How accurate is the AI resume fit score?

It is directionally accurate, not a hiring decision. Treat it as a triage signal: a high score (75+) means definitely worth a human read, a low score (under 40) usually means skip unless something in the gaps catches your eye. The summary and gap analysis are typically more useful than the number itself.

Can I use this AI resume screener with real candidate resumes?

Yes, confidently. This tool has no database. The job description and resume are sent straight to the AI for scoring, the result comes back to your screen, and nothing is written to our disk. We do not log the job description, the resume text, the candidate name, or the result. If your team needs a fully self-hosted version that runs on your own infrastructure, Entexis delivers that as a custom build.

What resume file formats does the AI screener accept?

PDF, Word document (.docx), and plain text files up to 5 MB. The PDF should have real text in it (you can tell because you can highlight and copy from it). Scanned PDFs that are just images will not work. If your upload does not go through, switch to the "Paste text" tab and drop the resume content directly into the form.

Can Entexis build a custom AI resume screener for our team?

Yes. Entexis uses this exact tool internally to screen candidates for our own roles, so it is not a vapour demo. For a client build the process is: spend a day with your hiring team to understand how you actually evaluate candidates, write the scoring rubric and prompt around that, launch a private beta once scope is agreed, and tune from real screenings. The screener lives on your stack and you own the code.

How long does it take to build a custom AI resume screener?

It depends on what you need end-to-end: deeper integrations, multiple job families, and custom scoring rubrics all add scope. The basic version of what you see on this page, tuned to your roles, your rubric, and hosted on your stack, typically goes live once scope is agreed. From there we layer on whatever else your team actually uses.

Does the AI resume screener work for non-English job descriptions and resumes?

Yes. The underlying model is multilingual. Job descriptions and resumes in Spanish, French, German, Hindi, Portuguese, Arabic, and most major European and Asian languages produce equally sharp output. The verdict, strengths, gaps, and interview questions come back in the same language as the job description. Mixed-language pairs (e.g. an English job description with a Hindi resume) work. Output defaults to English in that case.

Will my job description or candidate resume content be used to train AI models?

No. We use a leading enterprise-grade AI partner whose policy is that content sent through their interface is not used to train their models. They keep brief logs purely for abuse monitoring, then delete them. For a custom build, Entexis delivers with whichever AI partner your team prefers: including options with zero data retention, or a fully self-hosted private model so content never leaves your environment.

Can I customize the AI scoring rubric to match our hiring bar?

For a custom build, yes. That is the core of the work. Entexis sits with your hiring team, studies how you actually evaluate candidates (must-haves, dealbreakers, nice-to-haves, seniority bands), and writes a scoring rubric that matches. The result is consistent screening that reflects your team's standards, not a generic "good resume" detector.

What roles does the AI resume screener work best for?

It works best for roles where the job description describes specific skills and seniority: engineering, data, product, design, marketing, sales, recruiting, customer success. It is less useful for roles where the job description is intentionally vague or where fit is mostly about leadership presence and chemistry (executive search, senior partnerships). Those still need a human read first.

How does the AI resume screener handle bias and fairness?

The screener follows the job description you give it. So whether it is fair depends on how the job description is written. Entexis helps clients focus on what people can actually do (like years of real experience) instead of where they studied. Things like age, gender, race, and religion are never used to score a candidate. No AI tool can fully remove bias though, so we treat this as a quick first-pass filter, not a replacement for a human read.

Free AI Resume Screener: Score Any Resume vs Any Job

Where this leads

What AI-integrated hiring looks like in practice.

The outcomes teams see once screening lives inside an AI layer wired to their rubric. Exact numbers vary by role family, but the shape holds.

5-8 sec

Per resume, every time

Score, strengths, gaps, and interview questions in one pass. No clicking through tabs.

10+ languages

Multilingual out of the box (English, Hindi, Spanish, French & more)

Job descriptions and resumes in any major language. Works on mixed-language pairs too.

10-40 hrs

Saved per week, per recruiter

Triage hundreds of resumes in minutes. Recruiters focus on the shortlist, not the slush pile.

Your rubric

Tuned to your hiring bar

Must-haves, nice-to-haves, dealbreakers, seniority bands: codified once, applied consistently.

1 slice

Where every build starts

Basic version tuned to one role family goes live once scope is agreed. Then we layer on whatever else your team uses.

Under the hood

The integration layer that makes AI usable on real resumes.

Every component below is what a production hiring-AI build needs. The model is a commodity; the value is in the pipeline wrapped around it.

PDF / DOCX parsing

Drop in the resume the candidate sent. We extract the text and feed it straight to the model. No copy-paste needed.

Instant fit score

0–100 score that weighs must-haves vs nice-to-haves. Color-coded so you can triage a stack of resumes in seconds.

Strengths, made specific

Cites named technologies, real years of experience, and delivered projects from the resume, not generic praise.

Gaps and risks, called out

Honest weaknesses and red flags vs the role's bar: seniority mismatches, missing must-have skills, project-shape risk.

Sharp interview questions

Three questions you can paste into the screening loop, designed to probe gaps and pressure-test claimed strengths.

API or in-product

Run as a standalone tool, or plug it into your existing hiring system as a first-pass step before a human ever opens the file.

Multilingual (English, Hindi, Spanish, French & more)

Job description and resume can be in any major language: Hindi, Spanish, French, German, Arabic, Mandarin, Portuguese. Mixed pairs (English JD + Hindi resume) work too. Verdict comes back in the JD's language.

The build path

4 steps from a JD and a resume
to a structured hiring signal.

The pipeline the demo above runs on. The same pipeline scales into a production build tuned to your rubric and wired to your ATS.

