LLM Engine & AI Workflow Platform
AI workflow orchestration platform integrating Large Language Models to automate document processing, business workflows, knowledge retrieval, and intelligent decision-making.
/// overview
Developed an AI workflow orchestration platform that integrates Large Language Models into enterprise processes. Supports document ingestion and processing, retrieval-augmented generation (RAG) over private knowledge bases, structured workflow automation, and decision-making pipelines that route outputs to downstream systems.
The problem
Businesses have large volumes of documents and internal knowledge that employees navigate manually. Existing LLM integrations are either too shallow (chat-only) or too expensive and complex to wire into real business workflows.
The approach
Built a Python backend with LangChain/LlamaIndex for RAG pipeline construction. Used FastAPI for the orchestration API layer, PostgreSQL with pgvector for embedding storage, and Redis for workflow state and caching. Implemented document chunking, embedding, and retrieval pipelines. Added a workflow DSL for defining multi-step LLM chains with conditional branching.
Outcome
Automated document review, knowledge Q&A, and decision routing workflows that previously required manual analyst time — with retrieval accuracy grounded in private organizational data.
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