Case study
Squint AI — Document-to-SOP Pipeline
An AI-powered document-processing platform that converts unstructured business documents into structured, editable standard operating procedures.
Snapshot
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Case study
System breakdown
01
Overview
A document-processing platform for transforming messy business files into structured SOP data that can be edited and reused downstream.
02
Problem
Business process documents often arrive as PDFs, DOCX files, and plain text with inconsistent formatting, making them hard to convert into usable application data.
03
Solution
The pipeline extracts text and tables, normalizes document structure, and uses AI-assisted parsing to produce structured JSON.
04
Personal contribution
Built parsing workflows, structured output schemas, evaluation flows, and integrations across the document-processing stack.
05
Technical architecture
FastAPI services, Python document extraction libraries, Pydantic schemas, Celery jobs, AWS S3 storage, and a Next.js application layer.
06
Results
Processed more than 20 SOPs, approximately 50,000 words, and reached more than 90% parsing accuracy.
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