HR Venture CV Engine: Smarter Screening, Faster Shortlists
An intelligent CV screening engine that automates candidate evaluation at scale — uploading bulk CVs, parsing skills and experience with OpenAI + RAG, scoring each candidate against a custom job description and persona, and returning ranked shortlists with explainable, evidence-backed scores.
Single role, live run
Explainable AI scoring
OpenAI + retrieval
Fully automated
01 / Project Overview
What We Built & Why
HR Venture needed to screen hundreds of CVs for multiple executive roles simultaneously. Manual screening was taking weeks, introducing inconsistency, and making it impossible to apply role-specific evaluation criteria at scale.
We built a full-stack AI CV Engine that lets recruiters define a hiring persona, upload a job description, configure a custom scoring framework, and bulk upload CVs. The engine uses OpenAI + RAG to extract skills, experience, and achievements from every resume — then scores, ranks, and returns a shortlist with an Evidence Pack for each candidate.
Core problem solved: Screening 191 CVs manually for a CIO role took weeks. With CV Engine, 189 CVs were processed in 75 minutes — returning a ranked shortlist with explainable match scores, evidence points, and candidate fit reasoning ready for the recruiter to review instantly.
Persona-Driven Scoring
Recruiters define the ideal hiring persona — seniority, priorities, deal-breakers. Scoring adapts per role.
Bulk CV Upload
Upload hundreds of CVs in one go — PDF, DOCX, any format, all processed automatically.
Explainable Scoring
Every score backed by evidence from the CV — no black-box decisions, full recruiter transparency.
Ranked Shortlists
Top 10, 20, 50, or full ranked output — plus Excel export for downstream hiring workflows.
Live Application
CV Engine — Running in Production
Step 01
Upload Role + CVs Batch
Recruiters input the Company and Job Title, upload the Job Description (PDF/DOCX), optionally upload an Ideal Candidate Persona, define a Scoring Framework (XLSX/PDF), then bulk upload all candidate CVs in one batch.
Step 02
Roles Dashboard — Processing Status
The roles dashboard shows all active processing jobs — status, role, company, time taken, and progress bar. Four roles completed for Probat and Breitling — CIO (128 mins, 191 CVs), GHM (70 mins, 185 CVs).
Step 03
Ranked Results — Evidence-Backed Shortlist
CIO role for Probat — 191 total CVs, 189 processed, 2 duplicates removed. Candidate #1 Marcin Palmer ranked with 88.2% match score and a full Evidence Pack explaining exactly why he fits.
02 / What Makes It Intelligent
The AI Architecture Behind the Engine
The recruiter defines context once — persona, JD, scoring weights. The engine handles everything else: parsing, analysis, scoring, and ranked output.
RAG-Grounded Scoring
RAG ensures every score is grounded in actual CV content — no hallucinations, no guesswork. Each data point traced to source text.
Persona Alignment
The same CV scores differently for a junior vs senior role. Persona alignment means scoring always reflects what the role actually requires.
Custom Weightage
Recruiters set the scoring framework — prioritise skills over experience, or vice versa. Every role can have a completely different evaluation model.
Explainable Output
Every score has a reason. Recruiters receive an Evidence Pack per candidate — builds trust and enables confident, defensible hiring decisions.
03 / How It Works
The CV Engine Pipeline — Step by Step
Recruiter inputs hiring persona — seniority level, key priorities, and deal-breakers to guide how scoring is applied across all CVs.
Job description uploaded as evaluation context. Custom scoring criteria and weightage configured for this specific role.
Multiple CVs uploaded in one go. The engine accepts varied formats and prepares all documents for AI analysis.
OpenAI + RAG extracts skills, experience, education, and achievements from each resume regardless of formatting or structure.
Each extracted profile compared against the job description using the RAG pipeline to identify alignment and gaps per candidate.
Each CV receives a weighted score based on evidence found in the document — with clear reasoning for every data point.
Candidates ranked by score. Recruiters receive Top 10, Top 20, Top 100, or the full ranked list for review.
Recruiters receive a shortlist with scores and reasoning — enabling fast, confident, evidence-backed hiring decisions.
04 / Business Impact
What CV Engine Delivers
191 CVs screened in 75 minutes — a process that previously took hiring teams days of manual review is completed automatically before the next morning.
Zero subjective bias — every candidate scored by the same AI model against the same JD and criteria, eliminating inconsistency across reviewers.
Ranked shortlists with Evidence Packs — recruiters start interviews knowing exactly why each candidate is a fit, reducing mis-hires significantly.
Scales to any volume — the same pipeline handled CIO and GHM roles for Probat and Breitling simultaneously, with no infrastructure changes.
Custom scoring per role — finance roles can weight financial acumen, tech roles weight engineering depth, without any code changes.
05 / Tech Stack
Technologies Used
06 / Skills & Deliverables
What Was Built & Applied
Want to automate CV screening for your hiring team?
From bulk CV upload to RAG-powered scoring and ranked shortlists with Evidence Packs — we build AI hiring engines that process hundreds of candidates in hours, not weeks.