The AI-native end-to-end examination grading platform

Don't mark by hand, just review

Qdemy reads handwritten or typed answer scripts and turns them into scored, annotated, publish-ready results — to your rubric, in every subject.

Try it freeBook a demo
20,000+ students evaluated · DPDPA compliant
Scanned script · Q2
For a rod of varying density λ(x), moment of inertia is I = ML²/12 I = ∫ x²λ(x) dx taken from 0 to L… evaluating for λ(x) = kx gives I = kL⁴/4 = ML²/2
strike-through detected · rough work isolated
Graded · anon-4102
8.5 / 10
Correct integral form for variable density3 / 3
Evaluation for λ(x) = kx4 / 4
Final substitution — sign error in step 31.5 / 3
Feedback note drafted · annotated PDF ready
Trusted by institutions grading at scale
Sharda UniversitySharda University
300papers/hour
Noida International UniversityNoida International University
99%OCR accuracy
DPIIT#startupindia
DPDPA Compliant
Microsoft for StartupsMicrosoft for Startups
20K+students evaluated
Sharda UniversitySharda University
300papers/hour
Noida International UniversityNoida International University
99%OCR accuracy
DPIIT#startupindia
DPDPA Compliant
Microsoft for StartupsMicrosoft for Startups
20K+students evaluated

50× faster than marking by hand

Every script marked to the same standard — the first booklet and the five-hundredth, at 9 am or 2 am. No fatigue, no drift.

Marking by hand
6 scripts/hr

Six weeks of evaluation camps. Standards drifting between the first pile and the last. Re-totalling errors, transcription slips, and a backlog that outlives the term — while students wait for results that arrive too late to matter.

With Qdemy
300 scripts/hr

The 5-stage pipeline — OCR, census, strike detection, rough-work isolation, rubric scoring — turns a full batch around while the term is still fresh. Every answer scored against your rubric, with a reasoning trace and an annotated PDF, in under a minute per paper.

Made for the way your institution works

Select one to see how Qdemy fits.

Universities illustration

Qdemy for Universities

End-semester evaluation for 20,000 scripts without the six-week backlog. One rubric, one standard, every department — results published while the term is still fresh.

Full lifecycle: rubric design to results publication
AI evaluation + human on-screen marking in one platform
Gold-set calibration matches your examiners’ style
Session · Winter 2026
Scripts uploaded18,442
AI-evaluated17,980
Flagged for review462
Days to publish6
Exam boards illustration

Qdemy for Exam boards

Consistent in Delhi, Dubai and Detroit. The same rubric applied at every centre, with anonymization and a complete audit trail for every mark.

One standard across all centres and shifts
Anonymous IDs until publish — no grader bias
Every decision logged with timestamp and actor
Moderation panel
Centres34
Inter-centre drift< 1.2%
Escalated to Chief Examiner128
Audit events2.1M
Coaching institutes illustration

Qdemy for Coaching institutes

Weekly test series marked overnight. Every student gets an annotated PDF with criteria-level feedback before the next class — not three weeks later.

Full batch turned around overnight
Annotated PDFs pushed to every student
Strictness dialled to your score distribution
Test series · Batch 14
Papers in batch3,200
Turnaround9 hrs
Feedback notes drafted3,200
Re-checks requested11
Educators illustration

Qdemy for Educators

Mark on screen with stamps, pen and highlights — or command the co-pilot in plain English and review its before/after diffs. Your expertise, minus the drudgery.

Full annotation toolkit, stored as structured data
Co-pilot takes real actions on your command
Low-confidence answers surfaced automatically
On-screen marking
Assigned scripts240
Marked today212
Low-confidence flags8
Double-marked100%
Students illustration

Qdemy for Students

Your own script back in days — annotated, with a score you can trace to the rubric and feedback that tells you exactly where the marks went.

Annotated copy of your actual script
Criteria-level justification per question
Results in days, not weeks
Scorecard · anon-4102
Physics II82 / 100
Q2 · method8.5 / 10
Feedback notes14
Published in5 days
AI grading co-pilot

Don't just get suggestions. Give commands.

Ask it anything in plain English — fix a model answer, balance a rubric, re-score with partial credit. It proposes a concrete before/after change you accept or discard. No prompt engineering, no context-switching.

Try it yourself
Re-score Q7 with partial credit for the correct DFS traversal
I can do that — traversal order would earn 2 of 5 marks even when the final path is wrong. Here's the diff:
Proposed changeQ07 · scoring
Full marks only for complete correct path
+2 marks: correct DFS traversal order · +3 marks: correct final path
AcceptDiscard
✓ 14 papers re-scored · feedback notes updated
Evaluation engine

Score 300 papers in under an hour

The 5-stage pipeline reads every handwriting style, detects struck-through edits and rough work, aligns each answer to the rubric, applies scoring criteria, and emits an annotated PDF — automatically, for every paper.

