From Inspiration to Operations
Turning ideas into prototypes and prototypes into products
Mechanical & Metallurgical Engineering
- CAD modeling and drafting: Solidworks
- Prototype development and testing
- Risk Analysis, Failure Mode Effects Analysis.
- Studies: FMEA Literacy, The Compact Wallet
- Material selection, testing, and sourcing
- Studies: NPD & Metallurgy
- FEA-Finite Element Analysis,
Project Management, Engineering Lead
- Design Transfer
- Advanced Product Quality Planning
- Supplier Development
- Design and Production Documentation
Applied Data Science & Augmented Intelligence
- Data Engineering
- Data Analytics & Visualization
- Machine Learning
- NLP
- KG-RAG Architecture
Studies: Binary Classification, Temperature Data, The Compact Wallet
Fixed price packages that leverage AI with domain experts in the loop
QMS Starter Kit: Typically a 3-session engagement designed for medical device startups. Leveraging a Knowledge Graph RAG system, this package delivers several foundational QMS SOPs (Standard Operating Procedures) such as; Design and Development, V & V Testing, and Risk Management. This selection of SOPs establishes a framework for start-up companies to manage their contractors and employees such that all early, innovative efforts immediately contribute to a compliant Design History File (DHF) from day one. [ Learn More / View Deliverables–UNDER CONSTRUCTION ]
Design Controls Starter Kit: For startups that have a working knowledge of the User Needs and an early prototype build or design, but lack a structured regulatory framework. This package initiates product design Traceability and Risk Management. By establishing this engineering documentation in a logical, traceable methodology, the team’s work effort and progress will immediately start contributing to the Design History Folder. These frameworks provide project transparency and form the foundational task structure from which a project plan can take shape. [Learn More / View Deliverables: UNDER CONSTRUCTION ]
Mining MAUDE Data, Insights Report The FDA’s MAUDE database is public but messy. Throughly reviewing the thousands of recods is difficult, but these records provide excellent objective evidence of produict failure modes. This evidence provides invaluable support for risk management, post-market surveillance, competitor evaluation, and 510K test planning. This package utilizes applied data science and iterative text-mining techniques to parse, categorize, and filter the unstructured adverse event narratives for several targeted product codes. [Learn More / View Deliverables: UNDER CONSTRUCTION]