DevOps architecture

Defin AI DevOps Architecture

LLM Infrastructure & Compute

Defin AI operates a self-hosted Large Language Model (LLM) ecosystem, leveraging DeepSeek R3, Mistral, and OpenAI for:

• Conversational interactions

• Complex reasoning

• Advanced classification

Our models run on RunPod’s scalable GPU resources within a Docker-based environment, enabling: ✅ Dynamic GPU scaling based on real-time demand ✅ Optimized resource allocation for peak efficiency For large-context tasks, we integrate Ollama, Anthropic, and OpenAI, ensuring seamless processing of extensive data.

Embeddings & RAG (Retrieval-Augmented Generation)

We maintain full data privacy by using in-house Nomic AI embeddings instead of third-party providers. Our RAG pipeline is built on distributed TimescaleDB, ensuring: 🔹 High availability & redundancy 🔹 Automated document embeddings & reranking 🔹 Fast, scalable data retrieval

Conversational History & Privacy

Our tiered storage system optimizes speed, scalability, and privacy:

• Redis – Real-time conversation processing

• MongoDB Atlas – Long-term encrypted storage 🔒 Privacy-first: Users exist as encrypted IDs, ensuring data remains confidential unless explicitly shared.

Agent Orchestration & Monitoring

Defin AI uses Crew AI for multi-agent orchestration, enabling seamless interaction between research, trading, and intelligence agents. We ensure real-time system health monitoring through self-hosted Elasticsearch, tracking: ✅ Performance bottlenecks ✅ Token processing efficiency ✅ Usage trends & term frequency

By continuously optimizing model efficiency & responsiveness, Defin AI delivers a scalable, high-performance AI ecosystem for real-time Web3 intelligence & trading.