Healthcare AI
Architecture Strategy
We built this because choosing an architecture is the decision nobody talks about until it's too late. Every approach makes a bet on how much you trust the machine—this tool helps you see what you're betting.
The Core Tradeoff
The more autonomous the AI, the more structure it needs underneath. That's the tension this framework helps you navigate.
Your Use Case
Patient Triage
Symptom assessment, urgency classification, routing.
Benefits Verification
Insurance eligibility, coverage confirmation.
Prior Authorization
Medical necessity evaluation, policy compliance.
Clinical Decision Support
Treatment recommendations, drug interactions.
QA / Audit
Encounter review, compliance documentation.
Your Scale
Your Stage
Isolated environment to validate user experience and technical feasibility. No real patient data is exposed.
Observer & Critic. No real-world liability.
Architecture for Patient Triage
CustomGPT / Basic LLM
Profile: Quick Prototyping
Why It Fits
Proves you can engage patients with a natural conversational flow. Speed is key here; you need to show stakeholders that the "front door" experience is welcoming before building the clinical brain.
Operational Assumptions
- No real patients involved
- Scripted happy-path scenarios
- Clinician oversight on all output
Governance Triggered
- None required
CustomGPT / Basic LLM prioritizes flexibility over upfront investment. That's the right bet if you need to prove the concept before investing in infrastructure.
Architecture is just the beginning.
Choosing the right pattern gets you to the starting line. What happens next—how you structure knowledge, enforce policies, and maintain auditability—determines whether you cross the finish line.
The Neuro-Symbolic approach isn't just another architecture. We believe it's the foundation for AI systems that can be trusted in high-stakes environments.