How AI Screenr Works
How AI interview software works: set up a job description in minutes and automate candidate screening with AI voice interviews. Candidates complete interviews async with no scheduling. Get scored reports with transcripts in minutes. Powered by AI Screenr. 3 free interviews. No credit card required.
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Three steps from job posting to ranked shortlist
The AI Screenr workflow in summary — no integration, no scheduling.
Configure the role
Paste a job description for one-click AI setup, or build it manually in about 5 minutes. AI Screenr extracts skills, disqualification rules, scoring weights, and up to 5 structured question templates. Edit anything before launch.
Share the interview link
Put one link in your ATS, email, SMS, or job board. Candidates interview async on any device — no scheduling, no app install, no account creation. Typical duration is 15 to 25 minutes (configurable from 5 to 60).
Review the scored shortlist
Within 2 minutes of each interview, a scored report arrives in your dashboard with a Strong Yes / Yes / Maybe / No recommendation, dimension scores, evidence quotes, and a ranked shortlist.
See a live walkthrough in under 5 minutes with 3 free interviews.
Try Free — No Credit CardStep 1 in action — a realistic job configuration
What AI Screenr produces after one-click setup from a pasted job description. Every field is editable before you launch the interview link.
Senior Product Manager (B2B SaaS)
Job Details
Basic information about the position. The AI reads all of this to calibrate questions and evaluate candidates.
Job Title
Senior Product Manager (B2B SaaS)
Job Family
Product
Product roles emphasise prioritization, discovery, and stakeholder reasoning — the AI calibrates follow-ups around judgement and trade-offs rather than execution detail.
Interview Template
Competency-Based Screen
Allows up to 4 follow-ups per question. Pushes on trade-off reasoning and asks for specific past examples — surfaces the difference between experienced PMs and PM-adjacent candidates.
Job Description
We're hiring a senior product manager to own a core B2B workflow surface. You will partner with engineering, design, and go-to-market to discover real customer problems, prioritise decisively, and ship outcomes — not just outputs.
Normalized Role Brief
Senior product manager with 5+ years of B2B SaaS experience, a track record of owning an end-to-end product area, and the judgement to make scoping calls without a step-by-step guide. Writing-first, opinionated, calm under ambiguity.
Concise 2-3 sentence summary the AI uses instead of the full description for question generation.
Skills
Required skills are assessed with dedicated questions. Preferred skills earn bonus credit when demonstrated.
Required Skills
The AI asks targeted questions about each required skill. 3-7 recommended.
Preferred Skills
Nice-to-have skills that help differentiate candidates who both pass the required bar.
Must-Have Competencies
Behavioral/functional capabilities evaluated pass/fail. The AI uses behavioral questions ('Tell me about a time when...').
Defends trade-off decisions with evidence; can explain what was cut and why, not only what shipped
Runs real customer conversations, not surveys; distinguishes surface complaints from the underlying job the customer is trying to do
Moves engineering, design, and go-to-market together without authority; resolves scoping conflicts with written analysis
Levels: Basic = can do with guidance, Intermediate = independent, Advanced = can teach others, Expert = industry-leading.
Knockout Criteria
Automatic disqualifiers. If triggered, candidate receives 'No' recommendation regardless of other scores.
B2B Experience
Fail if: No prior B2B SaaS product experience at any scale
This role requires B2B-specific judgement — buyer is not user, procurement cycles, seat economics
Tenure
Fail if: Less than 5 years of product management experience
Senior level — needs to own an area without structured onboarding from day one
The AI asks about each criterion during a dedicated screening phase early in the interview.
Custom Interview Questions
Mandatory questions asked in order before general exploration. The AI follows up if answers are vague.
Walk me through the most consequential prioritization call you have made in the last 12 months. What did you cut, what did you keep, and what do you know now that you didn't know then?
Describe a time your discovery work changed the direction of a planned feature. What did you hear, how did you validate it, and what shipped instead?
Tell me about a cross-functional disagreement you resolved. What was the disagreement, what did you write down, and how did the team decide?
