Our Solutions

InductiveEDC is designed to modernize how studies are built, validated, and prepared for downstream use. Through dynamic CDASH-compliant form design, edit check programming, validation logic, and AI-enabled auto validation, the platform helps teams reduce manual effort while improving consistency and control.

With standards-aligned workflows and support for SDTM-ready dataset generation with minimal manual intervention, InductiveEDC helps sponsors and CROs improve data quality earlier, reduce downstream rework, and accelerate database lock with greater confidence.

RBQM is Inductive Quotient’s clinical oversight intelligence platform, designed to give clinical operations, medical monitoring, and data management teams a unified, near real-time view of study quality and performance.

It brings together key operational, quality, and risk signals, including enrollment, data quality, protocol compliance, safety, deviations, and site performance - into a centralized environment that supports faster, more informed decision-making.

Aligned with the ICH E6(R2) RBQM framework and designed for evolving E6(R3) expectations, the platform helps teams detect emerging risks earlier, monitor KRIs and QTLs continuously, and take timely action across study and portfolio levels.

Prabandhan is Inductive Quotient’s unified oversight platform for clinical programs, designed to bring study, vendor, regulatory, milestone, and financial tracking into one connected environment. It helps teams manage operational complexity across studies with better visibility, stronger coordination, and more proactive control.

The platform also extends into advanced use cases such as meta-analysis across studies, including PK/PD parameter analysis, while Agentic AI capabilities help extract and dynamically populate study-level information to accelerate execution and improve consistency.

TrialLattice is a connected collaboration platform that brings sponsors, CROs, and clinical sites into one structured environment for site identification, feasibility, startup, and activation.

It helps teams manage qualification workflows, essential document exchange, communication, startup milestones, and site activation tracking with greater visibility and control. By reducing fragmented emails, disconnected trackers, and manual handoffs, TrialLattice helps accelerate startup timelines and improve execution across studies and regions.

Built for modern trial teams, the platform creates a more coordinated ecosystem where stakeholders can collaborate efficiently, respond faster, and move sites toward activation with less operational friction.

Clinevra is an AI-powered Protocol Copilot that transforms protocol documents into structured, review-ready clinical assets.

It ingests protocol content from PDF, Word, and connected sources, then extracts key clinical, statistical, and operational elements through a governed workflow. These outputs can be converted into study details, schedules of assessments, trial domains, specifications, metadata, and protocol-driven downstream assets.

With review, approval, and traceability built into the process, Clinevra helps reduce manual interpretation effort, improve consistency, and support metadata reuse across the clinical data lifecycle.

OverviewCDQOps is Inductive Quotient’s AI-powered clinical data quality and validation platform designed to improve the speed, consistency, and control of study data review. It helps teams identify data issues early, automate validation workflows, and strengthen oversight across clinical data management activities.

Built to support modern study execution, CDQOps streamlines edit check management, discrepancy identification, reconciliation support, and data review workflows through intelligent automation and configurable quality rules. By reducing manual effort and improving traceability, it helps clinical teams accelerate data cleaning, improve data reliability, and move faster toward database lock.

Aushadhi is an AI-powered medical coding platform built to modernize the coding lifecycle across clinical trials and pharmacovigilance workflows. It helps teams process verbatim terms faster, improve coding consistency, and reduce manual effort through intelligent automation and governed review workflows.

Using AI, NLP, and contextual learning, Aushadhi generates high-confidence coding suggestions for MedDRA and WHO Drug while supporting study- and sponsor-specific coding rules. This helps reduce variability, minimize rework, and improve coding quality across studies and programs.

From intake through approval, Aushadhi supports role-based review, decision traceability, and controlled workflow execution. With interoperability across EDC, CTMS, and safety systems, it helps teams accelerate review cycles, support audit readiness, and improve downstream efficiency.

Evidexia is a real-world evidence platform designed to transform fragmented real-world data into analysis-ready, decision-grade evidence. It supports the full RWE lifecycle-from multi-source data ingestion and harmonization through cohort design, analytics, and evidence generation.

The platform maps data from EHRs, claims, registries, and patient-generated sources into an OMOP-aligned model, enabling more consistent analytics across heterogeneous datasets. Its visual cohort builder helps teams define study populations quickly, evaluate feasibility in real time, and refine study criteria with greater confidence.

Evidexia supports a broad range of RWE study designs, including retrospective cohort studies, case-control studies, interrupted time series, and target trial emulation. With advanced statistical methods and regulatory-aligned workflows, it helps teams generate evidence for regulatory, HTA, market access, and commercial decision-making.