Intelligent Patient Recruitment
Mines EHR data — clinical notes, labs, imaging, diagnosis codes — to identify eligible patients across your network in hours.
Trusted by startups and global leaders
One Platform. Three Trial Problems Solved.
Recruitment, retention, and real-world evidence — the three most expensive failure points in clinical research, addressed by AI built for the pace modern trials demand.
Retention & Participant Engagement
Tracks dropout signals — missed check-ins, side effects, late visits — and flags at-risk participants to coordinators with next steps.
Real-World Evidence Generation
Aggregates EHRs, claims, registries, wearables, and pharmacy records into regulatory-grade cohorts for post-marketing studies.
Where Clinical Trials Lose Time and Money
Most trials fall behind not because the science fails, but because the right patients can't be found, enrolled, or retained fast enough for usable data.
How We Fix ThisWhat Clinical Trial AI Manages
Recruitment, retention, and real-world evidence — managed from a single AI platform built for trial sponsors, CROs, and academic medical centres.
What Your Trial Team Works With Every Day
Four purpose-built workspaces for the people running the trial — site coordinators, biostatisticians, regulatory teams, and trial leadership. Enough information to make the right decision in two minutes flat.
Site Performance Dashboard
Enrollment progress by site, accrual vs. projections, dropout rates, and predictive flags for underperforming sites — surfacing untapped recruitment potential before timelines slip.
Participant Engagement Monitor
Retention risk scores updated continuously, flagged participants with recommended interventions, and communication logs — showing which participants need attention before they drop out.
Real-World Evidence Workbench
Cohort building, data source integration, outcome analysis, and regulatory-grade documentation — everything biostatistics and regulatory teams need from label expansion to post-marketing surveillance.
Protocol Optimisation Analytics
Eligibility analysis, enrollment rate predictions, and site feasibility scores — available before the trial opens so amendments happen before enrollment, not because of it.
Unified Trial Intelligence Layer
Recruitment pipeline, retention scores, evidence status, and regulatory milestones in one live view — replacing disconnected spreadsheets and manual databases.
Clinical Trial AI: What the Numbers Showed.
Each metric ties to a real AI-driven trial outcome.
Book a Live Demo85%
Retention rate achieved using adaptive engagement and personalised communication — keeping enrolled patients in is the highest-return investment a sponsor can make.
16 vs 2
Eligible patients identified by AI in 1 hour versus 2 found manually over 6 months — the case for computational eligibility screening.
27.3%
CAGR of the AI patient recruitment and retention market through 2034 — operational trial problems now treated as seriously as scientific ones.
USD 13.23B
Projected AI clinical trial market by 2034, up from USD 2.48B in 2025 — driven by recognition that recruitment and retention are solvable with AI.
Weeks
Instead of years — AI-generated synthetic data now validates surrogate oncology endpoints, reshaping evidence generation at every stage of development.
85%
Reduction in pre-screening time via protocol feasibility analysis — enrollment rates predicted and criteria adjusted before a single patient is screened.
Who This Platform Serves
Built for every organisation running or sponsoring clinical research — from Phase II pharma programs to investigator-initiated trials at academic medical centres.
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Pharma & Biotech
Pharma & Biotech
Phase II and III recruitment timelines determine how quickly a molecule reaches approval. AI-powered recruitment and retention compresses those timelines — and scales across multi-trial portfolios without compromising data quality.
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CROs
CROs
Sponsors measure CROs on enrollment speed, data quality, and protocol adherence. AI tools that improve all three are a competitive differentiator — and the platform integrates with your existing trial management infrastructure.
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Academic Medical Centres
Academic Medical Centres
Academic investigators now access predictive eligibility screening and protocol feasibility analysis previously available only to large sponsors — without the resource overhead of industry-sponsored trials.
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Medical Device & Diagnostics
Medical Device & Diagnostics
Real-world evidence for post-market surveillance and performance evaluation, generated to regulatory standards — including CDSCO requirements for India-based trials and MDR compliance for international programs.
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Decentralised & Hybrid Trials
Decentralised & Hybrid Trials
Supports decentralised trial workflows — remote monitoring, wearable data integration, virtual visit scheduling, and digital consent — without requiring a separate DCT infrastructure.
Built on Standards Your Institution Already Operates Under
Integrates with your existing EHR, trial management, and data governance infrastructure — no parallel systems, no forced migrations.
Clinical Data
EHR & Clinical System Integration
FHIR-compliant APIs within your data governance framework.
Trial Management
Clinical Trial System Connectivity
Works alongside your existing CTMS, EDC, and eClinical platforms.
Real-World Data
Real-World Data Source Integration
Unified analysis layer for cohort building and evidence generation.
Compliance
Regulatory & Privacy Compliance
Compliance for pharma, biotech, and academic research orgs.
Analytics
Evidence & Reporting Layer
Audit-ready data lineage formatted for direct submission.
Engagement
Participant Engagement Infrastructure
Omnichannel communication, consent, and remote visit support.
The Right Patients. Enrolled Faster. Retained Longer. Evidence Generated at Scale.
The tools exist to find patients faster, keep them engaged, and generate regulator-accepted evidence — at a cost that makes more trials viable. Book a demo to see how it works for your program.
Book a Clinical Demo
Award-Winning AI Development & Consulting
100 Fastest Growth Companies
Global Spring Winner
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Google Cloud Partner
Highly Rated on Trustpilot
Verified Agency
Top App Development Company
ASSOCHAM Member
Frequently Asked Questions
[ 1 ]
How does the platform access patient data for recruitment screening?
The platform integrates with EHR systems through FHIR-compliant APIs, working within your institution's existing data governance framework. Patient data used for screening is processed under your institution's research data use agreements. No patient data leaves your environment without appropriate authorisation.
[ 2 ]
Can the AI identify patients who do not yet fully meet eligibility criteria but might in future?
Yes. Predictive eligibility screening is one of the platform's core capabilities. The AI identifies patients whose disease trajectory suggests they will meet criteria within a defined window, allowing sites to begin pre-screening conversations before formal eligibility is established — significantly reducing the time from identification to enrollment.
[ 3 ]
How does the retention risk scoring work in practice?
The model is trained on dropout patterns from historical trial data — what behavioural and clinical signals predict dropout before it happens. It monitors each enrolled participant's data continuously and generates a risk score that updates with every new data point. When a participant's score crosses a defined threshold, the platform notifies the site coordinator with a reason-coded alert and a suggested intervention.
[ 4 ]
Is the platform compliant with CDSCO requirements for India-based trials?
Yes. The platform supports CDSCO trial registration and reporting requirements, Schedule Y compliance documentation, and ethics committee documentation workflows for trials conducted in India. For global trials with Indian sites, it handles both local and international regulatory requirements within a single workflow.
[ 5 ]
Can we use this for investigator-initiated trials with limited IT infrastructure?
Yes. The platform is designed to work with real-world institutional IT environments, not just fully modernised ones. For investigator-initiated trials, we provide integration support for whatever clinical and administrative systems are in use — including partial or legacy setups — so AI-powered recruitment tools are accessible without a full EHR implementation as a prerequisite.
[ 6 ]
How quickly can enrollment improvement be seen after deployment?
Protocol feasibility analysis and predictive eligibility screening are available from day one. Sites running active trials typically see measurable changes in screening throughput within the first two to four weeks. Retention improvements become visible over the first full month as the engagement monitoring baseline is established for enrolled participants.
Global presence