Open-ended questions work best. The AI automatically follows up if answers are vague or incomplete.
Question Blueprints
Structured deep-dive questions with pre-written follow-ups ensuring consistent, fair evaluation across all candidates.
B1. Design the first version of a usage-based billing upgrade prompt for our product. Walk me through the user segment, trigger, copy, and success metric.
Knowledge areas to assess:
Pre-written follow-ups:
F1. How would you avoid prompting users who are already in a renewal conversation?
F2. What is your guardrail metric, and at what threshold would you stop the prompt?
F3. How do you decide between an in-product prompt, email, or account-manager outreach?
B2. A top-10 customer asks for an enterprise feature that would cost 2 engineer-quarters and would not benefit any other customer. Walk me through how you decide.
Knowledge areas to assess:
Pre-written follow-ups:
F1. How do you communicate the decision to the account team?
F2. What if the customer threatens to leave?
F3. How would your answer change if the feature could be generalised later?
Unlike plain questions where the AI invents follow-ups, blueprints ensure every candidate gets the exact same follow-up questions for fair comparison.
Custom Scoring Rubric
Defines how candidates are scored. Each dimension has a weight that determines its impact on the total score.
| Dimension | Weight | Description |
|---|---|---|
| Communication Clarity | 12% | Does the candidate structure answers clearly, use concrete examples, and avoid hedging? |
| Relevance of Answers | 12% | Does the answer directly address the question asked, or does the candidate move to a safer topic? |
| Technical Knowledge | 18% | Understanding of product management fundamentals — discovery, prioritization, metrics, lifecycle |
| Problem-Solving | 14% | Ability to reason through uncertain scenarios, weigh trade-offs, and defend conclusions when challenged |
| Role Fit | 14% | Match with the actual demands of a senior B2B PM role — writing-first, opinionated, comfortable with ambiguity |
| Confidence & Presence | 6% | Steady under follow-up questions; acknowledges gaps without losing composure |
| Behavioral Fit | 10% | Collaboration signals — how the candidate talks about disagreement, conflict, and giving team credit |
| Completeness of Answers | 14% | Covers the full question, not only the easy half; volunteers caveats and counter-examples |
Default rubric: Communication, Relevance, Technical Knowledge, Problem-Solving, Role Fit, Confidence, Behavioral Fit, Completeness. Auto-adds Language Proficiency and Blueprint Question Depth dimensions when configured.
Interview Settings
Configure duration, language, tone, and additional instructions.
Duration
25 min
Language
English
Template
Competency-Based Screen
Video
Enabled
Tone / Personality
Friendly but structured. The AI keeps the conversation moving and asks politely for detail when answers are vague.
Adjusts the AI's speaking style but never overrides fairness and neutrality rules.
Company Instructions
We are a mid-stage B2B SaaS company with around 80 engineers. Our product is used by RevOps and sales teams. Mention that the role reports to the VP of Product and partners with a Principal Engineer and a Lead Designer.
Injected into the AI's context so it can reference your company naturally and tailor questions to your environment.
Evaluation Notes
Treat written-communication signals (does the candidate structure answers, reference specific metrics, name stakeholders) as positive. Penalise vague generalities and answers heavy with industry terms but no specifics.
Passed to the scoring engine as additional context when generating scores. Influences how the AI weighs evidence.
Banned Topics / Compliance
Salary negotiation, references, equity package, compensation structure — these are handled separately after the scored round.
The AI already avoids illegal/discriminatory questions by default. Use this for company-specific restrictions.
How AI Screenr filters a 100-candidate pipeline
From applications to shortlist: each stage narrows the pipeline based on evidence, not instinct. The numbers below are typical for a mid-volume tech role.
Applications received
All incoming candidates enter the pipeline — from job boards, referrals, ATS auto-responses, and direct outreach.
Disqualification rules
Hard filters you define — minimum experience, work authorization, location, salary range, language. Candidates who fail are flagged, not automatically rejected.
Must-have skills
Pass or fail on the non-negotiable skills for the role (for example, senior React depth for a Senior React Developer). Evaluated live during the interview.
