Hypodossier: Fintech Automation
Automating mortgage document administration for financial institutions through intelligent extraction and classification pipelines. We built an AI-driven platform capable of recognizing over 250 document types and extracting 500+ granular data fields to eliminate manual bottlenecks for corporate lenders.
95% Accuracy
Recognition
500+ Data Fields
Extraction
250+ Document Types
Classification
Challenge & Scope
Requirements
Eliminating manual data entry for irregular financial documents
Achieving 95% recognition accuracy for 250+ distinct document types
Strict data privacy and GDPR regulatory adherence
Scaling the extraction pipeline to handle large document volumes
Strategic Objectives
Automate complex mortgage document processing with 95% document recognition accuracy and GDPR compliance.
Key Deliverables
Technical Execution
Intelligent Pipeline
Developed a computer vision pipeline that automatically cleans, cuts, and rotates documents, ensuring they are formatted for classification for AI classification.
250-Type Classifier
Engineered an AI classification engine that identifies and names over 250 specific mortgage and financial document types with 95% accuracy.
500-Field Extraction
Implemented a data extraction engine that captures over 500 data points (income, debt, asset values), enabling total automation of lender workflows.
Technical Stack
Languages / AI
Python
Computer Vision Pipelines
NLP
Web Stack
Django Ninja
React
TypeScript
Database
PostgreSQL
Compliance
GDPR / Global Privacy Standards
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Features & Outcomes
95% Recognition
Reduced manual verification requirements.
Auto-Cleanup
Optimized document legibility and storage size.
Lending Dashboard
Single source of truth for all processed mortgage files.
Client Benefits
Reduced manual document processing workload compared to manual high-cost labor
Support for seasonal demand increases to handle seasonal mortgage application spikes
Access to specialized AI and vision senior engineers in Ethiopia
Direct collaboration across European time zones (UTC+3)
Strategic Outcomes
Eliminated time-consuming manual document administration for lenders
Allowed financial specialists to focus on high-value customer relationships
Established a 95% document recognition accuracy for complex document recognition
Ensured full technical compliance with standardized data residency and GDPR laws
System Interfaces
Project Team
Sami
Full-stack Engineer
Vision Pipelines & Backend Engineering
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