This example project focuses on internal workflow automation: ingesting documents, extracting structured data, routing work, and keeping humans in control where judgment is required.
The Problem
Many teams still move information through inboxes, spreadsheets, PDFs, and repeated copy-paste work. AI can help, but only if the output becomes structured, reviewable, and connected to the system of record.
The Approach
The automation flow separates extraction from approval. LLMs can summarize, classify, and propose fields. The application validates formats, highlights low-confidence values, routes exceptions, and records the human decision before updating operational records.
What It Shows
- Document intake and structured extraction without pretending AI is always correct.
- Human-in-the-loop review for high-risk or ambiguous fields.
- Workflow routing based on validated state, not raw model output.
- Audit trails that explain where each record came from.
Outcome
Example project: demonstrates document processing, human-in-the-loop review, structured extraction, and LLM integration with existing business systems.