Platform Architecture
A fully serverless, AI-ready clinical trial management platform built on proven technology with enterprise-grade security and compliance.
Platform Architecture
A fully serverless, AI-ready clinical trial management platform built on proven technology with enterprise-grade security, compliance, and modern analytics — designed for scalability and seamless integration.
Zero maintenance overhead with fully managed services. Built on AWS cloud infrastructure with automatic scaling. Start small and scale seamlessly as your trials grow without infrastructure management.
- Zero maintenance overhead with fully managed services
- Built on AWS cloud infrastructure with automatic scaling
- Start small and scale seamlessly as your trials grow
- No infrastructure management required
- Dedicated single-tenant stack per client, in your chosen AWS region
- Uptime SLA available with managed plans
The Third Data Layer
Transactional systems answer what is happening now. Analytical systems answer what happened at scale. The temporal knowledge graph answers what is connected, how it evolved, and why.
Transactional
"What is happening now?"
The operational databases the trial runs on — visits, forms, consents, queries — optimized for the current state of every record.
- Current subject status
- Open queries and tasks
- Live operational lookups
Analytical
"What happened at scale?"
The warehouse and governed semantic layer — aggregates, trends, and KPIs computed from centrally defined metrics.
- Enrollment rates and trends
- Governed KPIs
- Cross-study aggregates
Temporal Knowledge Graph
"What is connected, how did it evolve, and why?"
The third layer: entities and relationships with time on every edge — the connective tissue the first two layers cannot express.
- Amendment provenance
- Affected sites and activities
- Before/after history of any change
A Living Graph from the Audit Trail
Business events stream from the clinical system into Graphiti, which extracts and resolves entities and relationships over time into a FalkorDB temporal knowledge graph — served to AI assistants through MCP.
Clinical System
CTMS / EDC transactions
Business Events
Stream / CDC from the audit trail
Graphiti
Entity & relationship extraction, resolution, temporal processing
FalkorDB
Temporal knowledge graph, queried in Cypher
MCP Services
Fixed-shape graph tools over MCP
AI Assistant
Answers with provenance
Every edge in the graph carries valid-from / valid-to — the graph remembers what was true, when.
The auditor's question
“Explain everything around Amendment 4 — who requested it, who approved it, which sites were affected, what happened after.”
Designed to be answered in one graph traversal — instead of days of joining audit tables.
A Two-Tier Ontology — Designed, Not Inferred
The graph is built on a deliberately designed clinical ontology: first-class trial entities in Tier 1, and process entities the graph layer promotes in Tier 2.
Tier 1
First-class trial entities
Tier 2
Promoted by the graph layer
CDISC SDTM/ODM exports are modeled as export-artifact nodes with derived-from provenance edges — records of what was exported and where it came from, not business entities.
“AI can help populate and evolve the graph. The enterprise owns the ontology.”
The governance principle behind the knowledge engine.
Reference Architecture
Two parallel paths from the clinical applications — governed numbers through the analytical path, relationships and time through the graph path — converging at the MCP layer that serves the assistant.
Clinical Applications
The systems the trial already runs on
Analytical Path — Governed Numbers
Transactional DB
Operational records
Warehouse
Modeled, historized data
Semantic Layer
Centrally governed KPIs
Graph Path — Relationships + Time
Business Events
Stream / CDC
Graphiti
Extraction, resolution, temporal processing
FalkorDB
Temporal knowledge graph
MCP Layer
Both paths served through one protocol
AI Assistant
Agents run on Amazon Bedrock (AWS)
Enterprise-Grade Capabilities
Built on modern cloud infrastructure with security, compliance, and scalability at its core
100% Serverless
Zero maintenance overhead with fully managed services. Start small and scale seamlessly as your trials grow.
Rock-Solid Foundation
Built on Frappe and ERPNext—the same platform powering Zerodha, India's leading fintech company.
Healthcare-Grade Security
SSO/OpenID Connect authentication, fine-grained role-based access control, and audit logging.
Optimized Performance
PostgreSQL, optional Redis caching, and S3-compatible storage for lightning-fast operations.
Multi-Platform UI
React/Next.js web app, native iOS/Android apps, and mobile-optimized interfaces.
Built-In Lakehouse
Modern dbt-powered analytics with PostgreSQL. Supports Snowflake and Databricks.
API-First Design
100+ documented REST APIs. Use as headless CTMS with standards-based integration.
AI-Ready Platform
Includes MCP server for building agentic AI applications and intelligent workflows.
Dedicated Deployment
A dedicated single-tenant stack per client, deployed in the client's chosen AWS region.
Clean Architecture
Separation of concerns with well-defined layers for maintainability and extensibility.
Standards-Based
FHIR-ready, HL7 compatible, and follows industry best practices for interoperability.
Rapid Deployment
Cloud-native design enables quick deployment and instant scaling for your trials.
API-First Architecture
Comprehensive REST APIs with detailed documentation for seamless integration and extension
Complete API Documentation
Interactive API reference with code examples in multiple languages
100+ REST Endpoints
Comprehensive coverage of all platform features and data access
Secure & Authenticated
OAuth 2.0, API keys, and role-based access control
Headless CTMS Mode
Use Zynomi as a backend for your custom applications
Ready to Experience Modern CTMS?
Discover how Zynomi's architecture delivers security, scalability, and speed for your clinical trials.