A dedicated research environment for exploring, prototyping, and validating AI capabilities before productionization.
This repository serves as the innovation lab for BC Government AI initiatives. It will provide a space for experimentation, proof-of-concepts, benchmarking, and technical exploration without the constraints of production systems.
The R&D repository will support:
Exploratory Research
- Evaluation of emerging AI models and techniques
- Benchmarking of different approaches (open-source vs. managed services)
- Technical feasibility studies
- Performance and cost analysis
Prototyping
- Rapid proof-of-concept development
- Model experimentation and comparison
- Architecture pattern validation
- Integration testing with government systems
Knowledge Development
- Documentation of findings and lessons learned
- Technical deep-dives and tutorials
- Best practices and design patterns
- Team upskilling materials
Technology Evaluation
- Open-source model assessment
- Cloud service comparisons
- Tool and framework selection
- Security and compliance validation
Initial focus areas include:
- Document understanding (OCR, layout analysis, VLMs)
- Conversational AI and agentic architectures
- Model Context Protocol (MCP) implementations
- Retrieval-augmented generation (RAG) patterns
- Multi-agent orchestration
- Active learning and human-in-the-loop workflows
Research will follow a structured progression:
- Exploration: Initial investigation and literature review
- Experimentation: Hands-on prototyping in notebooks
- Evaluation: Benchmarking against requirements
- Documentation: Capture findings and recommendations
- Transition: Move validated patterns to production repositories
This repository feeds innovations to production systems:
- bc-laws-ai: Legal document retrieval and question-answering
- ai-adoption-document-intelligence: Document processing and OCR
- Other future AI applications
Successful experiments will be refactored, hardened, and migrated to appropriate production repositories with clear handoff documentation.
Each research initiative will be organized in its own workspace with:
- Problem statement and objectives
- Experimental notebooks and code
- Evaluation results and metrics
- Documentation and recommendations
- Migration path to production (if applicable) High-Level Architecture ai-adoption-research-and-development/
├── experiments/ │ ├── document-intelligence/ # OCR & document processing R&D │ │ ├── ocr-benchmarks/ │ │ ├── vlm-evaluation/ │ │ ├── layout-analysis/ │ │ └── custom-model-training/ │ │ │ ├── conversational-ai/ # Chatbots & agents │ │ ├── agentic-patterns/ │ │ ├── mcp-integration/ │ │ └── orchestration/ │ │ │ ├── rag-systems/ # Retrieval-augmented generation │ │ ├── embedding-models/ │ │ ├── retrieval-strategies/ │ │ └── graph-rag/ │ │ │ └── specialized/ # Other research areas │ ├── active-learning/ │ ├── model-distillation/ │ └── privacy-preservation/
├── notebooks/ │ ├── exploratory/ # Initial investigations │ ├── comparative/ # Model/approach comparisons │ └── tutorials/ # Learning materials
├── prototypes/ │ ├── document-intelligence/ # PoC implementations │ ├── conversational/ │ └── integration-demos/
├── benchmarks/ │ ├── datasets/ # Test datasets │ ├── evaluation-scripts/ # Automated evaluation │ └── results/ # Benchmark reports
├── research-papers/ │ ├── literature-review/ # Curated research │ └── technical-analysis/ # Internal findings
├── infrastructure/ │ ├── notebooks/ # Jupyter environment setup │ ├── compute/ # GPU/CPU resources │ └── data-storage/ # Research data management
├── docs/ │ ├── research-notes/ # Ongoing documentation │ ├── findings/ # Completed research summaries │ ├── migration-guides/ # Production handoff docs │ └── architecture-patterns/ # Reusable design patterns
├── shared/ │ ├── utilities/ # Common research tools │ ├── evaluation/ # Shared evaluation frameworks │ └── visualization/ # Data viz utilities
└── migration/ ├── bc-laws-ai/ # Code staged for bc-laws-ai ├── document-intelligence/ # Code staged for doc intel └── templates/ # Migration templates