Plain-English AI
AI-101 & Glossary.
Plain-English explanations of the AI and automation terms that matter in regulated industries.
This page is designed for leaders and teams in healthcare, life sciences, and other regulated sectors. It explains the language we use around governed AI, agentic automation, and MLOps, without assuming a data science background.
- No jargon for jargon's sake, clear, practical definitions.
- Focused on risk, governance, and real-world workflows.
- Linked directly to solutions, use cases, and deeper resources.
Nothing here is legal or regulatory advice. It's a practical companion to conversations with your counsel and risk teams.
Start with These Core Concepts
Agentic AI
AI systems that can take autonomous actions using tools and workflows to accomplish complex tasks.
Why it matters: In regulated industries, agentic AI must have clear governance, human oversight, and audit trails.
Explore Agentic AI SolutionsAI Readiness & Governance
The organizational capability to deploy AI responsibly with proper oversight, policies, and infrastructure.
Why it matters: Without readiness assessment, organizations waste resources on pilots that never reach production.
Learn About AI ReadinessMLOps
Practices for deploying, monitoring, and maintaining machine learning models in production.
Why it matters: Healthcare and life sciences need MLOps that includes compliance, audit logging, and drift monitoring.
Explore MLOps ServicesLLM (Large Language Model)
AI models trained on vast text data that can understand and generate human-like language.
Why it matters: In regulated contexts, LLMs need careful fine-tuning, prompt guardrails, and data handling policies.
LLM Fine-Tuning ServicesPHI / PII
Protected Health Information and Personally Identifiable Information, data categories with strict handling requirements.
Why it matters: Any AI system touching patient or personal data must be designed for HIPAA and privacy compliance.
Security & ComplianceAI Governance & Compliance
Frameworks and processes ensuring AI systems meet legal, ethical, and organizational standards.
Why it matters: Regulators and boards increasingly expect documented AI governance, not just good intentions.
Governance-as-a-ServiceGovernance & Risk
Agentic AI & Automation
Data & Infrastructure
Models & MLOps
General AI Concepts
How to Use This Glossary
It's okay if you don't recognize many terms, that's exactly why this page exists. Here are a few ways to get the most out of it:
Use it during strategy meetings so everyone shares the same language.
Share specific terms with colleagues when a concept feels fuzzy.
Use 'Where this shows up in our work' to connect terms to real projects.
From Definitions to Decisions
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Common questions
AI-101 FAQs.
Who is this glossary for?
Leaders and teams in healthcare, life sciences, and other regulated sectors who need clear, non-technical explanations of AI terms. No data science background required.
Is this technical documentation?
No. It's a practical, plain-English companion to your internal technical and legal documentation. It's designed to help you have informed conversations, not to replace deep technical resources.
Can we share this internally?
Yes. Many clients share specific terms or this entire page in internal AI working groups, steering committees, and board presentations.
Does this replace legal or compliance advice?
No. This glossary is designed to complement (not replace) discussions with your legal, compliance, and risk teams. Always consult qualified professionals for specific guidance.
Will you add more terms over time?
Yes. As our work and the AI landscape evolve, this glossary will grow. We regularly add terms based on client questions and industry developments.
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If you're trying to connect these concepts to your own systems, data, and regulatory environment, that's exactly where we can help.
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