What is FoW on the dashboard?

Prompt Engineering, the Prompt Engineering assessments. It reports its own sessions, completion count and average readiness score alongside interviews and resumes.

What counts as a Strong Fit?

An interview outcome of 70 percent or above. Moderate Fit is 40 to 69 percent and Weak Fit is below 40.

What resume score should we be aiming for?

ATS Ready starts at 75 percent. Between 60 and 74 the resume is flagged as Needs Improvement, and below 60 as Poor.

Can we see branch by branch performance?

Yes, where branch data is available. The dashboard carries a branch-wise analytics section that fills in once branches are present on the student records.

Can we nudge students from inside the platform?

Yes. Both the interview and resume tables have a Send Notifications action, so you can filter to a group and message that group.

Do students see the same numbers?

No. Students see their own scores, levels and cohort ranking. The organisation view belongs to the placement cell.

Placement operationsFor universities and placement cells6 min read

Track how ready your batch is

Read the admin dashboard the way it is built to be read: averages first, then the risk cards, then the outcome bands, then the individual students behind them.

Before you start

A batch onboarded and signed in.
Students who have completed at least one interview or uploaded a resume.

Steps

8 steps, in order.

Read the averages, but only as a baseline

The Dashboard opens on the summary cards: Avg Communication Score, Avg Interview Score, Avg Resume Score, Avg Technical Score, Success Rate, Total Interviews, Total Students and Total Resumes. Read them as a starting line rather than a target. Early in a season the interview averages sit low because the batch is attempting hard rounds cold, which is the point of practice.

Organisation

Dashboard

View all your stats at a glance.

63Overall readiness

Summary

Strongest Resumes at 71. Weakest Coding at 54.

1,240

Students

512

Interviews

964

Resumes

318

Coding

Interviews431 of 512

58avg

431 completed · 81 missed

Strong ≥7034%

Moderate 40-6939%

Weak <4011%

Cancelled/Missed16%

Resumes964 resumes

71avg

964 uploaded

ATS-ready ≥7548%

Needs improvement 60-7429%

Poor <6023%

Needs a look

Suspected misconduct

of 431 interviews

Incomplete attempts

completed in under 10 minutes

Interviews without camera

camera off for the whole round

Low scoring candidates

under 40 on their last round

Top companies

#1Amazon118

#2Google96

#3Microsoft74

#4Adobe61

Summary cards across the organisation, with the risk counts underneath.

Go to the four numbers that need a person

The Needs a look card holds Suspected Misconduct, Interviews Without Camera, Incomplete Attempts and Low Scoring Candidates. These describe something happening rather than something measured, and each row links into its own list. Low Scoring Candidates belongs with the readiness work on this page. The other three are review queues, and Review flagged sessions covers how to work them.

Take these weekly. Left to the end of a season they stop being fixable.

Compare today against overall

Organisation Metrics puts Today next to Overall for interviews, completed, missed, average interview score, resumes uploaded and average resume score. This is the momentum read. A batch whose Today column is empty for a fortnight has disengaged, whatever the overall averages say.

Read the outcome bands, not just the means

Interview Outcomes splits the cohort into Strong Fit at 70 percent and above, Moderate Fit between 40 and 69, Weak Fit below 40, and Cancelled or Missed. Resume Outcomes splits into ATS Ready at 75 and above, Needs Improvement between 60 and 74, and Poor below 60. Two cohorts with the same average can have completely different shapes here, and the shape is what tells you where to put coaching.

Top
Interview
Strong Fit, 70% and above
Resume
ATS Ready, 75% and above
Middle
Interview
Moderate Fit, 40 to 69%
Resume
Needs Improvement, 60 to 74%
Bottom
Interview
Weak Fit, below 40%
Resume
Poor, below 60%
Not attempted
Interview
Cancelled / Missed, Pending
Resume
No resume uploaded

Band	Interview	Resume
Top	Strong Fit, 70% and above	ATS Ready, 75% and above
Middle	Moderate Fit, 40 to 69%	Needs Improvement, 60 to 74%
Bottom	Weak Fit, below 40%	Poor, below 60%
Not attempted	Cancelled / Missed, Pending	No resume uploaded

Check who is actually turning up

Distinct Users counts the individual students behind the interview, resume and Prompt Engineering totals. It is the number that catches a cohort where twenty keen students are generating most of the activity and the rest have not started. Interview Durations tells you the same thing from the other side, in completed counts and total time.

