The full stories
The problem, what I built, the numbers, and what I would do differently. Start with the three enterprise platforms; they only make sense together.
Takes a strategic R&D question and turns it into a ranked list of agent use cases worth building.
Makes the clinical trial data those agents need AI-ready at source, with an owner per study.
The agent platform scientists actually use. Describe the work and the agents do it.
10 case studies
Agentis: The Strategy Layer
Which clinical questions deserve an agent, and why
AIthena: The Data Foundation
Clinical trial data treated as a product, so the agents have something clean to work on
Navari: Enterprise Agentic AI
From a pilot group to 1,000+ users while I ran it, and about 10,000 after I handed it off
ARIA: Sustained Autonomous Research
One research kernel, adapted across three organizations, published, and still running on borrowed iron
OncoVLM: Domain-Specific Foundation Models
Proving that focused training beats raw scale
Agora Quantum: Autonomous AI Hedge Fund
9 agents debating over 8,000 securities daily
Continuum: Portable AI Memory
Identity that travels with you across every Claude interface
DGX Spark: Personal Research Lab
128GB VRAM enabling experiments that rival funded research teams
MCP Servers: AI Tool Ecosystem
Extending Claude to social media, knowledge bases, and beyond
Trading Evolution: From Bots to AI Hedge Fund
How simple crypto bots evolved into a 9-agent autonomous trading system