AI & Generative AI Technologies
Build AI systems that connect models to trusted data, tools and workflows.
Model choice is only one part of AI architecture. Production systems also require context, evaluation, permissions, observability, data quality and human oversight.
Connected capabilities
Themes
What this area covers
Selection
Selection criteria
- Task quality
- Data sensitivity
- Latency
- Cost
- Model portability
- Evaluation needs
- Human oversight
- Vendor risk
Technologies
AI & Generative AI technologies
OpenAI Platform
AI Platform
Models and APIs for generative AI, reasoning, multimodal and agentic application use cases.
Azure AI
Cloud AI Platform
Microsoft cloud AI ecosystem for model access, enterprise integration and AI application development.
Google Vertex AI
Cloud AI Platform
Google Cloud platform for building, deploying and operating AI/ML applications.
AWS AI Services
Cloud AI Platform
AWS ecosystem for generative AI, machine learning and AI application infrastructure.
LangChain
AI Framework
Application framework for composing LLM workflows, retrieval and tools.
Model Context Protocol
Protocol
Open protocol pattern for connecting AI systems to tools and data sources.
PyTorch
ML Framework
Deep learning framework for research and production model development.
TensorFlow
ML Framework
Machine learning framework for training and deploying ML models.
Hugging Face
AI Ecosystem
Open model and dataset ecosystem for NLP, vision and generative AI.
Vector Databases
Data Technology
Vector indexing and retrieval technology used for semantic search and RAG.
Architecture before stack
Have a stack already—or need help choosing one?
Tell us what you are building, what already exists and the constraints that matter.