LLM and Agentic AI
Production-oriented prototypeEnterprise Document Intelligence
Problem
Contracts and invoices arrive as unstructured documents that teams must read manually before any downstream system can use them.
System
An AI document-processing system that ingests documents, applies OCR and parsing, chunks and embeds the text, and uses retrieval with LLM orchestration to produce entity extractions and structured reports. Results are exposed through a FastAPI service for application integration.
My contribution
Designed the document-processing workflow, built the ingestion and preprocessing pipeline, implemented retrieval and LLM orchestration, developed structured API outputs, and created evaluation and error-analysis workflows.
System architecture
- Documents
- Parsing and OCR
- Chunking
- Embeddings
- Retrieval
- LLM processing
- Structured output
- API
Technical focus
- Retrieval-augmented generation
- OCR and text parsing
- Entity extraction
- Structured outputs
- Workflow automation
Technology stack
- Python
- LangChain
- Transformer models
- FastAPI
- REST APIs
Data source
Dataset and data-collection write-up is being prepared.
Data pipeline
Ingestion and preprocessing write-up is being prepared.
Model or LLM workflow
Model and workflow write-up is being prepared.
Evaluation and results
Evaluation setup and results write-up is being prepared.
Challenges and engineering decisions
Key engineering decisions write-up is being prepared.