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AI-Powered Candidate Screening & Ranking for Recruiting Teams

Stop Spending 23 Hours
Screening Resumes Per Hire.

Upload your job description. Upload your candidate pool. Get every applicant assessed, ranked, and summarized against your job description — instantly.

No credit card required

↓ See how it works

Works alongside ·Greenhouse·Lever·Bullhorn·Workday·Any ATS

The Math

0 hrs

reported average hours screening resumes per hire

$0

avg. cost per hire (SHRM, 2022)

0+ hrs

to read 250 resumes manually

< 2 min

PER SCREENING SESSION WITH RESUME AUTOPSY

Sources: Shortlistd (2025); SHRM Human Capital Benchmarking Report (2022).

What the output looks like

CandidateStatusResumeInterviewVerdict
1. Jordan MillsInterview transcript analyzed
Resume
91
Interview
88
VerdictStrong Match
Resume91
Interview88

Kubernetes and CI/CD expertise matches the JD across every dimension — AWS certs seal it

2. Priya SharmaInterview transcript analyzed
Resume
74
Interview
61
VerdictPartial Match
Resume74
Interview61

Solid infrastructure background; missing Terraform and the team leadership the JD requires

3. Ryan NakamuraInterview transcript analyzed
Resume
55
Interview
42
VerdictWeak Match
Resume55
Interview42

SRE experience partially transfers but gaps in cloud cost ownership and cross-team coordination

4. Elena Volkovno interview
Resume
31
Interview
VerdictWeak Match
Resume31
Interview

Junior-level cloud exposure; 3 years short of the seniority the role demands

5. Casey Laurentno interview
Resume
12
Interview
VerdictMismatch
Resume12
Interview

On-prem Linux background has no meaningful overlap with the cloud-native JD requirements

Jordan Mills — expanded view (sample)

Interview Transcript
88/ 100
Strong Match
Technical interview

Jordan Mills identified as candidate — speaks first and consistently refers to personal ownership of infrastructure described on the resume

Demonstrated SkillsSkills from the job description that the candidate actively backed up with specific examples during the interview.

  • Walked through a real multi-region EKS outage with clear ownership of the recovery — credible and specific, matches resume claims
  • Articulated CI/CD architecture decisions with reasoning on trade-offs; referenced the 400-engineer GitHub Actions pipeline directly
  • Demonstrated working knowledge of AWS cost optimization beyond surface-level answers — gave specific numbers from a previous cost-reduction initiative

Gaps AddressedResume weaknesses or missing areas that the candidate clarified or provided new context for during the interview.

  • Team leadership gap from resume analysis partially resolved — candidate has mentored two junior engineers and led incident response, though no formal direct reports yet

Unsupported ClaimsResume strengths the candidate couldn't back up, contradicted, or walked back when questioned during the interview.

  • Resume mentions 'chaos engineering familiarity' but candidate could not name a specific tool or describe a real fault injection exercise when asked directly

Notable ResponsesStandout moments from the interview — specific answers that were particularly strong, revealing, or memorable.

1.

When pushed on scaling beyond current team size, gave a thoughtful answer about delegation patterns and hiring bar — signals leadership readiness

2.

Proactively flagged a limitation in their CloudFormation knowledge without being asked — honesty is a good sign

CommunicationHow clearly and effectively the candidate communicated — structure, conciseness, and command of language.

Concise and technically precise. Answers stayed on-topic without prompting. No rambling, no over-explaining. Strong communicator for a senior IC.

Overall AssessmentOverall evaluation tying interview performance to role fit, referencing specific things the candidate said.

Resume score holds up under interview scrutiny. The chaos engineering gap is confirmed but candidate is self-aware about it. The leadership gap is smaller than the resume suggested. Recommend advancing to the team-fit round.

Resume Analysis
91/ 100
Strong Match
YearsexceedsLevelalignedIndustrysame industry

Seven years of dedicated DevOps experience across fintech infrastructure, with direct Kubernetes cluster management and CI/CD pipeline ownership at scale. The strongest alignment is in AWS and Terraform — the gap is in chaos engineering practices the JD lists as preferred.

Strengths

  • Kubernetes at Scalehigh
    "Manages 30+ EKS clusters across 4 AWS regions" maps directly to the JD's multi-region cluster requirement.
  • CI/CD Pipeline Ownershiphigh
    "Built GitHub Actions pipelines supporting 400+ engineers" — the JD asks for 500+, but the architecture transfers.

Gaps

  • No Team Lead Experiencedealbreaker
    The JD requires managing a team of 3–5 engineers. The resume shows IC work only — no direct reports or people management mentioned.
  • No Chaos Engineeringsignificant
    The JD lists Gremlin or Litmus as preferred. No mention of chaos practices anywhere on the resume.
  • CloudFormation Gapminor
    The JD lists CloudFormation alongside Terraform, but the candidate's IaC experience is Terraform-only.

Keywords

AWS Kubernetes Terraform CI/CD Chaos Engineering

Experience Alignment

Seven years exceeds the five-year minimum. Staff-level scope aligns with the senior IC role. Fintech infrastructure background is a direct match for the industry context.

Overall Assessment

Strong shortlist candidate. The Kubernetes and CI/CD depth directly answers the core of the JD. Chaos engineering is a preference, not a requirement — worth probing in the technical screen rather than screening out.

AI-generated assessment — not a professional evaluation or predictor of hiring outcomes.

↑ Open any candidate for the full diagnostic — resume analysis, interview transcript breakdown, and keyword coverage.

Sample output.

