AI / Automation
AI Document Extraction POC
A proof-of-concept AI workflow for analyzing files and extracting structured data from documents.
- Role
- AI POC development, prompt/workflow design, Python implementation
- Services involved
- Client visibility
- Anonymous case study — client details kept confidential
- Python
- LLMs
- Document processing
- JSON
- API integration
Overview
A focused proof-of-concept to test whether AI could take over slow, manual document handling. The goal was to validate value quickly and honestly — understand the quality, the limits, and the cost — before committing to a full production build.
The problem
What needed solving
Manual document review and data extraction can be slow, inconsistent, and difficult to scale.
The approach
How the work was handled
- 01
Discovery / review
Identified the document types, the data to extract, and what a good result looked like.
- 02
Technical planning
Designed the prompt and workflow logic and the structured output format.
- 03
Implementation
Built the Python pipeline to read files, analyse content, and return structured JSON.
- 04
Delivery / improvement
Tested on real examples and recommended a path for a production version.
The solution
What was delivered
Created Python-based AI/LLM workflows capable of reading files, analyzing content, and returning structured JSON outputs.
Key contributions
The work in detail
- Created AI extraction logic
- Designed prompt and workflow structure
- Implemented structured JSON output
- Integrated file and API processing
- Delivered a working proof of concept
Stack & tools
Technologies used
- Python
- LLMs
- Document processing
- JSON
- API integration
Outcome & value
Helped validate whether AI could reduce manual work before investing in a full production system.
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