Language level
Optional CEFR assessment (A1–C2) in the language you specify, with a dedicated interview phase. Skipped if not configured.
Scored interview
8 default scoring dimensions (customizable) score every answer on a 0–100 scale with evidence quotes, quality ratings (Strong / Moderate / Weak / None), and confidence values per dimension.
Ranked shortlist
Top-scored candidates with a 4-point recommendation, executive summary, strengths and risks, notable quotes, and coverage summary. Ready for the hiring manager.
Step 3 in action — a realistic AI screening report
Exactly what arrives in your dashboard within 2 minutes of the candidate saying goodbye. Every score is backed by transcript evidence, a quality rating, and a confidence value.
Alex Morgan
Confidence: 86%
Recommendation Rationale
Strong senior-PM signal. Alex articulated prioritization trade-offs with specifics — exact metrics, named stakeholders, what got cut and why — across multiple examples. Discovery discipline is genuine: the case of reversing a planned feature after three customer conversations was well-structured and included the counter-narrative (what they expected to hear versus what they actually heard). The visible gap is cross-functional leadership under disagreement; Alex defaults to consensus before pushing, which may be fine or may be a risk depending on the team dynamics. Recommended for the hiring-manager round.
Summary
Five-plus years of B2B SaaS product management with clear area ownership. Strong on discovery and prioritization — concrete examples with metrics and trade-offs. Cross-functional leadership is competent but consensus-leaning; the candidate does not push back early when the team direction is wrong. Writing-first instincts are evident from how answers are structured. Calm under follow-up questions.
Knockout Criteria
Over five years of B2B SaaS work across two companies with clear area ownership. Named specific enterprise-deal scenarios.
Seven years of product management experience. Comfortably above the 5-year minimum.
Must-Have Competencies
Multiple concrete examples with named metrics, explicit trade-offs, and what was cut. Defends decisions without hedging.
Real customer-interview examples — not surveys, not analytics. Distinguished underlying customer jobs from stated feature requests twice without being asked.
Works well across engineering, design, and GTM but tends to build consensus before pushing hard. Likely fine for collaborative teams; a risk in environments that need a strong product voice.
Scoring Dimensions
Consistently structured answers with explicit context, decision, and outcome. No filler. Named specific metrics and stakeholders without being asked.
“I cut the Phase 2 reporting work because we had 11% adoption on Phase 1 after four weeks — below our 25% threshold. The data team wanted to keep going; the tradeoff was between doubling down on adoption vs. shipping a feature that no one was using yet. Anna on analytics and I wrote a one-page decision memo; we killed it.”
Answers hit the specific scenario asked rather than moving to safer ground. When pressed, Alex stayed on the question instead of reframing.
“You asked about a prioritization call I regret. The honest answer is the customer-success dashboard — we built it, adoption was high, but the exec team never used the weekly digest we also shipped. I should have killed the digest in week two, not week six.”
Strong fundamentals on discovery, prioritization frameworks, and metric instrumentation. Missed a small point on activation-metric definition vs. retention but self-corrected when probed.
“For the upgrade prompt I'd use activation defined as 'user completed three saved reports in the first week' as the trigger, not time-based. Activation predicts retention in our data — we know from the month-three cohort analysis.”
Thinks in trade-offs. For the top-10 customer scenario, Alex walked through cost of distraction, precedent risk, and three alternatives before reaching a recommendation.
“Two engineer-quarters is not the only cost — there's the precedent cost, the integration-debt cost, and the opportunity cost. I'd first explore whether we can deliver 80% of the value as a services engagement; if not, I'd scope it explicitly as a paid, roadmap-committed item with a price that reflects the opportunity cost.”
Writing-first, opinionated, comfortable being specific. Mentioned three decision memos by name. No generic answers — all examples are team-specific.
“For hard decisions I write a one-pager: the decision, the trade-offs, who disagrees and why, and whether it can be reversed. It goes to the team before the meeting. People come in ready to decide, not ready to present.”