Why candidates ranked — not just scores

QUALIFICATION CHECKLIST — Priya Sharma · Sr. DevOps Engineer JD

MATCH

5+ years infrastructure / cloud engineering

"6 years SRE and platform engineering across AWS and GCP" — experience range and scope align

PARTIAL

Kubernetes / container orchestration at scale

"Managed EKS clusters for 3 services" — present but limited scope; JD requires multi-region fleet ownership

PARTIAL

CI/CD pipeline ownership (500+ engineers)

"Maintained Jenkins pipelines for 80-person org" — solid foundation but significantly under the scale requirement

MISS

Terraform infrastructure-as-code ownership

No evidence found in resume

MISS

Team lead experience (3–5 direct reports)

No evidence found in resume

Every requirement in your JD gets classified. You see exactly why a candidate ranked where they did.

What You Get

01

Evidence-backed scores

Every fit score (0–100) is backed by evidence quotes your hiring manager can actually read.

02

Qualification checklist

MATCH / PARTIAL / MISS per JD requirement — not a black-box number.

03

JD calibration

Fine-tune scoring requirements before any resume is processed. Promote, demote, or add criteria.

04

Match verdicts

Strong Match / Partial / Weak / Mismatch at a glance — with reasoning behind every verdict.

05

Interview analysis

Upload transcripts for AI-powered evidence-based evaluation of how candidates held up.

06

Candidate notes

Status tracking and recruiter notes per candidate — shortlist without a spreadsheet.

07

Batch processing

Up to 8 candidates per session on Starter, 15 on Pro — processed in parallel.

08

CSV + PDF export

Export ranked results and full candidate reports for team sharing and hiring manager review.

09

Privacy first

No training on your data. All content encrypted at rest. Sessions expire automatically.

Match verdicts are AI-generated estimates and should be reviewed with human judgment.

How It Works

01

Paste or upload your job description.

02

Optionally review and adjust the AI-extracted job requirements before scoring begins — promote, demote, or add criteria to match exactly what you're hiring for.

03

Upload your candidate pool of resumes.

04

Receive a ranked list with scores, verdicts, and one-line summaries.

05

Optionally upload interview transcripts for AI-powered candidate evaluation — see who held up under questioning.

Pricing

AES-256 encryptedNo training on your dataSessions auto-expire

Try it free first — 1 session, 3 candidates, no card required.

Free Trial

Free

3 candidates / session

1 session lifetime

Evidence-backed fit scores

Qualification checklist (MATCH/PARTIAL/MISS)

PDF report

Interview transcript analysis

Starter

$29/mo

8 candidates / session

15 sessions / month

Evidence-backed fit scores

Qualification checklist (MATCH/PARTIAL/MISS)

PDF reports + CSV export

Interview transcript analysis

Candidate notes & status tracking

Most Popular

Pro

$59/mo

15 candidates / session

30 sessions / month

Everything in Starter

PDF reports + CSV export

Interview transcript analysis

Candidate notes & status tracking

Priority processing

All plans — including the free trial — include qualification checklists, PDF reports, interview transcript analysis, and CSV export. Paid plans cancel anytime.

Cancel anytime. No lock-in. No sales call required.

Why we built this

Resume Autopsy started from a simple observation: reading 200 resumes manually is a terrible way to find the best candidate. Not because recruiters aren't good at their jobs — but because the human brain isn't designed to hold 200 consistent mental comparisons while also tracking 14 different job requirements.

We built this to do the systematic part — extract every JD requirement, check every resume against every requirement, flag what's present and what's missing — so you can spend your time on what actually requires human judgment.

We built it for recruiting agencies and independent recruiters who screen real candidate pools — not enterprise HR teams with implementation budgets and 6-month onboarding timelines. No per-seat pricing. No contract. Start today.

E

Eduardo

Founder, Resume Autopsy

Frequently Asked Questions

Resume Autopsy extracts the key requirements from your job description — which you can review and customize before scoring starts. It then analyzes each resume against those requirements, generating a fit score (0–100), a MATCH / PARTIAL / MISS checklist for each requirement with evidence quotes, and a match verdict: Strong Match, Good Match, Weak Match, or Mismatch. Every ranking is backed by evidence from the resume, not a black-box score.

No. Resume Autopsy sits between your ATS and your shortlist — it doesn't replace existing tools. Use Greenhouse, Lever, Bullhorn, or whatever you already use to collect applications. Upload your candidate pool to Resume Autopsy, get every resume scored and ranked against your JD, then bring the results back to your team. No integration or migration required.

No. Resume Autopsy does not train on your uploaded resumes, job descriptions, or candidate data. All content is encrypted at rest and sessions expire automatically.

Most sessions complete in under 2 minutes for a full candidate pool. Processing time scales with the number of candidates and resume length.

Resume Autopsy accepts PDF, Word (.docx), and plain text (.txt) files for candidate resumes. Job descriptions can be pasted or typed directly into the tool.

Resume Autopsy scores candidates solely on their match to the requirements in your job description — not on demographics, age, name, or any protected characteristic. We do not perform bias scoring or demographic inference. All hiring decisions should involve qualified human reviewers; AI analysis is a decision-support tool, not a replacement for human judgment.

Most AI recruiting tools are enterprise software with per-seat pricing, 6-month onboarding timelines, and implementation budgets. Resume Autopsy is built for small recruiting teams and agencies that need to screen candidates today — sign in with Google, upload your JD and resumes, get results in minutes. No contract, no implementation sprint, no per-seat fees.

Free Trial — No Approval Required

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