When Your Team Knows AI Exists But Can’t Use It Safely
Your team sees competitors using AI. They read about potential efficiency gains and automation. Some are experimenting with ChatGPT privately — but no one knows if that’s compliant, how to validate outputs, or what the regulator will accept.
You want to move faster. However, you can’t afford to break regulatory requirements or compromise patient safety in the process.
We help pharmaceutical and pharmacy teams work AI-native while staying inspection-ready.
Not by teaching AI theory. By building the practical capability to select tools, engineer prompts, validate outputs, and build solutions within your regulatory constraints — with full audit trails.
What AI-Native Actually Means in Pharma
Most organisations are AI-curious. They’ll use tools privately, experiment cautiously, and maybe draft documents with AI assistance. But they deliver work the traditional way.
AI-native means your team’s default workflow runs through AI:
- Literature surveillance automated with validated search agents
- Case triage accelerated using intelligent routing
- CAPA analysis enhanced with pattern detection
- Regulatory writing supported by prompt-engineered templates
- Evidence retrieval streamlined through RAG pipelines
- Signal detection augmented with explainable models
But here’s the critical part: Every AI deployment includes risk assessment, human-in-loop controls, complete audit trails, and ongoing performance monitoring.
Fast AND safe. Innovative AND compliant. That’s what we teach.
The Problem We’re Solving
Australian pharmaceutical and pharmacy teams face a fundamental capability gap:
- They know traditional GxP workflows well. They understand TGA requirements, ICH standards, and what inspectors expect. Years of experience in pharmacovigilance, clinical research, manufacturing, quality systems, and pharmacy practice.
- They know AI is changing everything. They read about efficiency gains, see competitors moving faster, and understand that this technology is no longer optional.
- But they don’t know how to bridge the two. What tools are safe in regulated environments? How do you validate AI outputs? What stays human-controlled? How do you document AI usage for inspection? What does ISO 42001 / Quality Systems actually require?
That gap is what we close.
I’m Carl Bufe — The AI-Native GxP Practitioner. I spent 20 years in traditional pharmaceutical compliance (pharmacovigilance, quality, GCP, pharmacy practice), then spent 3 years rebuilding those workflows with AI — safely.
We teach teams to work the way we work: AI-enabled by default, regulatory-compliant by design.
Training Philosophy
Start Where You Are
We don’t assume AI literacy. Whether your team has never written a prompt or they’re already experimenting with tools, we start with a baseline assessment and build from there.
- Foundation: Understanding what AI actually is, how LLMs work, what they can and can’t do reliably in regulated contexts.
- Practice: Hands-on prompt engineering with pharmaceutical use cases. You’re writing, testing, validating — not just watching demos.
- Implementation: Building actual tools your team will use next week. Literature search agents. Case triage workflows. Evidence retrieval systems.
- Governance: ISO 42001 frameworks, validation protocols, and audit trail design. Ensuring that everything you build can withstand scrutiny.
We teach tool selection.
We don’t push specific vendors or platforms. We teach decision frameworks:
- When to use closed enterprise AI (Microsoft Copilot, Azure OpenAI) vs. open models
- How to evaluate tools against regulatory requirements
- What security controls matter in pharmaceutical environments
- Where human oversight is non-negotiable
- How to build vendor-agnostic solutions that don’t lock you in
You learn principles that work regardless of which AI tools emerge next year.