Steady under probes. Acknowledged the two regrets without defensiveness. Slight hedging when pushed on the 'disagreement with leadership' scenario.
“I haven't had a full disagreement with a CPO-level stakeholder — most of mine have been with GTM leads. I can give you one of those if that's useful.”
Collaboration signals are positive but consensus-leaning. Several examples showed Alex pushing back only after team direction had already drifted, not earlier.
“I raised the concern at the fourth prioritization meeting. In hindsight the second meeting was where I should have raised it — by then we were three weeks in and the sunk-cost argument was already forming.”
Volunteered caveats and counter-examples without being asked. Answered both halves of compound questions — what went well and what did not.
“The upgrade-prompt experiment succeeded on conversion but the downstream effect was increased support volume from users who upgraded without understanding the entitlement. That was a miss we should have anticipated.”
Blueprint Question Coverage
B1. Usage-based billing upgrade prompt
+ Defined activation with concrete behavioural criteria rather than time
+ Paired conversion metric with a support-volume guardrail
- Did not discuss what threshold would trigger stopping the prompt
B2. Top-10 customer asking for non-generalizable feature
+ Framed cost beyond headcount — precedent and integration debt
+ Proposed a scoped, paid alternative before reaching refusal
- Did not address the case where the feature could be generalised
Interview Coverage
%
Overall Coverage
Strengths
- Writing-first instincts — decision memos are a default tool, not a ceremony
- Prioritization trade-offs backed by named metrics and specific dates
- Strong discovery discipline — distinguishes underlying customer jobs from stated asks
- Volunteers counter-examples and regrets without being asked
Risks
- Consensus-leaning under disagreement — pushes back after drift rather than before
- Limited evidence of CPO-level conflict; all examples are GTM-facing
Notable Quotes
“People come in ready to decide, not ready to present.”
“Two engineer-quarters is not the only cost — there's the precedent cost, the integration-debt cost, and the opportunity cost.”
“The honest answer is the customer-success dashboard — we built it, adoption was high, but the exec team never used the weekly digest.”
Suggested Next Step
Advance to a 60-minute hiring-manager round focused on one 'I disagreed with leadership' scenario and one 'scope-cut defence' case study. Probe for the moments where Alex would push back earlier in the process, not only when consensus has already drifted.
AI Screenr turns a job description into a scored shortlist in four steps. This page walks through the AI interview process end to end, so you know exactly what happens between clicking "create job" and opening the first ranked report.
- Step 1: Configure the role (one click or about 5 minutes manually)
- Step 2: Candidate completes the voice interview async (typically 15 to 25 minutes)
- Step 3: AI scores and summarises (under 2 minutes per candidate)
- Step 4: You review the ranked shortlist
No ATS integration required. Works with any existing hiring process.
Try the full AI interview process with 3 free interviews →
Step 1 — Configure the Role
You have two paths to a live interview link. Most teams use the AI-generated path.
Option A — One-Click AI Configuration
Paste a job description (internal or public, any length under 10,000 characters). AI Screenr extracts and fills in:
- Title, description, role brief, job family, interview template
- Required skills and preferred skills
- Must-have skills with required levels (basic, intermediate, advanced, expert)
- Disqualification rules (minimum experience, work authorization, salary range, language, and anything else in the job description)
- Custom interview questions
- Up to 5 structured question templates — each with must-cover topics, follow-up questions, and strong and weak answer indicators so the AI knows what a great answer sounds like for this specific role
You review the draft, adjust anything that does not match your actual standards, and save. Typical time: 30 seconds to a minute.
Option B — Manual Configuration
Prefer to build from scratch? The form walks you through every field. Allow about 5 minutes for a new role, faster once you have a template to copy.
What Gets Configured
Either path produces the same output:
- Core interview questions — typically 6 to 10 main areas mapped to scoring dimensions.
- Question templates — must-cover topics, follow-up questions, and answer indicators per question.
- Follow-up depth — how hard the AI pushes on shallow answers, configurable per dimension.
- Disqualification rules — flagged, not automatically rejected. You decide the next step.
