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2025-09-24 04:21:14 Prashant Kumar: update remaining sections in project
Projects/AnswerVault.md ..
@@ 43,9 43,32 @@
3. For Managers: Clear risk visibility, succession planning, workforce insights.
4. For Organizations: Preserve IP, resilience, smoother transitions.
- * **How does it work?** Describe the architecture, data flow, and the AI models/technologies you're using (e.g., TensorFlow, PyTorch, GPT-4, Hugging Face, OpenCV).
+ * **How does it work?**
+
+ **Architecture**
+ * Data Sources → HRMS, LMS, reviews, chat tools
+ * Data Processing → ETL pipelines, skill/role normalization
+ * Knowledge Graph → employees ↔ skills ↔ roles ↔ successors
+ * AI Layer →
+ * NLP for skill extraction/matching
+ * ML for attrition risk & successor readiness
+ * LLM chatbot for Q&A
+ * App Layer → dashboards, chatbot, alerts (Google Chat/Telegram)
+
+ **Data Flow**
+ 1. Collect & clean HR/learning/performance data
+ 2. Build knowledge graph of roles & skills
+ 3. Run AI models → risk prediction & readiness scoring
+ 4. Deliver insights via dashboards + chatbot
+
* **What makes it innovative?**
+ 1. Deeper knowledge capture (not just HR data, also chats, code, unstructured docs).
+ 2. Automated handover + AI-clone / chatbot of past work.
+ 3. Detailed knowledge graph showing who owns what modules, and linking artifacts.
+ 4. Continuous ingestion & update rather than periodic HR efforts.
+ 5. Contextual Q&A over actual work vs static competency profiles.
+
## Technology Stack
* NLP/LLM: OpenAI (GPT-4),Laama,
@@ 58,37 81,34 @@
## Project Architecture
- (Optional but highly recommended for complex projects. A diagram works best here. You can describe it if you can't add an image.)
- This wiki also supports "mermaid" where you can create architectural diagrams using text.
```mermaid
graph LR
- A[ THIS IS ] -- Link text --> B((MERMAID))
- A --> C(Round Rect)
- B --> D{Rhombus}
- C --> D
+ A[Data Sources -> HR, Chats, Docs, Code ] --> B[Data Ingestion Pipelines -> APIs, ETL, LLM extractors]
+ B --> C[Knowledge Store -> Postgres + pgvector + ElasticSearch]
+ C --> D[Knowledge Graph -> Employees ↔ Projs]
+ D --> E[AI Services Layer, LLM Q&A - LangChain, Succession Risk, Handover Gen]
+ E --> F[Client Interfaces, Web Dashboards - React, Chatbot - Telegram/GC ]
```
## Challenges & Learnings
- What was the hardest part of this project? What did your team learn about the AI models, the data, or the problem domain?
+ -- Add later
## Future Roadmap
What are the next steps for this project if it were to continue?
- * [ ] Short-term goal (e.g., Improve model accuracy to 95%)
- * [ ] Medium-term goal (e.g., Launch a mobile app)
- * [ ] Long-term goal (e.g., Open-source the project)
+ * [ ] Short-term goal (Integration with Google chats and Telegram, AI chat bot, dashboards (Employee profile, Knowledge graph)
+ * [ ] Medium-term goal -- Add later
+ * [ ] Long-term goal -- Add later
## Repository & Live Demo
- * **GitHub Repository:** [Link to your code repo here]
- * **Live Demo:** [Link to your live demo or video walkthrough here] (Highly encouraged!)
+ * **GitHub Repository:** -- Add later
+ * **Live Demo:** -- Add later
## Screenshots / Demo Video
- (Embed screenshots of your working application or a video demo here. A picture is worth a thousand words.)
-
- ---
+ -- Add later
- **Categories:** #project #ai-hackathon-[year] #category-[your-topic]
+ **Categories:** #project #ai-hackathon-[2025] #category-[HR, Succession Planner]
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