Paste the job description

The whole role description: responsibilities, must-haves, nice-to-haves, seniority. The richer, the better.

Drop the resume

PDF, DOCX, or pasted text. Up to 5 MB. We pull the text out automatically.

AI scores it

The model assesses fit on must-haves and nice-to-haves, then writes specific strengths, gaps, and questions.

You decide

A glance at the fit score and gaps tells you whether to advance, reject, or send for a deeper read.

Frequently Asked Questions

Is this a product we can subscribe to?

No. What you see here is a live demo of an AI hiring layer. A production build is delivered to you as software you own, running on your stack. Same building blocks, tuned to your rubric, integrated into your ATS. No monthly seat fees to a vendor.

How reliable is the fit score?

Directional, not decisive. 75+ means the candidate clears the JD bar and is worth a real read. Under 40 means the JD's must-haves are not there. The verdict paragraph and the gap list are usually more useful than the number itself for a recruiter deciding next steps.

Is it safe to run real candidate data through the demo?

The demo has no database and does not log the JD, resume text, candidate name, or output. Content is passed to the AI vendor over their enterprise API (no training use), the result comes back to your browser, and it ends there. For production, a real build can run under zero-retention terms with a commercial vendor or on a self-hosted open model inside your VPC, depending on how strict your compliance bar is.

Which resume formats work?

PDF, DOCX, and plain text up to 5 MB. The PDF must have real selectable text (not a scan). If a scanned PDF fails, switch to the paste-text option. Production builds add OCR for scanned resumes and can ingest attachments straight from Gmail, Google Drive, or your ATS.

What does a real hiring-AI integration look like?

The AI slots in as a first-pass step inside your existing hiring pipeline. Candidate applies to a role in your ATS (Greenhouse, Lever, Ashby, Workable, Zoho Recruit, Bamboo). The AI reads the JD + attached resume, writes fit score + strengths + gaps + suggested questions back onto the candidate record. Recruiter sees it in their normal tool. No new dashboard, no new login.

How long until it goes live?

A first working slice, tuned to one role family and one ATS, typically goes live once scope is agreed. From there, additional role families, deeper rubric logic, take-home graders, and recruiter copilots layer on once you have proof from real screenings.

Does it work outside English?

Yes. English variants (US, UK, Indian), Hindi, Spanish, French, German, Portuguese, Arabic, Mandarin, and most major European and Asian languages produce sharp output. Verdict, strengths, gaps, and interview questions come back in the JD's language so recruiters read them natively.

What if the JD and resume are in different languages?

Common and handled natively. English JD + Hindi resume, Spanish JD + English resume, Arabic JD + French resume: the model reads each document in its own language, then writes the assessment in the JD's language. Useful when you hire across regions where candidate resumes rarely match the JD's language.

Where does the data go?

For the demo: to the AI vendor's enterprise API (no training use, brief abuse logs then deleted) and back to your browser. Nothing lands in our DB. For a production build you pick: enterprise API, the same vendor under a zero-retention contract, or a self-hosted open-weights model running fully inside your infrastructure so resumes never leave your VPC.

Can we tune the rubric ourselves?

Yes. The rubric is configuration, not code. Must-haves, dealbreakers, nice-to-haves, seniority bands: your recruiters edit them from an admin panel. When your hiring bar shifts or you open a new role family, the logic updates without a rebuild.

Which roles benefit most?

Roles where the JD lists concrete skills and a clear seniority: engineering, data, product, design, marketing, sales, recruiting, customer success, support. Less useful for roles where fit is mostly chemistry and judgment (top executive search, senior partnerships), where a first human read is still the right first step.

How do you handle bias?

The screener follows the JD. Fairness starts with a JD that describes what people can do, not who they are: real years of experience over pedigree fields, demonstrated outcomes over school names. Age, gender, race, religion never enter scoring. No AI removes bias entirely, so this stays a first-pass filter, not a hiring decision.

Can Entexis build this for us?

Yes. This is what we do. Hire us on Upwork and we will scope your first role family, encode your hiring rubric, integrate the screener into your ATS (Greenhouse, Lever, Ashby, Workable, HubSpot), and get a working first slice live once scope is agreed. You own the code, the model runs where you choose, and candidate data stays on your infrastructure. Look for us at upwork.com/agencies/entexis.

See who's a fit before you interview.
Paste the job, upload the resume, get a score.

Job description

Candidate resume

Verdict

Strengths

Gaps & Risks

Suggested interview questions

What AI-integrated hiring looks like in practice.

The integration layer that makes AI usable on real resumes.

PDF / DOCX parsing

Instant fit score

Strengths, made specific

Gaps and risks, called out

Sharp interview questions

API or in-product

Multilingual (English, Hindi, Spanish, French & more)

4 steps from a JD and a resume
to a structured hiring signal.

Paste the job description

Drop the resume

AI scores it

You decide

Frequently Asked Questions

Custom hiring AI, wired into your stack.

Job description

Candidate resume

Verdict

Strengths

Gaps & Risks

Suggested interview questions

What AI-integrated hiring looks like in practice.

The integration layer that makes AI usable on real resumes.

PDF / DOCX parsing

Instant fit score

Strengths, made specific

Gaps and risks, called out

Sharp interview questions

API or in-product

Multilingual (English, Hindi, Spanish, French & more)

4 steps from a JD and a resumeto a structured hiring signal.

Paste the job description

Drop the resume

AI scores it

You decide

Frequently Asked Questions

Custom hiring AI, wired into your stack.

Related labs

4 steps from a JD and a resume
to a structured hiring signal.