Tutorops turns student data, assessment evidence and recruiter requirements into measurable readiness, skill gaps and actionable interventions — for the placement office and for the student.
Scores every student against the requirements of a specific job role using transparent, deterministic scoring.
AvailableSee which skills sit below the recruiter requirement and how much each gap is costing the readiness score.
AvailableBuild assessments from a bank tagged by skill and difficulty. Students open a secure single-use link — no platform account to create.
AvailableCreate targeted preparation plans that reach the threshold, with the hours and projected score stated up front.
AvailableFilter eligible students, rank them by fit for the role and export an evidence-backed shortlist.
AvailableCompare readiness across departments, batches and roles, and find the gaps costing the cohort most.
AvailableUpload your student spreadsheet, let Tutorops match your column names, and review any problem rows before anything enters your workspace.
AvailableEvery readiness score traces back to the skills, weights and assessment evidence behind it.
AvailableEvery panel below is the engine's output on the demo college, generated when you loaded this page. Click a tab.
Software Engineer — Java, threshold 80. Every eligible student scored on the role's own weights.
Ranked by the readiness points the cohort recovers if the gap closes — not by how many students are weak.
| Skill | Students affected | Average shortfall | Points recoverable |
|---|---|---|---|
| REST API | 43 (79.6%) | 2.95 levels | 195 |
| Java | 29 (53.7%) | 3.6 levels | 181 |
| Object Oriented Programming | 22 (40.7%) | 4 levels | 152 |
| Speaking | 13 (24.1%) | 2.31 levels | 150 |
| SQL | 25 (46.3%) | 3.44 levels | 149 |
Eligibility rules first, then readiness ranks whoever is left.
Suresh Bose is 4.1 points short of 80. This plan reaches 84.5 in 44 hours.
Ordered by readiness points gained per training hour, so a cheap skill can be scheduled before a bigger one.
Each student counted against the best role they are eligible for.
Nothing here is a standalone module. Each step is the input to the next, which is why a shortlist can be traced back to an answer a student gave.
A TPO defending a shortlist should never have to say "the model chose them". Here is exactly where each side of that line sits today.
Deterministic arithmetic. The same student and role always produce the same number, and 1,899 automated checks hold it there.
Paste or upload a job description and Tutorops drafts the requirement list — skills, levels, criticality, CGPA and backlog rules — for you to review. Confirm it and every student is scored against the new role immediately.
Nothing is saved until a person confirms it, and the screen names which reader produced the draft. With an AI provider configured it uses the model, and the output is validated against a strict schema with malformed entries dropped. Without one it runs a deterministic scan against the skill taxonomy — less clever with unusual phrasing, and incapable of inventing a requirement that is not in the text.
AvailableRegister your college, upload your student list and see your first readiness picture in minutes.
Upload your student list. Run your first baseline. See who is ready, who is not, and why. Two minutes to set up, no credit card, no sales call.