- Scoring dimensions — 8 default (fully customizable) plus a 9th language dimension when the interview is non-English.
- Language and CEFR target — interview language (57 supported) and whether language proficiency is being assessed (A1 to C2).
- Interview duration — 5 to 60 minutes, typically 15 to 25.
- Video recording — optional, opt-in per role.
- Link expiration — how long the link remains active for candidates.
For concrete role examples see React Developer, Backend Developer, and Sales Manager. Each page shows a filled-in configuration and a sample report.
Step 2 — Candidate Completes the Voice Interview
This is the only step that involves the candidate. Everything before it is on the recruiter side, and everything after it is automated.
What Candidates See
- The link. One URL, sent via your usual channel — ATS auto-reply, email, SMS, job-board message. No account creation, no app install, no scheduling page.
- Consent and microphone check. Before recording begins, the candidate sees a consent screen, grants microphone access, and completes a 10-second microphone check.
- Greeting. The AI introduces itself, explains the interview process, confirms the role, and answers "what happens with this recording?" in plain language.
- The conversation. The AI asks questions, listens, and adapts. Strong answers are acknowledged and pushed deeper. Shallow answers get follow-up questions. Candidates can ask the AI to repeat a question, take a pause, or ask clarifying questions during the conversation.
- Close. The AI wraps up, offers the candidate a chance to ask anything, and confirms what happens next.
What Makes the Interview Fair
Every candidate for the same role gets the same scoring criteria. The AI adjusts follow-up questions based on each candidate's specific answers, so no two transcripts are identical, but all transcripts are scored against the same criteria. The candidate cannot tell from the interview alone whether they are doing well or poorly, which reduces performance anxiety and produces more honest signal.
Timing and Completion
- Typical duration: 15 to 25 minutes (configurable from 5 to 60 per role).
- Completion rate: 80–90% — significantly higher than one-way recorded video because there is real interaction.
- If the candidate gets disconnected, they can resume from the same link for up to 24 hours. The interview continues from where it left off. Partial interviews are flagged in the report.
For more on the async workflow, see async interview software.
Step 3 — AI Scores and Produces the Report
Within 2 minutes of the candidate saying goodbye, a structured report is ready in your dashboard. Here is exactly what is in the report:
Top of the Report
- Overall score — 0 to 100 weighted aggregate across all scoring dimensions.
- 4-point hiring recommendation — Strong Yes / Yes / Maybe / No.
- Overall confidence — 0.0 to 1.0 score reflecting how much evidence the AI had to work with.
- Executive summary — 2 to 3 sentence overview for hiring managers who only read the top.
Dimensional Scores
Each scoring dimension shows:
- Score (0 to 10, then weighted) with a 1 to 2 sentence rationale
- Quality rating: Strong / Moderate / Weak / None
- Confidence value per dimension (0.0 to 1.0)
- Evidence excerpts — direct transcript quotes that support the score
- Linked questions — which interview questions produced the evidence
- Missing evidence notes — what the criteria expected but the transcript did not show
Disqualification and Must-Have Results
If you defined them in Step 1:
- Disqualification results — triggered or assessed flags plus evidence for each rule.
- Must-have skill results — pass or fail plus evidence per skill.
Summary Blocks
- Strengths — 3 to 5 bullets of what stood out.
- Risks — 3 to 5 bullets of what raised concerns.
- Notable quotes — the most interesting lines the AI identified in the transcript.
- Suggested next step — a recommendation based on the score and evidence quality.
- Coverage summary — how much of your custom questions, skills, disqualification rules, and question templates were actually addressed by the candidate's answers.
Transcript and Recording
- Full transcript of the conversation, searchable and time-stamped.
- Audio recording by default. Video recording if you enabled it for this role.
Scoring uses the version of the criteria saved at the time of the interview. If you adjust the criteria in the middle of a hiring process, earlier interviews keep their original scores and the new criteria apply from that point forward. You see clean version history instead of silent recalculation.
