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AI Builders is an open-source, collaboration-driven AI engineering organization. Our members — students, researchers, and experienced engineers alike — build complete, production-ready systems following the same lifecycle a professional AI engineering team would use: from problem definition and data collection, through model training, evaluation, deployment, and documentation.

We work equally across two core pillars: production-ready Computer Vision systems and LLM / RAG / AI Agent systems — including retrieval-augmented generation pipelines, agentic workflows, and API-driven inference — alongside active work in deep learning, NLP, and MLOps more broadly.

Who this organization is for:

The full Core Team roster is maintained in TEAM.md.

An enterprise-grade Document AI system combining a document preprocessing pipeline with retrieval-augmented generation (RAG) and LLM-powered AI agents, enabling automated understanding, retrieval, and reasoning over large document collections. Tech stack: Python, RAG, LLMs, AI Agents, FastAPI Status: 🚧 Actively in development

A production-grade Computer Vision + LLM integration reference project. YOLOv8 detection and tracking feed an 8-rule industrial safety Rule Engine (PPE compliance, proximity, idleness, crowd density); events are logged via FastAPI, and a local LLM (Qwen2.5) generates daily/weekly HSE compliance reports in both Persian and English. Tech stack: Python, YOLOv8, FastAPI, OpenCV, Hugging Face Transformers, Gradio, PostgreSQL, Docker, uv Status: ✅ Completed · License: MIT

A two-stage Computer Vision system for detecting, classifying, and tracking assembly defects on PCB boards. A board-segmentation YOLO model crops the PCB region, which a second YOLO model then scans for defects (with ByteTrack multi-frame tracking) — exposed via a FastAPI REST API and a Streamlit dashboard, shipped as a single Docker image. Tech stack: Python, YOLO (Ultralytics), OpenCV, FastAPI, Streamlit, ByteTrack, Docker Status: ✅ Completed · License: Not specified in repo

A typical AI Builders project follows the same engineering lifecycle end-to-end:

flowchart TD
    A[Problem Definition] --> B[Dataset Collection]
    B --> C[Data Annotation]
    C --> D[Data Cleaning & Analysis]
    D --> E[Feature Engineering]
    E --> F[Model Development]
    F --> G[Training & Fine-tuning]
    G --> H[Evaluation]
    H --> I[Optimization]
    I --> J[API Development]
    J --> K[Deployment]
    K --> L[Documentation]

    subgraph Serving Layer
        J
        K
    end

    subgraph MLOps Layer
        M[MLflow Tracking]
        N[Docker Containers]
        O[CI/CD - GitHub Actions]
    end

    G --> M
    K --> N
    N --> O

We operate the way professional AI teams do: through code review, structured feedback, and shared ownership.

Ways to contribute:

• Build or improve ML/DL models and CV pipelines
• Create, clean, or annotate datasets
• Build and document APIs
• Review pull requests and write tests
• Improve documentation or onboarding materials
• Conduct applied research
• Fix bugs and optimize performance

Contribution workflow:

1. Check open issues or propose a new one describing the problem you want to solve
2. Fork the repository and create a feature branch
3. Follow the coding and documentation standards
4. Open a pull request referencing the relevant issue
5. Address review feedback from a maintainer before merge

Security issues should not be filed as public GitHub issues — please report them privately via [email protected].

Organization: AI Builders Founded: 2026 Type: Open-source, community-driven

License: AI Builders has no single org-wide license — licensing is set per repository (see the project list above).