Raycaster

Product Introduction

1. Definition: Raycaster is an AI-native regulatory intelligence platform for biopharmaceutical development. It operates as a cloud-based SaaS solution that automates document synchronization across clinical protocols, study reports (CSRs), Module 2/3 regulatory submissions, and quality documentation.
2. Core Value Proposition: Raycaster eliminates manual rework in drug submissions by ensuring scientific updates automatically propagate across all linked documents. Its primary function is to accelerate regulatory compliance while maintaining alignment between data, protocols, and global health authority requirements (e.g., ICH, FDA, EMA).

Main Features

1. Regulatory Research Engine:

   • How it works: Raycaster ingests protocols, CSRs, SOPs, and regulatory guidance (ICH, GCP, GMP) into a unified knowledge graph. Using NLP and semantic search, it cross-references user queries with source documents, highlighting compliance gaps and impacted sections. Outputs include traceable citations to specific paragraphs in regional guidelines.
   • Technologies: Combines transformer-based language models (e.g., BERT variants) with graph databases for contextual linkage.

2. AI-Assisted Drafting:

   • How it works: Generates draft protocols, Investigator Brochures (IBs), CSRs, and Module 2/3 sections using predefined templates and structured data inputs. Maintains version control by auto-inserting standardized boilerplate (e.g., safety narratives, control strategies) while allowing manual overrides.
   • Technologies: Template-driven natural language generation (NLG) integrated with clinical data lakes and validation rule sets.

3. Change Impact Analysis:

   • How it works: When users modify parameters (e.g., clinical endpoints, CMC specifications), Raycaster maps dependencies across the submission dossier. It flags affected documents (e.g., protocols ↔ Module 3 reports) and suggests harmonized updates via diff algorithms.
   • Technologies: Dependency-tracking algorithms with real-time graph traversals and machine learning-based impact prediction.

Problems Solved

1. Pain Point: Manual document misalignment in drug submissions causes 30-50% rework (per industry benchmarks), delaying approvals and increasing compliance risks. Raycaster automates synchronization, eliminating version conflicts.
2. Target Audience:
   • Clinical/Medical Writers: Reduce protocol-amendment reconciliation.
   • CMC/QA Teams: Streamline control strategy updates across Module 3.
   • Regulatory Affairs: Accelerate ICH-aligned Module 2/2.6 summaries.
   • Nonclinical Toxicologists: Manage ICH S/M3 guideline adherence.
3. Use Cases:
   • Real-time protocol amendments triggering automatic CSR updates.
   • Process parameter changes auto-propagating to CMC validation reports.
   • Health authority queries resolved via cross-document gap analysis.

Unique Advantages

1. Differentiation: Unlike static templates (e.g., Veeva Vault) or generic AI tools, Raycaster embeds regulatory intelligence directly into documents. Competitors lack end-to-end impact tracing (e.g., tracing a toxicity limit change from study reports to Module 2.4).
2. Key Innovation: Proprietary "document-aware" AI architecture where files retain contextual awareness of scientific sources, regulatory obligations, and inter-document relationships. This enables autonomous updates without manual scavenger hunts.

Frequently Asked Questions (FAQ)

1. How does Raycaster ensure compliance with FDA/EMA regulations?
   Raycaster’s NLP engine is pre-trained on ICH, 21 CFR, and Annex 1 guidelines, with citations auto-linked to source paragraphs. All AI outputs undergo human review loops for GxP compliance.

2. Can Raycaster integrate with existing eTMF or QMS systems like Veeva?
   Yes, Raycaster operates agnostically via APIs, syncing with Vault, SharePoint, and legacy Word/Excel stacks without data migration.

3. What biopharma submission types does Raycaster support?
   It covers INDs, NDAs, MAAs, and lifecycle management for protocols, IBs, CSRs, Module 2 (nonclinical/clinical), Module 3 (CMC), and quality/compliance documents.

4. How does Raycaster handle data security for confidential drug development information?
   The platform uses SOC 2-compliant infrastructure with end-to-end encryption, audit trails, and role-based access controls meeting FDA 21 CFR Part 11 requirements.

5. What AI training data validates Raycaster’s accuracy?
   Models are trained on anonymized regulatory documents from 500+ drug approvals and continuously refined using feedback from regulatory affairs specialists.