“Which role should I even target?”
The system scores your evidence against all eight tracks and names a defensible primary plus a backup.
Placement season is open. Summer-internship shortlists begin within 24 days at many campuses. Start the readiness scan
Campus placement preparation
Know your role, gaps, and next move.
The scan reads your CV, names the role you can defend, and turns your gaps into a weekly plan.
Today workspace Final placement · Ready for the window
Illustrative sample dataHighest impact today
View planYour plan was refreshed with your latest progress. See why your plan changed
- 1Quantify two project bulletsready Open CV scan
Screening rubrics reward measurable impact. Your CV lists responsibilities without numbers.
15 minutes · CV evidence - 2Complete one market-sizing caseneeds-attention Start practice
Product cases test structure and mental math under pressure.
30 minutes · Case Arena - 3Record Why Product Management?in-progress Open brief
A self-recorded answer gives your mentor a concrete starting point.
10 minutes · Mentor review
Readiness evidence
Review evidence mapEvidence Verified CV proof and quantified stories
developingArticulation Case and interview scores
buildingStory Impact narrative ready for review
neededCV verified8 claims, 6 with sources
Mock score71 · rubric cited
Mentor reviewBriefing loaded
7-minute readiness scan No card required For internships and final placements
Diagnostic to plan in one sitting Eight role tracks, one readiness model CV, case, and mentor evidence in one loop
The problem is not a lack of preparation material. It is not knowing what matters next.
Placement season rewards four decisions, made in the right order. Most students make them alone, under a deadline.
“Where am I actually weak?”
Not “cases” in general: a named gap, like bullets without numbers or mental math under pressure, ranked and actionable.
“What should I do next?”
Weekly missions with durations and proof gates, reordered as shortlists move closer.
“Is my preparation actually improving?”
Readiness bands, practice scores, and mentor sign-offs: a record of change you can defend.
See what your CV already proves before you sign in.
The scan reads the CV you use today, scores it against your target track, and names the gaps that will surface in screening.
- Grounded preview: every score cites its source line in your CV.
- Your data, your control: unclaimed files expire in seven days or are deleted on request.
- Carried forward: claim it and the same result moves into your account, no second scan.
One placement loop. No disconnected preparation.
Ten steps, three phases, one readiness model.
Decide
Diagnose 7-minute readiness scan
Select role 8 tracks scored on evidence and fit
Build plan Weekly missions ranked by impact on the nearest shortlist
Know your baseline Readiness bands you can re-measure weekly
Build and practise
Build proof Assignments become portfolio artifacts with metrics
Position CV Role-specific versions with before/after diffs
Practise cases and interviews Case Arena, simulator, and mock rounds, rubric-scored
Mentor review Human judgment, with full context loaded
Execute and improve
Apply Campus and external applications tracked by stage
Learn and improve Outcomes re-weight gaps. The next application starts stronger
Every outcome feeds back into the readiness model. A loop, not a checklist.
Prepare for the role, not the generic interview.
Each track changes your competencies, CV rules, missions, and practice queue.
Product track
Product Management
Product sense, prioritisation logic, and evidence of shipped decisions.
Current readiness 64%
Competencies evaluated
Product judgementStructured problem-solvingBusiness and tech fluencyCommunicationResume evidence
Critical gaps
- CV lists responsibilities but no quantified product outcomes
- No market-sizing practice under time pressure
- Weak “Why product management” narrative
Sample weekly plan
- Quantify two product bullets 15 min
- Market-sizing case with written structure 30 min
- Record “Why Product Management?” 10 min
- Teardown one app you use daily 25 min
Sample CV recommendation
Restructure the top two bullets as problem → action → measured result. Lead with the metric: conversion, retention, or time saved.
Suggested practice
- Product prioritisation case
- Metric definition and sizing case
- Behavioural: influence without authority
Start the readiness scan against this track. The result stays yours whether or not you save the plan.
Build my Product planOne readiness model behind twelve modules.
Decide
Build
Practise
Execute
Learn
Decide
Diagnose, choose a role, and set the plan.
3 modules
Placement Diagnostic
InputCV facts, experience band, role targets, first deadline.
DecisionScores evidence, articulation, and role fit per target.
OutputReadiness baseline and the three gaps blocking shortlisting.
Retaking shows change.Role Navigator
InputStrengths, proof, constraints, interview deadlines.
DecisionRanks eight tracks by shortlist odds and evidence fit.
OutputA primary role with a reason plus one backup.
Weights shift to the chosen track.Weekly Mission Planner
InputGaps, deadlines, available hours, mentor availability.
DecisionOrders a seven-day queue by impact on the nearest shortlist.
OutputToday’s three missions with times and one start button.
Completing a mission updates its gap the same day.Build
Turn experience into scored proof and role-ready CV versions.
2 modules
Proof Portfolio
InputInternships, projects, work, wins, failures.
DecisionDetects claims without evidence; maps stories to competencies.
OutputSix to ten proof stories, each with a metric.
Proof coverage becomes a scored dimension.CV Positioning Studio
InputCurrent CV, target role, verified proof stories.
DecisionRewrites bullets as problem → action → measured result.
OutputRole-specific CV versions with before/after diffs.
The CV-evidence score updates per version.Execute
Run the application pipeline and deadline pressure.
2 modules
Application Command Center
InputCampus drives, portals, referrals, deadlines.
DecisionTracks stage, documents requirements, re-prioritises prep per application.
OutputOne pipeline with a next action on every row.
Pipeline health feeds time-to-shortlist estimates.Opportunity Matching
InputRole track, readiness bands, proof, constraints.
DecisionScores opportunities by fit and deadline pressure.
OutputA ranked shortlist of roles to apply to this week.
Application quality follows from match.Learn
Turn every outcome into the next stronger round.