Full run history — every attempt preserved; re-run with a new rubric or strictness
Strictness per subject — dial in to your expected score distribution
Low-confidence flags — surfaced with the AI's reasoning trace for human review
Live · evaluating anon-1045OCR ✓ · Normalize ✓ · Score ●
68%scored
Now: Score — applying rubric criteria
5 / 8 questions scored
0m 28s
elapsed
Event log
ScoreQ04 scored · 8 / 10
ScoreQ03 scored · 7 / 8
Resolve2 ambiguous regions resolved
OCRReading 6 pages · 1 strike detected
Score so far
26of 60
Applying rubric criteria…

Rubric designer

AI extracts sections, questions and Bloom's tags from the question paper. Marks reconcile live — catch mismatches before you freeze a version.

Q1 · Define entropy5 mk
Q2 · Derive I for λ(x)10 mk
Q3 · Elastic vs inelastic5 mk
✓ Reconciles to 100 / 100

Every pen stroke, understood

From clean print to difficult cursive — 99% OCR accuracy, with strike-throughs and rough work detected automatically.

Handwritten answer sample
→ transcribed · confidence 0.99

STEM-grade, natively

LaTeX in rubrics and model answers, diagram annotation against references, graph recognition with partial marks for correct structure.

100 FAP3'3'
I = ∫₀ᴸ x²·λ(x) dx
✓ diagram matched to reference · ✓ axes · ✓ vector direction

Every student gets an annotated result

A fully annotated copy of their own script — scores stamped per question, feedback notes, and criteria-level justification. Not just a number.

8.5/10
"Sign error in the final substitution — method fully correct. See the worked step in the margin."

Human expertise, digital precision

On-screen marking for human examiners — stamps, freehand pen, highlights and comments, all stored as structured data. Double marking with Chief Examiner moderation built in.

✓ Tick✗ Cross✎ Pen▬ Highlight💬 CommentDouble marking
50×
faster than manual grading
99%
OCR accuracy, all handwriting
300
papers scored per hour
20K+
students evaluated in production

Your data never leaves your control

Enterprise-grade protection, anonymization and audit trails — compliant with DPDPA 2023.

Full anonymization

Evaluators — human or AI — see only ANON-XXXX IDs. Real identity is reconciled only at publish, enforced at the data layer.

Complete audit trail

Every AI decision, override and version freeze is logged with timestamp and actor. Nothing is ever overwritten.

Fully compliant

Fully compliant with DPDPA and global data security and privacy regulations — PII encrypted at rest and in transit.

Accolades to Qdemy

"Six weeks of evaluation camps became six days. Faculty reviewed flags instead of marking piles."

Controller of Examinations
Partner university

"The co-pilot fixed a broken model answer across 400 already-graded papers in one command. That sold me."

Head of Department, Physics
Partner university

"We calibrated against 40 human-graded papers and the agreement rate has stayed above 91% ever since."

Chief Examiner
Examination board

"Students stopped filing re-check requests once they could see exactly where every mark came from."

Exam cell administrator
Coaching institute

"It read handwriting our own examiners argued about — and showed its reasoning for the score."

Senior examiner
On-screen marking pilot

"Anonymized marking with a full audit trail was the requirement. Qdemy was the only platform that had both built in."

Dean of Academics
Partner university

Common questions

What is Qdemy and who is it for?

An end-to-end examination intelligence platform for universities, boards, and large coaching institutions — rubric design, AI evaluation, on-screen human marking, and results publication for handwritten exams at scale, from a few hundred to 20,000+ students per session.

Does it support both AI and human on-screen marking?

Yes — both modes coexist. AI evaluation runs the full 5-stage pipeline in minutes; On-Screen Marking routes the same scanned scripts to human examiners. Use one mode per subject or combine both with a moderation step.

How accurate is the AI evaluation?

Over 99% OCR accuracy on standard ruled booklets and AI–human agreement above 91% after calibration. Upload 30–50 human-graded papers and the platform tunes its strictness (γ) to match your examiners’ grading style.

How does it handle partial credit and complex rubrics?

Each question is broken into scoring components with their own marks and model answers. The AI scores each independently, reasoning about partial demonstrations of knowledge rather than requiring an exact match.

What happens when the AI flags a low-confidence answer?

The evaluation cockpit surfaces it with a warning. An evaluator reviews the AI’s reasoning trace and accepts or overrides the score with a note — every override lands in the audit trail.

Does it support LaTeX, diagrams, and graphs?

Natively. LaTeX renders in rubrics and model answers, diagram annotation checks student drawings against labelled references, and graph recognition detects axes, curves and data points for partial marks.

How does anonymization work?

Every student gets an Anonymous ID at session start. Evaluators — human or AI — never see names or roll numbers. The mapping is stored separately and revealed only after results are published, enforced at the data layer.

Is it DPDPA compliant?

Yes. Built for India’s Digital Personal Data Protection Act: PII encrypted at rest and in transit, scripts never leave the platform unless a COE administrator exports them, and every action carries an audit trail.

How long does onboarding take?

Most institutions are live within two weeks: week one covers setup, booklet calibration and a pilot rubric; week two is a dry run on real scripts. After sign-off, sessions run without QverLabs engineers in the loop.

Start grading smarter

A 30-minute demo: we walk through your current workflow, show the platform live, and scope setup for your institution. Most are live within two weeks.

Try it freeBook a demo
Free tryout for educators at grade.qdemy.ai · No commitment · Typical setup: 2 weeks