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Yosavo Studio by Youssef Ibaichou

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

  1. 01

    Discovery / review

    Identified the document types, the data to extract, and what a good result looked like.

  2. 02

    Technical planning

    Designed the prompt and workflow logic and the structured output format.

  3. 03

    Implementation

    Built the Python pipeline to read files, analyse content, and return structured JSON.

  4. 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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