Introducing The Consilium: A Personal AI Advisory System
I’m embarking on what might be my most interesting personal project yet: building “The Consilium” - a multi-agent AI advisory system that acts as my personal council of specialists. Think of it as having a team of AI advisors, each expert in different aspects of life management.
Why “The Consilium”?
The name comes from the Latin term for an advisory council - historically used by Roman emperors for their trusted advisors. It perfectly captures what I’m building: a council of AI agents, each specialized in different domains of personal life.
The Agent Council
My planned specialists include:
- Financial Advisor: Insurance policies, housing decisions, investment tracking
- Location Intelligence: Neighborhood data, local services, property values
- Travel Coordinator: Flight tracking, travel planning, loyalty program optimization
- Environmental Monitor: Weather patterns, seasonal planning, local conditions
- Health & Wellness: Appointment scheduling, health metrics, preventive care reminders
- Master Coordinator: Routes queries between agents and synthesizes comprehensive responses
Technical Philosophy: Frugal over Fast
This isn’t about bleeding-edge performance or enterprise-scale infrastructure. It’s about building cost-effective, privacy-first personal AI infrastructure:
Key Design Principles
- Local Model Serving: Using ramalama on Fedora instances for cost control
- AWS us-east-2: Keeping costs low by avoiding premium regions
- Red Hat Ansible: Infrastructure as code for reproducibility and documentation
- Spot Instances: Targeting 60-90% cost savings over on-demand pricing
- Privacy-First: All processing happens on my own AWS infrastructure
Estimated Costs
- Phase 1 (Single Instance): $15-30/month
- Phase 2 (Full Council): $50-80/month with Spot instances
- Data & Storage: $5-10/month for logs and model caching
The Architecture Vision
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Financial │ │ Location │ │ Travel │
│ Advisor │ │ Intelligence │ │ Coordinator │
│ │ │ │ │ │
│ ramalama + │ │ ramalama + │ │ ramalama + │
│ Llama 3.2 3B │ │ Llama 3.2 3B │ │ Llama 3.2 3B │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
└───────────────────────┼───────────────────────┘
│
┌─────────────────┐
│ Master │
│ Coordinator │
│ │
│ Query Router │
│ & Synthesizer │
└─────────────────┘
Why Build This?
Personal AI infrastructure is becoming more accessible, but most examples focus on enterprise use cases or expensive cloud AI services. I want to demonstrate how individuals can build sophisticated, cost-effective AI systems for personal use while maintaining complete privacy and control.
There’s also something poetic about documenting the building of an AI system that will eventually help manage the very blog posts documenting its creation.
The Journey Ahead
I’ll be documenting this entire build process here - the successes, failures, cost optimizations, and lessons learned. This is as much about the learning journey as the destination.
Planned Blog Series
- Setting Up the Foundation - AWS infrastructure with Ansible
- Deploying ramalama - Local model serving on Fedora instances
- Building the First Agent - Financial advisor implementation
- Agent Communication - Inter-agent protocols and coordination
- Cost Optimization Deep Dive - Keeping it frugal with Spot instances
- Security and Privacy - Protecting personal data in the cloud
- Lessons Learned - What worked, what didn’t, what I’d do differently
Getting Started
The project is already underway with a proper directory structure, Ansible playbooks, and deployment guides. Everything is designed to be reproducible and well-documented.
If you’re interested in following along or building something similar, the entire project will be documented with infrastructure-as-code principles. While I won’t be open-sourcing the personal configuration details, the architectural patterns and deployment strategies will be fully shared.
What’s Next?
Next up: Setting up the foundational AWS infrastructure in us-east-2 and getting our first ramalama instance running. The Consilium’s first advisor is about to come online.
Follow along as I build The Consilium - proving that personal AI infrastructure can be both sophisticated and affordable.