What about messy handwriting? Will it misread students?
Our hybrid OCR + vision pipeline is built for Indian handwriting styles and messy scans.
Independent evaluations put AI–human agreement at ~95% on descriptive answers — and higher
when teachers calibrate the rubric on a sample first. Crucially, nothing is final until a
teacher signs off — a misread mark is caught and corrected in seconds, not weeks.
Does this replace our teachers?
No. It removes ~90% of the correction time that is re-reading, re-totalling and writing the same
comments — turning ~20–25 minutes per script into ~2–3 minutes of review. Teachers keep full
authority over every mark and gain time for actual teaching.
We already use MyCamu / Entab. Do we need new software?
No. We integrate with your existing ERP and student app through REST APIs and webhooks. Teachers
and students stay in the tools they already use.
Do we need to buy scanners or new hardware?
No. We work with the ADF scanners campuses already own — Canon, Fujitsu, Epson — via standard
TWAIN/WIA drivers, with automatic deskew, contrast and blank-page discarding.
How does your pricing work?
Per enrolled student, per month, billed for the academic year. Schools pay ₹75–100 per
student/month and universities slightly more; the per-student rate drops as total student
strength grows. You only start paying after a free 100-paper pilot confirms the value on
your own scripts.
Where is student data stored?
On Indian domestic cloud infrastructure (E2E Networks / AIC Cloud), encrypted in transit and at
rest, and aligned with DPDP 2023 requirements. We can also run fully on-prem for sensitive campuses.
Will this survive re-evaluation disputes?
Yes. Every tick, score and override is permanently flattened into the PDF at sign-off, creating a
physical-equivalent audit trail that stands up in re-evaluation and legal review.
How is this different from pasting papers into ChatGPT or Gemini?
ChatGPT and Gemini are general-purpose chatbots: paste a paper and you get a guess — no rubric, no
per-question mark breakdown, no double-blind masking, and nothing you could defend in court. GradingAI
is built around the exam office: a rubric engine that shows the reason for every mark, teacher
sign-off, a flattened pen-mark PDF for disputes, and ERP integration. It's a grading system, not a
chat window.
What's your moat? What stops someone copying you?
Anyone can build a vision model. The moat is the feedback loop: every mark your teachers approve or
override re-calibrates our rubric to your syllabus, your past papers and your students' handwriting.
Accuracy compounds with every exam we grade for you, and a competitor starting today has zero grading
history on your campus. Switching away means throwing away that calibrated accuracy.
Why is the feedback loop so important?
Grades alone are static — the loop is what compounds value. A corrected paper becomes a diagnostic
report, which pinpoints the exact sub-topics a student failed, which drives targeted teaching, which
shows up in the next exam — and every step gives the AI cleaner signal. It turns a one-time correction
service into an asset that gets smarter every term.