**SkillShift** is a system designed to help employees and HR/Admins manage, explore, and retrieve organizational knowledge through **AI-assisted interactions**.

Organizations often face operational and productivity risks when employees leave or move across teams without structured handovers or visibility into their unique knowledge, decisions, and expertise.

This tool leverages **NLP-based knowledge mining** to automatically build a dynamic knowledge base and organizational decisions — all derived from communication channels like **Google Chat** and **Telegram**. The platform centralizes internal communication data, maintains tags for context classification, and provides employees and HR/Admins with an AI assistant to query messages and insights related to them or their assigned tags.

In modern organizations, large volumes of valuable information — discussions, decisions, technical clarifications, and project context — are scattered across internal messaging tools. This tool provides a unified way to collect, organize, and retrieve such communication efficiently using AI-assisted search and tag-based categorization.

Unlike traditional HR or knowledge base systems, this assistant focuses on **conversation intelligence** rather than static documentation. Instead of manually searching through thousands of chat messages, users can simply ask **natural language questions** through the AI assistant, which semantically understands the context and retrieves relevant messages tagged to their area, project, or topic.

- View their profiles, assigned tags, and interact with the AI assistant to find relevant discussions or information.

- Manage and assign tags, configure data source integrations, and use the AI assistant for organization-wide insights.

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The system aims to achieve the following core objectives:

- Consolidate organizational conversations from multiple data sources such as **Google Chat** and **Telegram** into a single, searchable knowledge base.

- Simplify knowledge discovery through a **conversational AI assistant**.

- Enable structured organization of communication via **tags** for projects, discussions, or themes.

- Empower employees with **role-based access** to only relevant messages and topics.

- Provide HR/Admins tools for efficient management of users, tags, and data sources.

- Enhance organizational transparency and reduce information silos through AI-assisted knowledge retrieval.

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- **Integrations:** Google Chat & Telegram

- Employee Profile

- AI Assistant

- Tag Management

- Data Source Management

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- **Employee:** View their profile and access the AI assistant.

- **HR/Manager/System Admin:** Manage integrations and tag management.

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- **Telegram Bot API:** Captures chat messages.

- **Google Chat API:** Webhook/polling for spaces & groups.

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- **Text Preprocessing:** Removes stopwords, links, emojis, and Personally Identifiable Information (PII).

- **Embeddings:** Creates message text embeddings for semantic search.

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| Table | Description |

|-------|--------------|

| `employees` | id, external_id, name, role, dept, tenure, is_active, is_admin |

| `messages` | id, employee_id, data_source_id, text, embedding |

| `tags` | id, tag_name, is_active |

| `employee_tag` | id, tag_id, employee_id, is_active |

| `data_sources` | id, platform, external_id, name, tag_id, description, is_active |

- Represents all individuals using or referenced by the platform.

- Attributes: `id`, `name`, `role`, `department`, `tenure`, `is_active`.

- Linked to multiple tags and messages.

- Stores chat data from Google Chat or Telegram.

- Each message is tied to one employee and one data source.

- Stores cleaned text and a semantic embedding vector.

- Represents thematic or organizational labels (e.g., *Project Alpha*, *DevOps*).

- Used to group employees and messages under meaningful categories.

- Represents external chat systems (Telegram group, Google Chat space).

- One data source → one tag; one tag → many data sources.

| Relationship | Type | Description |

|---------------|------|-------------|

| Employee ↔ Message | 1 → N | One employee can send multiple messages |

| Employee ↔ Tag (via employee_tag) | M ↔ N | Employees can belong to multiple tags |

| Message ↔ Data Source | 1 → N | Each message belongs to one data source |

| Data Source ↔ Tag | 1 → N | One tag can link to multiple data sources |

| Tag ↔ Employee_Tag | 1 → N | Each tag can appear in multiple employee_tag records |

| Employee ↔ Employee_Tag | 1 → N | Each employee can have multiple tag associations |

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- **Retriever:** Uses pgvector to fetch top-K relevant messages.

- Performs Q&A generation.

- **LangChain:** Used for the RAG (Retrieval-Augmented Generation) pipeline.

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**Framework:** FastAPI

| Method | Endpoint | Description |

|--------|-----------|-------------|

| GET | `/employee/{id}` | Retrieve a specific employee’s profile |

| GET | `/employee` | List all employees (Admin only) |

| POST | `/message/bulk_ingest` | Store messages fetched from integrations |

| POST | `/tag` | Create or update a tag (Admin only) |

| GET | `/tag` | Retrieve all active tags |

| GET | `/tag/{id}` | Get tag details with linked employees & sources |

| POST | `/employee_tag` | Assign or unassign a tag to an employee |

| GET | `/employee_tag/{employee_id}` | Fetch tags assigned to a specific employee |

| POST | `/data_source` | Add or update a data source |

| GET | `/data_source` | List all configured data sources |

| GET | `/data_source/{id}` | Get details of a specific data source |

| POST | `/chatbot/query` | Ask a natural language question |

| GET | `/admin/overview` | Dashboard summary for Admins |

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- Displays employee details, department, tenure, and assigned tags.

- Uses `/employee/{id}` and `/employee_tag/{employee_id}` APIs.

- Interactive chat that calls `/chatbot/query`.

- Allows employees to query messages related to assigned tags.

- Similar to employee view, with added admin controls.

- Create/edit tags via `/tag` API.

- Assign tags to employees via `/employee_tag`.

- Manage Telegram/Google Chat integrations.

- Create or deactivate sources using `/data_source`.

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**Example Query:** “Did we use Kubernetes in any project?”

1. Chatbot API receives query.

2. NLP extracts keyword → *Kubernetes*.

3. pgvector performs semantic similarity search.

4. Relevant messages + metadata passed into LLM.

5. LLM responds → “Yes, Kubernetes was used in Project Alpha (2024).”

6. Frontend chatbot displays the answer with source link.

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| Component | Technology |

|------------|-------------|

| **Frontend** | React, Tailwind |

| **Backend** | FastAPI (Python) |

| **Database** | PostgreSQL + pgvector |

| **AI/NLP** | OpenAI Embeddings, GPT-4 |

| **Integrations** | Google Chat API, Telegram Bot API |

| **Infra** | Docker Compose (MVP), AWS/DigitalOcean |

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- Data processed locally or within client’s environment.

- No raw chat data stored post-extraction.

- Encryption: HTTPS (in transit), AES-256 (at rest).

- Role-based access: Admin, Manager, Employee.

- OAuth 2.0 for integrations (Google, Telegram).

- Logs and audit trails for sensitive actions.

- Privacy-by-design principles.

- Data minimization: only relevant skill/project data extracted.

- Right to erasure and consent-based processing.

- Future goal: SOC 2 Type 2 & GDPR certification.

- Temporary data auto-deletion post-processing.

- PII replaced with internal IDs before storage.

- Configurable retention policies per client.

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- Integration with Slack, Microsoft Teams, Jira, Confluence, etc.

- **Employee Handover Package Generator** during role transitions.

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