AURA, SAGE, and NOVA are domain-trained AI models - each built exclusively for one industry's data, rules, and operational language. They are the intelligence layer that makes every VelorQ agent accurate, compliant, and trusted.
AURA is trained on NAAC, UGC, AICTE, NMC, and DCI regulatory frameworks, Bloom's taxonomy, Indian curriculum standards, pedagogical research, and the operational data from UpLeap's TerraLeap platform serving thousands of educators and students across India.
When AURA creates a question paper, it understands learning objectives - not just content. When it generates a NAAC report, it knows what assessors look for. When it tutors a student, it detects which concept is the root of confusion and adapts its explanation accordingly.
Generic AI generates questions about a topic. AURA generates questions at the right difficulty level, mapped to specific learning objectives, with balanced cognitive distribution across Bloom's taxonomy levels.
AURA is not trained on the NAAC handbook alone - it is trained on the pattern of what assessors look for in practice, what evidence convinces committees, and what gaps most commonly cause cycle failures.
When a student answers incorrectly, AURA analyses the error pattern to identify which specific misconception is driving the mistake - and addresses that, not the surface symptom.
AURA's multilingual capability preserves educational terminology accurately - not generic translation. A concept explained in Tamil or Telugu retains its pedagogical precision.
Six architectural isolation layers ensure every student's learning data is processed exclusively inside your institution's private environment. No exceptions.
Through the KCL Protocol, AURA receives anonymised pedagogical signals - which teaching approaches work for which learning profiles - making every new cohort benefit from previous experience.
SAGE is trained on NABH and NABL standards, JCI accreditation frameworks, HIPAA regulations, ICD-11 and SNOMED CT clinical terminology, HL7 FHIR data structures, and operational data from UpLeap's ClinicBeyond and AccreWise platforms running inside real hospitals across India.
When SAGE generates an accreditation document, it knows what inspectors actually check. When it surfaces a clinical protocol, it understands severity. When it answers a patient query, it uses medically accurate language - not AI approximations that could mislead someone about their health.
SAGE is trained on the operational pattern of NABH assessments - not just the standards document. It understands which evidence is compelling, which gaps are most commonly flagged, and what documentation formats pass inspection.
When SAGE reads a patient record, it understands the clinical significance - a deteriorating trend, a dangerous combination, a protocol deviation - and acts accordingly, not generically.
Patient queries answered by SAGE use medically accurate, professionally appropriate language - not consumer-AI approximations. The difference between 'take it easy' and a specific clinical instruction matters when someone is making health decisions.
SAGE's knowledge comes from UpLeap's ClinicBeyond and AccreWise platforms - software running in real hospitals managing real accreditation cycles and real clinical workflows.
Every patient record and clinical note is processed within your hospital's private environment. Six architectural barriers ensure no patient data can reach any other customer.
Through the KCL Protocol, safety signals detected by NOVA in pharmaceutical data are translated into clinical guidance signals for SAGE at the point of care.
NOVA is trained on CDSCO, FDA 21 CFR, and EMA guidelines, ICH E2A through E2F safety reporting standards, MedDRA medical terminology, eCTD v4.0 submission structure, GMP and GLP requirements, ICH-GCP principles, and deep operational knowledge of global regulatory programmes.
When NOVA identifies a gap in a submission dossier, it knows which section, which data requirement, and which authority's format is missing. When SafeWatch flags a signal, NOVA's MedDRA knowledge ensures the coding is accurate - not approximate. Approximate is not acceptable in drug safety.
NOVA does not produce a generic regulatory summary. It knows CDSCO's specific formatting requirements, FDA's section hierarchy, and EMA's data expectations - and compiles your dossier in the exact format each authority expects.
Adverse event MedDRA coding requires expert judgement about the most appropriate term at the right hierarchy level. NOVA's training on real pharmacovigilance data means its coding decisions match what an experienced safety scientist would produce.
NOVA is trained on multi-sponsor, multi-study operations - understanding the data separation, sponsor confidentiality, and parallel regulatory obligations that CROs manage across their portfolio simultaneously.
NOVA is trained on the pattern of what passes and what fails across health authority submissions - not just the published guidelines. This operational intelligence makes RegIntel faster and more accurate than manual preparation.
Every sponsor's dossiers and safety data are processed in complete isolation - six architectural layers ensure sponsor A's data cannot reach sponsor B's regulatory programme, by design.
Drug safety signals detected by NOVA - potential interactions, emerging adverse event patterns - are anonymised and shared with SAGE through the KCL Protocol, making clinical decision support smarter.
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