Every role page has a sample report. Check a few: QA Engineer, DevOps Engineer, Software Engineer, Product Manager.
Step 4 — You Review the Ranked Shortlist
The dashboard sorts candidates by overall score with disqualification flags at the top. A typical review cycle:
- Scan the ranked list. The top 20% usually deserve a closer look. Candidates with triggered disqualification rules drop to the bottom.
- Open the top reports. Read the 2 to 3 sentence summary first, then the strengths and risks bullets, then skim the evidence the AI flagged.
- Decide. Advance, reject, or flag for follow-up. Reject the clear no-fits in bulk. Keep the Strong Yes candidates for the hiring manager.
- Share with hiring managers. One click gives them a share link, a PDF, or a paste-able summary for Slack or email. No AI Screenr account needed.
Recruiter time per candidate drops from 25 to 45 minutes of live call plus notes to roughly 5 minutes of reviewing a structured report. For concrete ROI calculations at typical volumes, see replace screening calls.
Timing Summary
| Step | Who does it | Typical time |
|---|---|---|
| Configure role (AI one-click) | Recruiter | 30 to 60 seconds |
| Configure role (manual) | Recruiter | About 5 minutes |
| Candidate interview | Candidate | 15 to 25 minutes (configurable 5 to 60) |
| AI scoring and report generation | Automated | Under 2 minutes per candidate |
| Review report per candidate | Recruiter | About 5 minutes |
For a 50-candidate pipeline, recruiter time drops from roughly 25 hours (live screens plus notes) to roughly 5 hours (report review). That frees up 20 hours per week for more valuable work.
Example Reports by Role
AI Screenr covers every job category — from software engineering to healthcare, retail, construction, and hospitality. Each role page has a sample interview report so you can see exactly what arrives in your dashboard. A selection below across technical, specialist, and service-sector roles:
| Role | Category |
|---|---|
| Backend Developer | Technology |
| UX Designer | Design |
| Data Analyst | Technology |
| Financial Analyst | Finance |
| Recruiter | HR |
| Paralegal | Legal |
| Real Estate Agent | Real Estate |
| Construction Manager | Construction |
| Production Manager | Manufacturing |
| Veterinarian | Veterinary |
Or browse all 960+ role-specific AI interview guides by category.
Security and Privacy Through the Interview Process
Each step of the AI interview process has a defined data-handling contract. Consent is collected before any recording starts — candidates see an explicit consent screen covering what is recorded, how it is used, and who can see it. Audio and transcripts are stored in-region (EU hosting is available for GDPR requirements) with configurable retention periods per role, after which data is automatically deleted.
Candidates can request deletion of their data at any time through a self-service process, and we provide a Data Processing Agreement on request. On the hiring side, only authenticated users in your workspace can view reports. Shared report links can be set to expire automatically. For the full security and compliance details, see the Security, Privacy and Compliance section on the AI interview software page.
Ready to Try?
Start with 3 free interviews, no credit card. Configure your first role in a minute (one click) or 5 minutes (manual) and see your first scored report the same day.
- AI interview software — Overview of the AI interview software, including features and comparisons.
- Automated candidate screening — Explanation of how screening is automated.
- Replace screening calls — ROI analysis for teams that spend significant time on phone screening.
- Async interview software — How asynchronous interviews work.
- High-volume candidate screening — How to manage large numbers of candidates efficiently.
- Pre-screening interview software — Overview of early-stage candidate screening.
- Pricing — Overview of pricing and usage-based plans.
Frequently Asked Questions
How long does it take to set up AI Screenr?
What does a candidate see in an AI Screenr interview?
How quickly do I get scores after an interview?
Can I change the scoring criteria while a pipeline is active?
How do I share AI Screenr reports with hiring managers?
What happens if a candidate goes silent or gets disconnected?
Can candidates retake an AI interview?
How are interviews delivered to candidates?
Does the AI Screenr report sync back to my ATS?
How does the AI interview scoring actually work?
See it work in under 5 minutes
- One-click job setup
- Candidate interview link
- Scored report in 2 minutes
- Ranked shortlist
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