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MET-Ai Platform

How It Works

A complete walkthrough of the MET-Ai architecture, from data ingestion to clinical action, deployed and live within six weeks.

Platform Architecture
6 wks
To Deployment
Under 2s
AI Response
99.9%
Platform Uptime
The Architecture

How MET-Ai Works

A four-stage intelligent architecture that ingests data from every source and delivers AI-powered clinical decisions at every point of care.

1. Data Ingestion

Real-time collection from records, sensors, laboratories, imaging, and wearables.

2. Data Fusion

Structured and unstructured data unified into one comprehensive patient model.

3. AI Analysis

Specialised models analyse every dimension for diagnosis, risk, and recommendation.

4. Clinical Action

Insights delivered as alerts, reports, and automated workflows to the right person.

Working With Your Existing Systems

MET-Ai does not require the replacement of existing hospital systems. The API-first platform integrates with all major electronic record systems, imaging archives, and laboratory systems through open healthcare standards.

Pre-built connectors for widely used Indian hospital information systems mean your clinical team accesses MET-Ai's intelligence within the workflows they already know.

  • Bidirectional integration through HL7 FHIR and HL7 v2
  • A DICOM-compatible medical imaging pipeline
  • Automatic, bidirectional synchronisation of the clinical record
  • Pre-built connectors for leading Indian hospital systems
Universal Integration
Deployment Timeline

Live in Six Weeks

01
Weeks 1–2 · Discovery and Integration

A full assessment of your systems and workflows. Data pipelines are established and validated, and initial model calibration begins on your patient data.

02
Weeks 3–4 · Training and Parallel Running

Role-specific training for all staff. MET-Ai runs alongside existing systems so that every user can validate outputs and build confidence before go-live.

03
Weeks 5–6 · Go-Live with Support

Full deployment with on-site and remote support. System performance, alert accuracy, and adoption are monitored closely throughout the launch.

04
Ongoing · Continuous Improvement

Continuous retraining on your accumulating data, with quarterly performance reviews. The system grows more precise for your patient population every day.

See MET-Ai in Action

Contact Eeswar Makineni for a technical walkthrough tailored to your hospital's systems.

MediBot — MET-Ai Assistant
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