🎯 PathCraft AI | Strategic Skill-Gap Analysis Engine PathCraft AI is a local-first intelligence platform engineered to analyze technical alignment within the 2026 AI job market. By leveraging high-dimensional vector embeddings and deterministic matching logic, the system provides precise gap analysis and career deployment roadmaps without relying on external cloud APIs.

🛠 Technical Architecture The system is built with a modular "Local-First" stack, prioritizing data privacy, low latency, and mathematical accuracy.

Neural Inference (Local): Sentence-Transformers utilizing BERT-based architectures for high-precision semantic vector generation.

Vector Orchestration: ChromaDB for high-dimensional indexing and similarity-based retrieval.

Compute Logic: Pure Python 3.10+ featuring custom VectorMath modules for deterministic similarity scoring.

Tactical Interface: Streamlit with a customized Cyber-Industrial CSS framework for high-density data visualization.

Data Pipeline: Pandas for structured market intelligence and PyPDF2 for secure, localized document parsing.

🚀 Key Capabilities 📡 Offline Vector Intelligence Execute deep semantic analysis of professional experience against complex job requirements. The system maps CV data to a high-dimensional vector space stored in ChromaDB, enabling instantaneous alignment scoring without external API dependencies.

🛡️ Tactical Token Extraction A robust regex-based extraction engine designed to parse unstructured job descriptions. It identifies critical technical tokens (e.g., Vector Databases, RAG, PyTorch) to generate a clean, validated "Required Stack" list.

🧬 Automated Gap Synthesis The engine cross-references identified technical deficiencies with a localized courses.csv repository. This generates a direct bridge between detected gaps and verified educational modules from elite providers.

📟 Cyber-Industrial Command Center An engineering-centric dashboard designed for professional clarity:

Match Readiness Index: Real-time percentage reflecting semantic and technical alignment.

Deployment Status: Visual "MATCHED" (✔) and "GAP" (✖) status indicators.

📂 Project Structure

├── data/ # Verified Course Intelligence (courses.csv) ├── src/ │ ├── api/ # Tactical Command Dashboard (app.py) │ ├── core/ # Mathematical logic & analysis entities │ ├── services/ # VectorDB (ChromaDB) & Embedding orchestration │ └── use_cases/ # SkillGapAnalyzer & PathGenerator modules ├── ai_jobs_market_25_26.csv# 2026 AI Market Intelligence Dataset ├── ingest_jobs.py # Data Ingestion & Vectorization Pipeline └── requirements.txt # System Dependency Manifest

⚡ Deployment Guide 1.Environment Setup: pip install -r requirements.txt

2.Initialize Vector Database (Local Processing): python ingest_jobs.py

3.Launch Tactical Interface: streamlit run src/api/app.py