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Hire 60% faster with AI-ranked evidence.

Structured interviews, proctoring, AI scoring, and ranked candidate recommendations — all in one platform. Every feature below is live in production.

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60%Faster shortlist decisions
60Candidates ranked in one view
24/7Automated integrity monitoring
Senior Product Analyst
Dvar demo workspace — live screening pipeline
AI ready
60Applied
28Submitted
9Shortlisted
3Flagged
1John Macan96%
2Karan Mehta93%
3Nina Shah91%

From job post to shortlist, automated

Click any step to see what Dvar does there.

Traditional Hiring vs Dvār

Hiring Has Changed. Has Your Process?

See how Dvār transforms hiring from a manual, fragmented process into a structured, AI-assisted workflow.

Traditional Hiring Dvār
Candidate Screening Manual resume screening AI-powered candidate evaluation
Interview Review Watch hours of recordings AI summaries & transcripts
Candidate Comparison Excel sheets & subjective decisions AI rankings & fit scores
Integrity Checks Limited visibility Built-in proctoring
Shortlisting Days of manual effort Minutes to identify top candidates

AI-ranked candidate recommendations

Dvar scores all 60 applicants against the job description in seconds. The carousel below is the exact view inside the live product.

app.dvar.ai / jobs / senior-product-analyst / recommendations
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AI-Powered Analysis
Dvar's Recommendation
60 applied candidates ranked
Top fit candidates for Senior Product Analyst
Preview data shaped like production results.
Live from Dvar
AI Candidate Recommendation · Senior Product Analyst

AI evaluation — before a reviewer opens the file

Summary, JD match, strengths, weaknesses, suitability, and red flags — generated automatically for every candidate.

app.dvar.ai / candidates / John-Macan / evaluation
Summary
John demonstrates strong product analytics judgment, clean communication, and a habit of translating ambiguous business questions into measurable hypotheses. Her examples show hands-on SQL, funnel analysis, stakeholder alignment, and concise experimentation tradeoffs.
JD Match
High match. The role needs dashboard ownership, cohort analysis, experimentation, and executive communication — her responses directly map to all four.
Strengths
Strong SQL and metric design · Clear product thinking · Connects analysis to business action · Structured communication
Weaknesses
Could give deeper detail on data quality checks · Limited mention of formal A/B test power calculations
Suitability
Recommended for final round.
Resume Red Flags
A six-month contract gap between two roles was identified and should be discussed in the next round.
Live from Dvar
AI Evaluation · John Macan · Senior Product Analyst

Admin dashboard and daily reporting

Live status analytics and job-level tracking — no manual pulls, no dashboard login needed for a status check.

app.dvar.ai / admin / dashboard

Candidate status breakdown

Bar, stacked, pie, or line chart view.

Active jobs

Live roles and exam sessions in the workspace.

Interview / exam Team Invited Done Status
Senior Product Analyst
8 questions · AI ranking ready
PMO6028Live
Campus Aptitude Round
Timed exam · proctoring enabled
HR240198Open
Cloud Support Engineer
Technical interview · reviewer pending
GTA4231Review
MBA Case Assessment
Institution exam · daily report sent
HR118102Open
Active interviews
18
Open jobs and exam sessions today.
New submissions
44
Candidate responses ready for review.
Failed submissions
3
Flagged before reviewer time is spent.
Report delivery
23:59
Daily summary delivered automatically.
Live from Dvar
Admin Dashboard · Candidate Analytics & Job Tracking

Candidate review — transcript, video, and proctoring in one screen

No rewinding, no re-reading specs. Every question, answer, and integrity event is in context for the reviewer.

app.dvar.ai / review / John-Macan / responses

Interview Response: Product metric design

Question 2 of 8 — John Macan

Attempted
Question
Walk us through how you would define success for a new onboarding flow.
Candidate: I would start by separating activation from engagement. For activation, I would define the first meaningful action, then track completion rate, time to first value, and drop-off by step. I would also segment new users by source and role because onboarding friction can look very different across cohorts.

Interview Response: Experimentation and A/B testing

Question 5 of 8 — John Macan

Attempted
Question
How would you design and measure an A/B test for a new checkout flow?
Candidate: I would begin by defining the primary success metric before writing a single line of the test plan. For a checkout flow, that is typically conversion rate, but I would also track order value, error rate, and time to complete to avoid optimizing the wrong thing.
Live from Dvar
Candidate Review · Transcript, Video & Proctoring · John Macan

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