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