NikhilGPT is a personal AI knowledge agent designed to convert Nikhil's professional experience, project portfolio, achievements, BFSI domain expertise, and AI initiatives into an intelligent searchable assistant.
The objective was to build a practical AI system that can answer questions about Nikhil's professional background in a structured and scalable way while staying grounded in curated source documents.
Why NikhilGPT exists
Personal Knowledge Retrieval
Answer questions about Nikhil's background, projects, achievements, banking transformation work, technical expertise, and AI initiatives.
Interactive Professional Showcase
Move beyond a static resume by providing an AI-powered professional interface.
Applied AI Implementation
Demonstrate practical implementation of RAG, embeddings, vector search, prompt engineering, session memory, fallback handling, and evaluation automation.
What it can do
Answer questions about professional profile
Retrieve project achievements and case studies
Summarize banking transformation experience
Present technical and leadership capabilities
Explain AI initiatives
Handle greetings and assistant capability questions
Escalate unanswered questions using fallback email capture
End-to-end request flow
From user query to grounded response — with vector search, session memory, and fallback escalation.
Six engineered layers
01 Knowledge Ingestion Layer
- Google Drive stores source documents
- n8n detects new or updated files
- Text is extracted and prepared for retrieval
- text-embedding-3-small generates embeddings
- Pinecone stores vectorized chunks
02 Query and Retrieval Layer
- User submits question through web interface
- n8n receives query through webhook
- Retrieval-optimized query is generated
- Pinecone retrieves relevant chunks
03 Response Generation Layer
- GPT-4o-mini receives user query, memory, and retrieved context
- Response is generated as Nikhil's AI Assistant
- Answers remain grounded in curated documents
04 Session Memory Layer
- Redis stores recent conversation turns per active session
- Enables follow-up questions like “tell me more” and “what was the impact?”
05 Fallback Handling Layer
- If relevant information is not available, the assistant asks for email
- Question is forwarded to Nikhil
- User receives confirmation
06 Evaluation Layer
- Google Sheets stores structured test questions
- n8n runs automated evaluation
- Outputs are captured for quality review
The applied AI stack
Retrieval-Augmented Generation
Embeddings
Vector Search
Prompt Engineering
Session-Based Conversation Memory
Fallback-to-Human Workflow
LLM Evaluation
Measured outcomes
100
Test questions
80
Answered normally
20
Fallback responses
80%
Overall answer rate
100%
Boundary compliance
88.9%
Non-boundary answer coverage
Problems tackled along the way
Cluttered RAG source data
Restructured source documents and reduced duplicated information.
Binary response behavior
Refined prompts to improve intent handling and reduce unnecessary fallbacks.
Lack of conversational continuity
Added Redis session memory.
Weak visibility into performance
Created automated evaluation workflow with Google Sheets.
NikhilGPT demonstrates how RAG, vector search, workflow orchestration, session memory, fallback escalation, and evaluation can be combined into a working professional AI assistant.
What's next
Improve recruiter-style response synthesis
Reduce false fallback on valid questions
Improve metadata and chunking strategy
Add better conversation state handling
Enhance evaluation scoring
Refine UI/UX