Systematic framework for
evidence-based 510(k) classification
Regulatory equivalence determination requires systematic analysis of device characteristics, predicate lineage, and consensus standards. Veridocx provides computational methods for traceable, FDA-grounded regulatory analysis.
Supported through these programs
Comprehensive 510(k) Platform
From initial device classification to regulatory-grade documentation—computational methods and FDA-sourced data supporting each phase of 510(k) preparation.
Project Dashboard
Manage multiple projects, visualize milestones, and track progress through your 510(k) preparation journey. Stay organized with a centralized view of all your regulatory activities.
Multi-Layered Computational Framework
Veridocx is a multi-layered regulatory intelligence system—combining rule-based validation, semantic search, predicate lineage networks, and computational reasoning. Each layer addresses specific regulatory requirements and validation needs.
Medical device regulation requires more than language models. Our architecture ensures deterministic validation, FDA-grounded retrieval, and complete traceability for regulatory submissions.
Five-Layer Architecture
Each layer adds validation, context, and trust—LLMs are just one component
Rule-based validation layer
FoundationHard-coded logic derived from FDA guidances and CFR regulations. No hallucinations—just deterministic validation of classification rules, submission types, and regulatory pathways.
Semantic comparison engine
IntelligenceVector embeddings and domain-tuned search across FDA databases (510(k)s, product codes, standards). Retrieve the most relevant precedents based on device characteristics and indications.
Predicate lineage networks
ContextGraph-based analysis of predicate chains and clearance histories. Trace device relationships, identify credible predicates, and flag regulatory red flags based on historical patterns.
LLM reasoning agents
SynthesisLanguage models orchestrate workflows, synthesize evidence, and generate documentation—always grounded in retrieved facts, validated by rules, and backed by citations.
Human oversight & audit trails
TrustEvery recommendation includes confidence scores, source citations, and reasoning chains. RA/QA teams review, override, and maintain full control with complete traceability.
Validation Methodology
Data Sources
- •7,042 FDA product codes with classification regulations (CFR Part 862-892)
- •180,000+ 510(k) submissions with predicate relationships and clearance decisions
- •FDA-Recognized Consensus Standards synchronized nightly with regulatory databases
Quality Assurance
- •Expert validation with regulatory consultants and RA/QA professionals
- •Continuous monitoring of FDA database updates and regulatory guidance changes
- •Complete audit trails with source citations and confidence metrics for all outputs
Why This Architecture Matters
Medical device submissions require deterministic validation, FDA-grounded evidence, and complete audit trails. Our multi-layered approach ensures every output is trustworthy, traceable, and regulatory-grade—combining computational efficiency with methodological rigor.
Systematic 510(k) Preparation
Each phase combines computational methods with regulatory requirements to support evidence-based classification and substantial equivalence analysis.
Security & Data
Security: TLS 1.2+ encryption, AES-256 at rest, role-based access control, no model training on private data.
All projects are encrypted in transit and at rest. Role-based workspaces maintain audit trails for key actions. Your data remains private and under your control.
Important: Veridocx is an AI software tool for document preparation and research support. It does not provide legal or regulatory advice.
Data Sources & Validation
All recommendations are grounded in FDA regulatory databases and consensus standards. Our system synthesizes data from FDA 510(k) submissions, Product Classification databases, and FDA-Recognized Consensus Standards. Database synchronization occurs nightly to maintain current regulatory information.
~7k
Product codes (Nov 2025)
~200k
510(k) records
~2k
Recognized standards
Our Story
We're builders from Computer Science, Computer Engineering, and Biotechnology across the University of Toronto, University of Waterloo, and Arizona State University. After hands‑on work in ML research, data engineering, and med‑device projects, we saw a consistent pattern: regulatory work is mission‑critical, but fragmented and slow.
We're applying frontier AI—responsibly—to make 510(k) preparation faster, clearer, and more defensible. We don't replace expert judgment; we give RA/QA professionals and founders credible starting points, structured comparisons, and exportable rationales to move from idea to submission with confidence.
Meet the Team
Olivia Charles
CEO/CRO
MHSc Bioethics, Business & Life Sciences, University of Toronto. Research focus: Regulatory frameworks and medical device policy.
Cole Connelly
CFO/COO
Cell & Molecular Biology and Immunology, University of Toronto. Background in life sciences research and medical device development.
Jonas Martins
CTO
Computer Engineering & AI, University of Toronto. Research interests: Knowledge graphs, semantic search, regulatory AI systems.
Maaz Ahmed
Tech Lead
MS Software Engineering, Arizona State University. Technical focus: Distributed systems and regulatory data processing.
Matthew Li
Full-stack Engineer
Computer Science & Business, University of Waterloo. Expertise in full-stack development and regulatory platform architecture.
Pricing
| Plan | Price | Details |
|---|---|---|
| Alpha Preview | Free | Usage limits apply |
| Pro | Custom | Coming soon |
Transparent pricing will launch with general availability.
Frequently Asked Questions
Everything you need to know about Veridocx.
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