Business Objective / Goal
To improve accessibility and utilization of internal organizational knowledge through a secure, intelligent AI chatbot that enables employees to retrieve accurate information instantly using natural language queries—enhancing productivity and setting the stage for external solution deployment.
Solutions & Implementation
- Designed a modular AI chatbot architecture using OpenAI GPT-4 for LLM-based language understanding and generation.
- Built API integrations with internal systems (Google Workspace, Slack, Zoom Docs, HubSpot) to unify access to diverse knowledge sources.
- Integrated Vector Databases (Pinecone/FAISS) to enable high-speed semantic search and contextually relevant retrieval.
- Used Apache Airflow to automate preprocessing pipelines and maintain consistent document quality.
- Deployed the solution on AWS Cloud with containerization via Docker and Kubernetes to ensure elasticity and scalability.
- Implemented enterprise-grade security using OAuth 2.0, RBAC, data encryption, and detailed user audit logs.
Major Technologies Used
- AWS – Infrastructure scalability, security, and availability
- OpenAI GPT-4 – NLP and generative response engine
- Vector Databases (Pinecone/FAISS) – Semantic search retrieval
- Kafka – Real-time messaging and event streaming
- PostgreSQL – Metadata storage and user feedback tracking
- Docker, Kubernetes – Containerized deployment and orchestration
- OAuth 2.0, RBAC – Secure access and permission control
Business Outcomes
- Faster Knowledge Discovery Rapid and secure access to internal knowledge, reducing employee search time significantly.
- Enhanced Collaboration Boosted documentation access and cross-team communication through a unified chatbot interface.
- Self-Service Intelligence Delivered personalized, context-aware responses and continuous improvement through feedback analytics.
- Scalable Solution Framework Laid the foundation for white-labeled, client-facing knowledge assistants—boosting the organization’s competitive edge.
