You will get an AI chatbot grounded in your data with RAG, citations, and streaming
Project details
You'll get an AI chatbot that answers your users' questions using your content — not ChatGPT's general knowledge. The model is strictly grounded in the docs, FAQs, knowledge base, or website you provide, so it can't hallucinate competitor information, make up your pricing, or wander off into off-topic conversations.
What I build:
• Streaming chat UI matched to your existing site's design system
• Retrieval-augmented generation (RAG) over your content with chunking, embeddings, and vector search tuned to your data shape
• Citations on every answer so users can verify the source
• Safety guardrails: rate limiting, prompt-injection defense, content-policy enforcement
• Self-serve content updates so adding new docs doesn't require a redeploy
Tech I use: OpenAI / Anthropic Claude / Groq (your choice), Next.js, TypeScript, Pinecone or MongoDB Atlas Vector, edge functions.
Live proof at rajdeepratan.com — my portfolio uses the same pattern. The AI in the hero answers only from real data and refuses to invent.
I build chatbots founders ship to real customers. Message me with your site URL and what you want the bot to do.
What I build:
• Streaming chat UI matched to your existing site's design system
• Retrieval-augmented generation (RAG) over your content with chunking, embeddings, and vector search tuned to your data shape
• Citations on every answer so users can verify the source
• Safety guardrails: rate limiting, prompt-injection defense, content-policy enforcement
• Self-serve content updates so adding new docs doesn't require a redeploy
Tech I use: OpenAI / Anthropic Claude / Groq (your choice), Next.js, TypeScript, Pinecone or MongoDB Atlas Vector, edge functions.
Live proof at rajdeepratan.com — my portfolio uses the same pattern. The AI in the hero answers only from real data and refuses to invent.
I build chatbots founders ship to real customers. Message me with your site URL and what you want the bot to do.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-3, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$600
|
Standard
$2,200
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 28 days |
Number of Revisions | 2 | 4 | 6 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | - |
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | ||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$300 - $1,000
Multi-Language Support
(+ 3 Days)
+$400
Conversation Analytics Dashboard
(+ 4 Days)
+$500
30-Day Post-Launch Support
+$500Frequently asked questions
About Rajdeep
Senior AI Engineer & Full-Stack Architect
Ghaziabad, India - 6:32 pm local time
Most of my work sits at the intersection of three things:
- AI/LLM integration: designing prompt systems, streaming chat interfaces, RAG retrieval over real data, rate-limiting and abuse mitigation for public-facing LLM endpoints, and the safety scaffolding that keeps models on-topic.
- Production-grade Next.js & TypeScript: App Router, server components, edge middleware, NextAuth, MongoDB/Mongoose, and modern UI with Tailwind 4 + shadcn/ui. I write code that ships, not prototypes that rot.
- Cloud & system design: AWS, Vercel, MongoDB Atlas. I think about cost, latency, observability, and accessibility from day one, not as an afterthought.
A recent example I can point to: I designed and shipped my own portfolio application: a Next.js 16 + Groq-powered portfolio where visitors chat with an AI grounded in real MongoDB data. It runs on edge middleware with IP rate-limiting, ships at 100/100 Lighthouse accessibility, and respects prefers-reduced-motion across every animation. Live, in production, open to inspection.
I work best with clients who care about quality and want a thinking partner, not a code typist. If you're building something with AI at its core or trying to integrate AI into an existing product without breaking it. I'd love to talk.
Steps for completing your project
After purchasing the project, send requirements so Rajdeep can start the project.
Delivery time starts when Rajdeep receives requirements from you.
Rajdeep works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & content audit
30-min call to understand your chatbot's job, audience, and knowledge sources. I send back a written scope and content checklist before any work starts.
Chunking & embedding strategy
I review your docs, FAQs, or site content; design a chunking strategy and choose an embedding model that fits your data shape, budget, and recall needs.