LLM

Overview I design and deploy practical AI solutions that automate real business workflows - not generic chatbots, but purpose-built systems connecting your internal data, compliance rules, and operational processes with large language models. From hooking Claude or GPT into your application to building multi-agent orchestration pipelines, I handle the integration layer so AI works naturally within your existing systems. What I Can Do For You LLM and API Integrations Model integration: Connect Claude, GPT, Gemini, or local open-source models to your application via REST APIs or SDKs Context management: Conversation history, session management, and context window optimization Structured output: Parse LLM responses into typed, validated data your application can consume reliably Fallback chains: Graceful degradation across multiple model providers for high availability Retrieval-Augmented Generation (RAG) Knowledge base QA: Question-answering over your company documentation, policies, product catalogs, and technical manuals Vector search: Embedding pipelines with vector databases for semantic search over large document collections Real-world example: TOFY Go! - custom RAG pipelines for TOEFL-specific educational content delivery at Bee2 AI Agentic Systems Multi-agent orchestration: Hierarchies of specialized AI agents - one retrieves data, another analyzes, another formats output Sub-agent delegation: Complex tasks broken into parallel sub-tasks handled by independent AI agents Human-in-the-loop: Critical decisions routed to human approvers before execution Business Process Automation Document processing: Auto-extract, classify, and route invoices, contracts, and forms Reporting automation: AI-generated daily/weekly reports from structured and unstructured data Workflow triggers: Event-driven automation (new order, AI checks inventory, generates response) MCP (Model Context Protocol) Servers Custom MCP servers: Expose your internal tools, databases, and APIs as MCP tools for AI assistants Development tools: Custom MCP servers for code analysis, deployment automation, and DevOps Technologies LLMs: Claude API, OpenAI API, Gemini, open-source models (Llama, Mistral, Qwen) Frameworks: LangChain, custom agent orchestration Data: RAG pipelines, vector databases (Pinecone, Qdrant), structured SQL Protocol: Model Context Protocol (MCP) Integration: REST APIs, webhooks, message queues Deployment: Docker, AWS Lambda, serverless Languages: TypeScript, Python