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Voice AI interviews · ATS studio · Code sandbox

Interviews prove readiness.
This gets students placed.

One platform for voice AI mock interviews, resume scoring against live recruiter job descriptions, verified coding assessments, and campus placement tracking.

  • Explainable scoring
  • Alerts inside 10 minutes
  • Every application tracked
  • Multi-model AI engine

Built for placement cells, training teams, and hiring partners

Final-year cohortsTPO & placement cellsSkill trainersRecruitersNAAC / NBA reporting
< 10 min
Alert delivery SLA
From recruiter post to student notification.
25% weight
Voice AI interview
Spoken mock interviews with structured scoring.
72 hours
Application sweep
Every click resolves to a verified outcome.
Multi-model
AI engine
Reasoning, conversation and speed models, switched centrally.
Interactive product tour

Try the core loops before you sign in

Voice interview, resume scoring, code verification, and officer analytics — the same flows students and placement teams use daily.

AI Placement Technical InterviewerTTS Active
Multi-Turn Behavioral & Systems Engineering Track
Interviewer audioReady for next question
“Can you explain how optimistic locking prevents lost updates in a high-concurrency microservice environment?”
Candidate Response (Microphone Input):Transcribed live via Speech-to-Text

“Optimistic locking utilizes a version column or timestamp. When updating records, Hibernate or PostgreSQL checks if the version hasn't changed. If another transaction updated it first, an OptimisticLockException is thrown, preventing silent data overwrite.”

Voice interviewer with live transcription
AI Evaluation Engine
90 / 100 Overall
Four Evaluated Competencies
Mathematical mean feeds 25% of student readiness score
Technical Depth & Accuracy92
Problem Solving & Edge Cases88
STAR Framework & Communication94
Practical Project Knowledge86
Key Examiner Feedback:Excellent architectural precision regarding optimistic locking and exception handling. Candidate articulated transaction boundary tradeoffs clearly.
Readiness Impact:+22.5% Weighted Points
Who it's for

One system for the whole campus hiring loop

Candidates prepare, officers operate, recruiters get verified signal.

For candidates

Graduating students

Walk into every drive rehearsed, with a resume that matches the actual job description.

  • Voice AI mock interviews: Spoken practice rounds with structured feedback.
  • ATS resume scoring: Line-by-line rewrites tied to recruiter requirements.
  • Verified skill badges: Pass sandboxed tests to prove coding ability.
  • Explainable matches: Every recommendation shows why you fit.
For placement cells

TPOs & placement officers

Replace spreadsheets with an auditable pipeline and on-time alerts.

  • Job ingestion & dedupe: Extract CTC and criteria; duplicates are blocked.
  • 72-hour application sweep: Stale clicks resolve to clean funnel numbers.
  • Directory & CSV export: Filter by readiness; export compliance reports.
  • Skill heatmap: See what to train before hiring season.
For hiring partners

Recruiters & enterprise

Shortlist verified graduates without resume noise.

  • Verified skills: Backed by sandboxed tests, not self-reports.
  • Structured scorecards: Technical depth, problem solving, and STAR.
  • Pre-filtered eligibility: CGPA, branch, and backlog criteria applied.
  • Faster shortlists: Interview-ready graduates in hours, not weeks.
Explore recruiter access
Multi-model AI infrastructure

Never locked to a single model

Your platform administrator selects and switches the underlying engine centrally — reasoning, conversation, or speed-optimised — with a deterministic fallback when a provider is unavailable. Nobody else needs to think about which model is running.

Reasoning models
Code feedback, ATS critique, and job-description parsing.
Conversational models
Fluent turn-taking for voice mock interviews.
Low-latency models
Fast responses with large-context comprehension.
Open-weight models
Self-hosted options for institutions that require them.
Administrator-controlledUniversal gateway
Reasoning
For structured critique
Conversation
For live interviews
Speed
For fast turnaround
Self-hosted
For data residency
Honest by design: matching, readiness and eligibility are pure arithmetic and never depend on a model. AI-only features tell you plainly if a provider isn't configured, rather than fabricating a result.
Explainable scoring

No black-box readiness scores

Fixed arithmetic combines five verified components. The model assesses dimensions — code computes the total.

25%
AI mock interview

Spoken answers scored on technical depth, problem solving, STAR, and experience.

Same input, same score
25%
Verified skills

Skills proven by passing sandboxed coding tests.

Same input, same score
20%
Resume alignment

Keyword coverage, formatting, quantification, and readability.

Same input, same score
15%
Profile completeness

CGPA, projects, links, and credentials checklist.

Same input, same score
15%
Activity

Applications, sprints, and follow-through pace.

Same input, same score
Why Placement OS?

Built for campus hiring, not generic HR

How the platform compares to spreadsheets and legacy portals.

Comparison of Placement OS against spreadsheets and legacy campus portals
CapabilityPlacement OS 3.0SpreadsheetsLegacy portals
Voice AI mock interviewsSpoken interviews with structured scoringManual mocks onlyOne-way recorded video
Multi-model AISwitched centrally by your administrator, no vendor lock-inNoneSingle locked-in model
Resume critiqueLine-by-line rewrites against the JDPeer reviewKeyword counter
Skill verificationSandboxed tests with AI hintsSelf-reported checkboxesMultiple-choice quizzes
Job intake & dedupeAI extraction with duplicate guardCopy-paste, duplicates commonManual form entry
Application tracking72-hour sweep to verified outcomesStale rowsStuck on “Applied”
Compliance exportOne-click NIRF / NBA / NAAC CSVHours of cleanupRigid generic reports
FAQ

Answers, up front

Voice, scoring, dedupe, tracking, and deployment.

How do voice AI mock interviews work?
The interviewer speaks via streaming text-to-speech and listens via microphone transcription, with typed answers as fallback. Each answer is scored on fixed dimensions; code computes the total so results stay consistent.
Can students see hidden coding tests?
No. Hidden inputs run in an isolated runner and are redacted before any AI feedback call. The mentor sees only which checks failed, never the hidden data.
How is duplicate job spam prevented?
Company, title, and location are normalized and hashed on intake. Re-submissions of the same role collapse to one record, so students get one alert instead of four.
What happens after a student clicks Apply?
The click is tracked, the student applies on the recruiter site, then confirms here. Unconfirmed clicks are prompted and auto-resolved within 72 hours, so funnels show real outcomes.
Can a college use its own models?
Yes. A platform administrator can point the engine at a private or self-hosted endpoint. Model choice is an administrator setting — students, staff and employers never see or select a model.

Run placements on rails, not spreadsheets

Open the live demo as a student or placement officer. Interviews, resume scoring, sandboxes, and analytics all work with demo data.

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