recruiters and hiring managers more structured evidence for early decisions while protecting their time for interviews and final assessment.

By combining semantic matching, structured candidate insight, and workflow delivery into familiar Microsoft tools, iQberry shows how recruitment teams can handle higher volume without scaling manual effort at the same rate.