AI ResumeScreening
Turn a pile of unstructured CVs into a ranked, explained shortlist, with a recruiter making every decision about who moves forward.
AI Resume Screening: what the work involves
A single job post in Pakistan can attract hundreds of applications within days, arriving as PDFs, Word files, phone photos of printed pages and WhatsApp forwards. Recruiters skim each one for ten seconds, tire by the fortieth, and quietly favour familiar universities or formatting. Good candidates with unusual layouts get missed, and the hiring manager never learns who was skipped.
We extract text from each file with a layout-aware parser, falling back to OCR for scans, then turn it into structured fields: roles, tenure, skills, education, certifications. A language model compares those fields with the requirements you define, and returns a score together with the exact CV lines that justify it. Protected attributes such as name, photo, age and gender are stripped before scoring. Scores only sort the queue; the recruiter opens every profile and can override any rank. We test the matcher on past hires you already trust before it sees a live vacancy.
Core features
Multi-format CV intake
PDF, DOCX, image and emailed attachments are collected from an inbox or form and converted to clean text, with scans passed through OCR.
Structured profile extraction
Every CV becomes a record of roles, dates, skills and education that you can search and filter in a table, not just read.
Requirement-based matching
You define must-have and nice-to-have criteria per vacancy, and each candidate is compared against them one by one.
Evidence beside every score
The reviewer sees the lines in the CV that support each match or gap, so a number is never accepted on trust.
Anonymised first pass
Names, photos, gender and age are hidden from the scoring step, which narrows one common route for unfair filtering.
ATS and sheet export
Shortlists push to your applicant tracker, Google Sheets or email, with the candidate status kept in sync.
What we get right before launch
Bias in the ranking
Models inherit patterns from past hiring. We exclude protected fields, audit score distributions across groups, and never let the system reject anyone automatically.
Candidate data handling
CVs hold phone numbers, addresses and family details. We agree retention periods, restrict access by role, and avoid sending identifiers to external model APIs where we can.
Unusual but strong profiles
Career changers and self-taught engineers rarely fit keyword logic. We keep a visible low-match tab so reviewers can rescue promising people the model underrated.
Tools and technology
- OpenAI
- Anthropic Claude
- Python
- FastAPI
- Tesseract
- Google Document AI
- PostgreSQL
- n8n
Common questions, answered
Does the system reject candidates by itself?
No. It orders and explains, nothing more. A recruiter opens the profiles and decides who proceeds, and every candidate, including low-ranked ones, stays visible and searchable in the queue.
How do you reduce bias in scoring?
We hide names, photos, age and gender from the scoring step, define criteria from the job rather than from past hires, and review score patterns on sample batches. It lowers risk but cannot remove it, so human review remains.
Can it read scanned or photographed CVs?
Yes, through OCR, though quality depends on the image. Blurry or skewed photos are flagged for manual reading instead of being scored on half-extracted text.
Can it connect to our existing applicant tracking tool?
Usually, if the tool exposes an API or webhooks. Otherwise we sync through email parsing or a shared spreadsheet, and we confirm the exact route during scoping.
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Ready to start your AI Resume Screening project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.