1 modules
Outcome Learning Loop
InputOutcomes: rejections, feedback, question recalls, offers.
DecisionMaps each outcome to competencies and finds repeat patterns.
OutputA fix-before-next-round list and updated evidence.
Rejection reasons become weighted gaps.Institute
Consent-scoped cohort views for placement teams.
1 modules
Placement Cell Analytics
InputConsented aggregate readiness, risk flags, outcomes.
DecisionRolls student data up without exposing private content.
OutputA cohort readiness map and intervention queues.
Institute support tasks appear on student plans.Three minutes of real workflow.
Seven minutes in, the gaps are named.
The scan reads what your CV proves, scores it against your track, and ranks the three gaps that block shortlisting.
- Claims separated from verified facts
- Readiness scored per role, not one number
- The first mission targets the top gap
Readiness scan result
CV evidence 64 / 100 In progress
Top gap Bullets lack quantified impact Priority
Next mission Quantify two product bullets 15 min
AI for repetition. Mentors for judgment. One shared student context.
Most platforms force a choice between a chatbot and a mentor. This product gives each one the work it does best.
AI runs the repetition
Volume work, grounded feedbackFirst-pass case evaluation, CV drafting, mission scheduling, and outcome capture.
- Scores cite your exact answer
- CV edits show before/after with sources
- High-stakes evaluations fall back to human review
Mentors supply the judgment
Where experience actually mattersStory selection, shortlist risk, CV sign-off, and offer decisions.
- Mentors open a briefing, not a blank call
- Rubric reviews become permanent records
- Consent-scoped: mentors see only what you share
One shared context
AI notes, mentor guidance, and your plan share one student record. Mentors see what you already practised.
AICase score 58 → 71 across 14 reps. Two reps queued for this week.
MentorLead with the launch story. It is the strongest evidence on the CV v3.
PlanNext: record “Why Product Management?”, then book mentor review at 6:30 PM.
Every application should improve the next one.
A rejection is data.
NovaPay · PM InternFirst shortlist window in 24 days
PreparingAster Consumer · Brand MTApplication answer in progress
ScreeningMeridian Consulting · AssociateCase round · 3 reps planned
Case roundZephyr Labs · APMBehavioural prep locked to CV v3
InterviewFieldline Analytics · AnalystDecision expected Friday
Offer stageAtlas Finance · AnalystRejected after case round · learnings captured
LearnedThe rejection became the plan.
Application #6 failed at the case round. The loop named the failure pattern, changed the queue, and prepared the next application.
Outcome capture · after application #6
- Captured: structure collapsed in the follow-up. Mental math slowed at 3× pressure.
- Plan updated: three profitability reps plus a daily math drill. The next CV leads with a quantified profit-and-loss story.
- Application #7 starts with the fixed skills already loaded.
Illustrative workspace using fictional companies. The loop mechanics are the product. The names are placeholders.
For placement cells
Give the placement cell a live readiness map, not another attendance dashboard.
Consent-scoped aggregates show who is shortlist-ready, who is at risk, and what to do about it, without exposing student privacy.
- Readiness by role and deadline: cohorts, not attendance rows.
- Risk queues with real next actions: targeted missions for students below shortlist readiness.
- Consent-governed reporting: aggregates and support summaries only, no private content by default.
- Outcome truth: offers and PPOs verified, separated from guidance data.
Cohort readiness map
214 consenting students · final placements
- 42 students below 55% readiness with a first shortlist under 30 days away.
- 17 CVs lack quantified impact for their target role.
- 9 mentor reviews requested for next week.
Next action: focused workshop on quantifying achievements Request a campus demo
Pricing
Start free. Upgrade when the loop needs depth.
The free path is a real product: scan, baseline, and first plan. Paid plans add practice volume, CV exports, and mentor credits, never a paywall mid-scan.
Free
Readiness scan + first plan
₹0
Your readiness baseline and first three missions, yours to keep.
- Grounded CV readiness scan
- Readiness baseline per role track
- First plan with three ranked gaps
Billed month
Placement Pro Monthly
₹499.00per month
- 5 Company sprints
- Peer practice included
- 5 Resume AI reviews / month
- 120 Voice interview minutes
Answers before you start.
What is Prepco?
Prepco is a campus placement preparation system. Diagnose readiness, choose a role, build a weekly plan, improve your CV, practise interviews, apply, and feed outcomes back into your plan.
Who is Prepco for?
Students preparing for summer internships and final placements, including unplaced students applying off campus. The loop serves all eight role tracks.
Is the readiness scan really free?
Yes. The scan builds your baseline and first plan at ₹0. Paid plans add practice volume, CV exports, and mentor credits, after you see the free result.
How do you decide which role I should target?
The diagnostic scores your evidence across eight tracks and weighs deadlines, location, and proof. It recommends a primary track with a reason; you make the final call.
Do I need to upload my CV to start?
Placement gaps live in what recruiters read. Run the preview account-free; sign in only when you save the plan.
How is this different from a generic AI chatbot?
Scores cite your exact answer. CV edits cite your source text. High-stakes evaluations fall back to human review when grounding fails.
What do human mentors actually do?
AI supports practice, drafting, scheduling, and outcome capture. Mentors judge story selection, shortlist risk, CV sign-off, and offer decisions from shared context.
Can our placement cell see students’ data?
Only what students consent to share: aggregate readiness maps and risk queues, never transcripts or private notes by default.
I am unplaced and applying off campus. Will this still help?
Yes. Rejection reasons become weighted gaps. Applications track by stage. CV versions adapt to each role.
Your placement season has already started.
You leave with the role to target, the gaps that matter, and tomorrow’s first mission.
Start the readiness scan7-minute readiness scanNo course purchaseBuilt for internships and final placements