Drill into the individual students

Interview Performance lists every attempt by student, company, job title, round type, score, date and status, filterable by name, registration number, date range, score range and sort order. Resume Optimisation does the same for resumes, with a Source column showing whether the record came from an interview or a direct upload. Both export to CSV, and both have a Send Notifications action for nudging the students you have just filtered down to.

Interview Performance

View performance statistics of past interviews.

Reg No / NameJob TitleScore

Temsu Jamir

22BCE1042

Associate Product Manager 2026

Hr Round

Aaron Menezes

22BCE1187

Content Strategist

Communication

Neena Momin

22BEC0931

Backend Engineer (SDE-1)

System Design

James Fernandes

22BCE1355

Backend Engineer (SDE-1)

Coding

Mercy Lyngdoh

22BME0620

Data Analyst Intern

General Interview

Franklin Marak

22BCE1409

Product Manager

Problem Solving Or Aptitude

Naomi Sangma

22BCS0788

Data Analyst Intern

Hr Round

Lalrin Chhangte

22BCE1512

Product Manager

Coding

Merenla Imchen

22BEC1104

Backend Engineer (SDE-1)

General Interview

Ryan Dsouza

22BME0455

Content Strategist

Communication

Esther Ralte

22BCS1220

Associate Product Manager 2026

Hr Round

Daniel Pereira

22BCE1788

Backend Engineer (SDE-1)

Coding

Imliakum Ao

22BEC0455

Data Analyst Intern

Problem Solving Or Aptitude

Showing 1-13 of 512

Every attempt by student, role, round type and status, with the report behind each row.

Check AI readiness separately

Prompt Engineering reports on its own scale. The Readiness Distribution puts every student in one of four bands, Beginner, Emerging, Job Ready or Advanced, with a count and a percentage each. Target the Beginner band directly rather than treating the whole batch as one group.

Assessments

Prompt Engineering

Assess your AI prompting and problem-solving skills through real-world tasks.

Total Sessions

486

Completed

412

Average Score

64/100

Total Attempts

1,904

Readiness Distribution

Beginner

23%

Emerging

140

34%

Job Ready

129

31%

Advanced

12%

Assessment Sessions (486 total)

Search name, reg no, or emailAll statuses All bands

Name / Reg NoAssessmentScore

Temsu Jamir

22BCE1042

AI-Powered Development

Job Ready

Aaron Menezes

22BCE1187

AI for Professional Communication

Emerging

Neena Momin

22BEC0931

AI for Data Analysts

Advanced

James Fernandes

22BCE1355

AI for Frontend Development

Beginner

Mercy Lyngdoh

22BME0620

AI for Product Managers

Job Ready

Franklin Marak

22BCE1409

AI for Business Intelligence

Emerging

Naomi Sangma

22BCS0788

AI for Software Engineers

Advanced

The four readiness bands, and the sessions behind them by student.

Export for the review meeting

Every table has a Download CSV action, and the Student Directory exports the whole batch with attempt counts, averages and last login. Take the numbers into your weekly review rather than describing them from memory.

What you get

At the end of this guide.

Cohort averages for communication, interview, resume and technical performance.

Counts for suspected misconduct, interviews without camera and incomplete attempts.

A today against overall momentum read.

Interview and resume outcomes split into named bands.

Per student attempt history, filterable and exportable to CSV.

A four band readiness distribution for AI prompting.

Common mistakes

What goes wrong, and the fix.

Judging the batch on the average interview score alone.

Read the outcome bands. Two cohorts with the same mean can have very different shapes.

Treating Interviews Without Camera as misconduct.

It is usually a device or briefing problem. Check whether it clusters in one branch first.

Ignoring Distinct Users.

Totals can be produced by a keen minority. Distinct Users tells you how much of the batch is really active.

Starting readiness work in the month before the drive.

The numbers only move with repeated practice. Start at the top of the season.

FAQ

Questions people ask on this one.

Still stuck? Talk to us and we will walk through your setup.

Related guides.

Integrity

Review flagged sessions

Work the three integrity lists on the Needs a look card: misconduct records, incomplete attempts and interviews without a camera. A review queue, not a verdict.

6 min read

Campus

Onboard a student batch

Send the admin list, get your placement team signed in, invite the batch, then set the companies and roles your students practise against.

6 min read

Practice

Run your first mock interview

Pick a company, set the round type and difficulty, upload your resume, book a slot, sit the interview, then read the report tab by tab.

6 min read

Practice

Get feedback on your resume

Target your resume at a company and role, read the ATS readiness breakdown, close the missing keywords, and re-run until you clear 70 percent.

5 min read

All guides

Try this in your own workspace.

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