🧬 CliniRepGen AI

Agentic, Distributed Intelligence for Clinical Study Report Compliance

🌍 Problem Statement

Clinical Study Reports (CSRs) are among the most complex documents in regulated science.
A single report must simultaneously comply with:

Health Canada (HC)
EMEA / EMA — ICH E3, E6(R3), E9(R1)
North America — FDA, 21 CFR, CDISC

Each authority enforces strict requirements on:

Document structure and completeness
Statistical validity and transparency
End-to-end traceability from protocol to conclusions
Consistency across text, tables, figures, and appendices

Today, compliance review is:

Manual and expert-driven
Slow (weeks to months)
Error-prone and expensive

A single missing estimand definition or inconsistent endpoint can delay approval by quarters.

🚀 Our Solution

CliniRepGen AI is an agentic, multimodal AI system that ingests Clinical Study Reports and automatically validates them against Canada, EMEA, and North American clinical standards.

The platform performs:

Deep regulatory research
Semantic cross-checking
Statistical integrity validation
Multiregional compliance harmonization

All in hours instead of weeks.

🏗️ Architecture Overview

(Referenced from the attached layered architecture diagram)

CliniRepGen AI follows a layered enterprise AI architecture, optimized for:

Distributed compute
Semantic embeddings
Multimodal reasoning
Agent-based decision-making

⚙️ Architecture Breakdown (Bottom → Top)

1. Distributed Cloud Infrastructure — Akash Network

The foundation of the system runs on Akash Network, providing:

Decentralized GPU/CPU compute
Containerized microservices
Cost-efficient scaling for large CSRs
Flexible, regulation-friendly deployment

This layer executes:

Embedding generation
Vector search workloads
Agent inference
Large-scale document parsing

2. Vector Database Cluster

All clinical artifacts are embedded and indexed in a distributed vector database.

Embedded assets include:

CSR sections (aligned to ICH E3)
Study protocols and amendments
Statistical Analysis Plans (SAPs)
Tables, Listings, and Figures (TLFs)
Regulatory guidance (HC, EMA, FDA)

Semantic similarity is computed via cosine similarity:

[ \text{Similarity}(x, y) = \frac{x \cdot y}{|x||y|} ]

This enables:

Guideline-to-section traceability
Detection of missing or weak compliance evidence
Cross-document consistency validation

3. Multimodal Embedding Pipeline

Clinical studies are inherently multimodal.

The pipeline processes:

📄 Narrative sections (methods, results, discussion)
📊 Statistical tables
📈 Figures (e.g., Kaplan–Meier plots)
📐 Mathematical expressions (hazard ratios, p-values, confidence intervals)

All modalities are projected into a shared embedding space, enabling reasoning such as:

Does the primary endpoint described in Section 10 match the statistical test defined in Section 9 and reported in Table 14.2.1?

4. Agentic AI Orchestration Layer

This layer represents the cognitive engine of CliniRepGen AI.

Specialized AI agents collaborate through decision graphs and feedback loops.

Key Agents

Regulatory Mapping Agent
Maps CSR content to ICH E3, HC, EMA, and FDA requirements
Statistical Integrity Agent
Validates endpoints, estimands, multiplicity control, and confidence intervals
Safety & Pharmacovigilance Agent
Verifies SAE completeness, MedDRA coding consistency, and exposure-adjusted incidence rates
Cross-Region Harmonization Agent
Identifies regulatory divergences across Canada, EMEA, and North America

One agent integrates with the You.com AI Research API, enabling: