Model Selection Strategy
Each AI model excels at different tasks. We route requests to the optimal model based on the use case.
OpenAI GPT-4o
Flagship conversational AI with superior reasoning and instruction-following capabilities.
Primary Use Cases:
- Policy writing & content generation
- Journey simulation & storytelling
- Plain language suggestions
- Conversational interfaces
Gemini 1.5 Pro
Advanced analysis engine with exceptional pattern recognition across large documents.
Primary Use Cases:
- Content gap analysis
- Deep document analysis
- Multi-document comparison
- Pattern detection (50+ pages)
Gemini 1.5 Flash
Speed-optimized model for real-time processing and high-volume tasks with cost efficiency.
Primary Use Cases:
- Topic extraction & analysis
- Quick insights generation
- Real-time content processing
- High-volume automation
Why Multi-Model Architecture?
Optimized Performance
Each model is deployed for tasks where it excels. GPT-4o for nuanced reasoning, Gemini Pro for large-scale analysis, Flash for speed-critical operations.
Cost Efficiency
Gemini Flash costs ~90% less than GPT-4o for bulk tasks. Strategic routing achieves 3x cost efficiency while maintaining output quality.
Provider Independence
Not locked into a single vendor. Fail-over logic ensures resilience - if one provider experiences downtime, requests automatically route to alternatives.
Massive Context Windows
Gemini's 2M token context window enables analysis of 50+ web pages simultaneously without chunking. Ideal for comprehensive gap detection.
Technical Implementation
Production-ready architecture built for scale, reliability, and observability.
Backend Stack
- Framework: Flask (Python)
- Database: PostgreSQL with SQLAlchemy ORM
- Server: Gunicorn WSGI with auto-scaling
- Deployment: Replit infrastructure
AI Integration
- Routing: Dynamic model selection per task
- Monitoring: Token usage & latency tracking
- Resilience: Automatic fail-over logic
- Security: Stateless API calls, no PII storage
Real-Time Monitoring
API monitoring tracks token consumption, response times, and costs across all providers. Automated alerts trigger when latency exceeds thresholds or API quotas approach limits.
Roadmap & Evolution
Our AI stack evolves with emerging models and government requirements.
Testing New Models
Continuous evaluation of Gemini 2.0, Claude 3 Opus, and other emerging models for specialized use cases.
RAG Implementation
Retrieval-Augmented Generation with government-specific knowledge bases for enhanced accuracy and context.
Enterprise Migration
Azure OpenAI integration for production deployment with Canadian data residency and enterprise governance.