AI for Education: Admissions, Exams and Learning for Global Institutions

Universities, autonomous colleges, K-12 school groups, and EdTech platforms. Each module is designed to drop into existing student information systems, learning management systems, and admission portals, not replace them.

Modules are independent. Most institutions start with one or two priority modules (commonly Admission Document Verification or Exam Evaluation) and add the rest as ROI is proven. Each module has its own onboarding, pricing, and rollout timeline.

Every module is built privacy-first. Data is processed in your chosen region, role-based access is enforced for officers and faculty, audit logs are retained for compliance, and consent is captured for every processing purpose. Compatible with DPDPA (India), GDPR (EU / UK), and other data protection regimes.

DigiLocker and APAAR for verification, common SIS platforms (Camu, Linways, Tata Edge, custom Oracle/PeopleSoft instances), LMS platforms (Moodle, Canvas, Blackboard), and standard SSO providers. Custom integrations are scoped during onboarding.

No. The platform is designed as a force multiplier. Officers approve verifications, evaluators review and override AI scores, and faculty mentors stay in the loop for student progression. AI handles the high-volume, repetitive work so people can focus on judgment calls.

Document Verification and Exam Evaluation MVPs ship in 2 to 4 weeks once integration credentials are in place. Larger deployments across multiple departments or campuses typically run a 6 to 8 week pilot before full rollout.

QverLabs is aligned with the scores given by leading human examiners. It employs recognition of images models that are trained on various kinds of handwriting, diagrams, and math symbols. It also adheres to very strict standards consistent with the institution’s marking policies.

QverLabs believes in transparent grading. Detailed, reviewable transcripts explain marks given according to pre-set rubrics. In case of discrepancies, certified faculty members can review and approve final evaluations. The system builds a fast digital record of all actions. When a student asks for a review, the Exam Cell can produce a detailed report. This report shows the student’s handwritten answer, the master rubric, and the points given for each step.

The DPDPA 2023 ensures that student data belongs to the institution. It is never allowed to train public models and is handled by the university’s rules on keeping or deleting records.

Yes. QverLabs enables multilingual OCR and grading. It can read English, Hindi and major regional languages spoken in India.

Visuals like circuit diagrams, chemical structures, detailed calculations, etc are studied with the use of vision models. They match it with step-by-step answer guides and reference visuals to check for accuracy.

Training faculty takes less than one hour. Evaluators use a straightforward browser tool that displays the scanned answer sheet on one side and the AI mark suggestions on the other side.

Campus centers need basic tools like high-speed document scanners with automatic feeders and simple computers connected to the internet for faculty to review.

The platform uses a method called Rubric-Bounded Decoding. It keeps the model on the syllabus and answer key so that it doesn’t add any extra criteria or give marks randomly.

Yes. QverLabs provides secure webhooks and RESTful APIs to integrate Student Information Systems and custom ERPs.

Exam Controllers can define global rules to award grace marks or change batch results after faculty members have completed their evaluations.