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🚀 SkillShift

Project: SkillShift

Tagline: Ensuring that when employees leave, change roles, or new hires join, no expertise is lost.

👥 Team: Teams/Team Orbit

📌 Status: 🟡 Planning

🔎 Overview

SkillShift is an AI-powered organizational knowledge assistant that helps companies capture, organize, and retrieve insights from internal communication platforms like Google Chat and Telegram. It allows admins to manage employees, data sources, and topic-based tags, while employees can query information using an AI chatbot powered by Retrieval-Augmented Generation (RAG). Messages are sanitized, embedded, and stored for semantic search, ensuring accurate and context-aware answers. With secure role-based access, SkillShift enables teams to find relevant knowledge quickly, reducing dependency on manual information transfer. The tool serves as a smart, centralized knowledge hub that enhances transparency, collaboration, and productivity across the organization.

✨ Key Characteristics

  1. 📝 Always-On Knowledge Capture
  2. 🤖 AI-Powered Summarization & Q&A
  3. 🔗 Role-Based Access Control (RBAC)
  4. 📦 Tag-Based Organization

❌ The Problem

  1. Huge knowledge loss when employees resign or switch roles.
  2. Scattered and unstructured information across multiple chat platforms.
  3. Single points of failure: only one person knows a critical system.
  4. Manual documentation is outdated and inconsistent.
  5. Experts are overloaded answering repetitive questions.

💡 Our AI Solution

✅ What does it do?

  • For Employees: Smooth onboarding, faster learning, less frustration.
  • For Teams: Reduced dependency on single experts, better collaboration.
  • For Managers/HR/Admins: Preserve IP, resilience, and smoother transitions.

⚙️ How does it work?

🏗️ Architecture

  • Frontend (React): Employee and Admin dashboards with AI chatbot interface.
  • Backend (FastAPI): REST APIs for authentication, data management, and AI query handling.
  • Database (PostgreSQL + pgvector): Stores employees, tags, messages, and embeddings for semantic search.
  • NLP Worker: Cleans and embeds chat messages for AI retrieval.
  • Docker Compose: Orchestrates all services for seamless deployment.

🔄 Data Flow

  1. Ingestion: Messages are fetched from Google Chat or Telegram data sources.
  2. Sanitization: Text is cleaned to remove PII, links, and noise.
  3. Embedding: Cleaned text is converted into vector embeddings using NLP models.
  4. Storage: Messages and embeddings are saved in PostgreSQL (pgvector).
  5. Retrieval: When a user queries, embeddings are generated and similar messages are retrieved for AI-generated answers.

🚀 What makes it innovative?

  1. AI-Driven Retrieval: Uses RAG to deliver context-aware, factual answers.
  2. Tag-Based Access: Organizes knowledge dynamically by topics or projects.
  3. Automated Knowledge Capture: Extracts insights directly from internal chats.
  4. Privacy-First Design: Sanitizes and anonymizes all data before storage.
  5. Seamless Integration: Connects with existing chat tools like Google Chat and Telegram.

🛠️ Technology Stack

  • NLP/LLM: OpenAI (GPT-4), LLaMA
  • Framework & Libraries: LangChain
  • Frontend: React.js + Tailwind (dashboards, chatbot UI)
  • Backend: FastAPI (Python) + JWT auth
  • Database: PostgreSQL (pgvector)
  • Integrations: Telegram Bot API, Google Chat API
  • Deployment: Docker + GitLab CI/CD (EC2/DigitalOcean)

🏗️ Project Architecture

graph LR
    A[📂 Data Sources → Chats, Docs, Code ] --> B[⚙️ Data Ingestion Pipelines → Remove PII, links, and noise]
    B --> C[🗄️ Knowledge Store → Postgres + pgvector]
    C --> D[🤖 AI Services Layer, LLM Q&A - LangChain]
    D --> E[💻 Client Interfaces, Web Dashboards - React, Chatbot ]

🧩 Challenges & Learnings

To be added during development phase

📅 Future Roadmap

  • Short-term (MVP):

    1. Employee & Admin Roles: Secure login with role-based access (RBAC).
    2. Tag Management: Admins can create, update, and assign tags to employees.
    3. Data Source Integration: Configure Telegram or Google Chat groups as data sources.
    4. Message Ingestion: NLP worker cleans and stores messages with embeddings.
    5. AI Chatbot (RAG): Employees query data and get contextual, AI-generated answers.
    6. Dashboards:
      • Employee Dashboard: Profile and chatbot view.
      • Admin Dashboard: Manage users, tags, and data sources.
    7. Dockerized Setup: Complete stack deployable via Docker Compose.

  • Medium-term: To be added

  • Long-term: To be added

📂 Repository & Demo

📸 Screenshots / Demo Video

🔜 Will be shared in next milestone

🏷️ Categories

#project #ai-hackathon-2025