This project is an AI powered computer vision platform currently under development.

The goal is to help users better understand the products they buy by analyzing packaging, ingredients, nutritional information, and potential harmful substances.

The platform combines computer vision, OCR, multimodal AI processing, knowledge bases, and personalized recommendation systems.

Core functionality includes:

• Product packaging recognition

• Ingredient extraction from labels

• Small text OCR analysis

• Harmful substance detection

• Nutritional information analysis

• Product comparison workflows

• Personalized recommendations based on user preferences and health criteria

The long term vision is to create an intelligent assistant that helps users make safer and more informed decisions while shopping.

Role

AI Systems Engineer

Project Status

Active MVP Development

This project is a large scale multimodal AI pipeline designed for processing heterogeneous data sources and preparing datasets for AI and machine learning systems.

The architecture combines document processing, OCR, image extraction, data normalization, knowledge base generation, asynchronous processing pipelines, dataset preparation, vector storage, semantic search, and adaptive workflow orchestration.

The system was designed to automate the full lifecycle of data ingestion and preparation from raw files to structured knowledge and AI ready datasets.

Key capabilities include:

• OCR and document understanding

• Multimodal data processing

• Dataset preparation for AI models

• Knowledge base generation

• SQL and vector database integration

• Asynchronous workflow orchestration

• Custom tokenizer and preprocessing modules

• Semantic retrieval and search pipelines

The project demonstrates my experience building large scale AI infrastructure, data engineering systems, and intelligent processing pipelines.

Role

AI Systems Engineer

Project Status

Prototype Completed

Hybrid AI Platform is a modular AI ecosystem currently under active development.

The platform combines multimodal AI, computer vision, LLM orchestration, knowledge bases, asynchronous data pipelines, workflow automation, reasoning systems, and a quantum-inspired orchestration layer designed to coordinate multiple AI modules and decision flows.

The core objective of the platform is to reduce dependence on isolated AI tools and create a unified intelligent architecture capable of managing complex workflows, data processing, automation, and decision support.

Current MVP directions include:

• Product packaging and ingredient analysis using OCR and computer vision

• Harmful substance detection in supermarket products

• Personalized product recommendations based on health screening and user preferences

• Business workflow automation and AI assistant systems

• Crypto market analysis and trading research modules

• Computer vision models for horse pain and inflammation detection

The architecture includes multimodal ingestion pipelines, OCR processing, dataset preparation systems, SQL and vector databases, knowledge base integration, orchestration workflows, adaptive routing, reasoning pipelines, and custom preprocessing components.

One of the project goals is to build an independent and scalable AI infrastructure with reduced reliance on external services whenever possible.

The platform is being developed as a long term AI ecosystem, future commercial product, and investment oriented technology platform.

Role

AI Systems Engineer

Project Status

Active MVP Development

Website

https://www.twine.net/signin

GitHub

https://www.twine.net/signin