You will get a chatbot which give answer according to data you put in it..
Project details
This project implements a Retrieval-Augmented Generation (RAG) system that combines information retrieval with large language model (LLM) capabilities to generate accurate and context-aware responses. The system retrieves relevant context from a knowledge base before generating an answer, significantly improving factual accuracy and reducing hallucinations in AI-generated content.
The pipeline involves:
Data Ingestion & Chunking: Documents are preprocessed, split into semantically meaningful chunks, and stored in a vector database (e.g., Pinecone, Chroma, or FAISS).
Embedding Generation: Each chunk is embedded using an OpenAI or SentenceTransformer model to enable efficient semantic search.
Retrieval Pipeline: At query time, the system fetches top relevant chunks from the vector store using cosine similarity or dot-product scoring.
Contextual Generation: Retrieved context is passed along with the user query to an LLM (e.g., GPT-4 or Llama) to produce a grounded, reference-backed response.
Evaluation: The outputs are tested for relevance, accuracy, and coherence using both automated metrics and manual review.
The pipeline involves:
Data Ingestion & Chunking: Documents are preprocessed, split into semantically meaningful chunks, and stored in a vector database (e.g., Pinecone, Chroma, or FAISS).
Embedding Generation: Each chunk is embedded using an OpenAI or SentenceTransformer model to enable efficient semantic search.
Retrieval Pipeline: At query time, the system fetches top relevant chunks from the vector store using cosine similarity or dot-product scoring.
Contextual Generation: Retrieved context is passed along with the user query to an LLM (e.g., GPT-4 or Llama) to produce a grounded, reference-backed response.
Evaluation: The outputs are tested for relevance, accuracy, and coherence using both automated metrics and manual review.
AI Algorithms
AdaBoost, Generative Adversarial Network, Multimodal Large Language ModelAI Applications
AI Chatbot, AI-Enhanced Classification, AI-Generated Code, Conversational AI, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Bing AI, GitHub Copilot, Gradio, Hugging Face, Jasper AI, Microsoft 365 Copilot, PyTorch, Replit, TensorFlowAI Models
ChatGPT, GPT-3, GPT-4, GPT-Neo, LaMDA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$200
|
Standard
$400
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 0 | 0 | 2 |
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.
Additional Revision
+$10
2 reviews
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BS
Brant S.
May 5, 2026
AI Pinecone LangGraph Work
Shubham was amazing to work with, we plan to work with him more in the future and is top of our list of people to recommend and to repeat work with.
BS
Brant S.
Dec 31, 2025
Python Developer for creating API libraries
Very adaptable and very adept at a number of different domains, really enjoyed working with him and the work he did!
About Shubham
AI Developer| Voice AI Agent| AI Automation| AI Integration Expert |AI
100%
Job Success
Noida, India - 5:57 am local time
Available 30+ hrs/week | Real-World AI Experience
I build AI systems that run in production — not demos or prototypes.
Most recently shipped a production AI SaaS platform with 55+ Claude-powered task types, 200+ API routes, 95+ React components, and real-time streaming — processing live data through structured multi-step AI pipelines. Also built a 4-agent autonomous system where AI agents coordinate daily through scheduled crons, governance rules, and self-learning feedback loops.
WHAT I BUILD
Multi-Agent AI Systems
Built production multi-agent pipelines using Claude, OpenClaw, and Paperclip. Agents with defined roles, communication channels, budget controls, and audit trails. Systems that run autonomously and evolve their own strategy.
AI-Powered SaaS Products
Full stack — Next.js, TypeScript, Supabase (PostgreSQL), Claude API, Tailwind CSS, Vercel. From Figma designs to deployed product. Structured AI pipelines that generate personalized outputs from user data — not generic chatbot wrappers.
AI Automation & CRM Integration Production n8n workflows with multi-step LLM chaining, conditional branching, Gmail/Calendar triggers, and database sync. Bidirectional CRM integrations with deduplication and conflict resolution.
Backend & Data Pipelines
Python, Node.js, FastAPI, real-time streaming with Redis/Kafka, PostgreSQL, MongoDB. Scheduled cron jobs, webhook handlers, retry logic, and rate limiting. Built for reliability at scale.
STACK
AI: Claude API (Opus + Sonnet), OpenAI, Prompt Engineering, RAG, LangChain
Frontend: Next.js, React, TypeScript, Tailwind CSS Backend: Python, Node.js, FastAPI, NestJS
Database: PostgreSQL, Supabase, MongoDB, Redis
Automation: n8n, OpenClaw, Trigger.dev
DevOps: Docker, AWS, Vercel, CI/CD
I am selective about the projects I take on. The ones I do, I deliver exceptionally well. If you need AI that actually works at scale, let us talk.
Steps for completing your project
After purchasing the project, send requirements so Shubham can start the project.
Delivery time starts when Shubham receives requirements from you.
Shubham works on your project following the steps below.
Revisions may occur after the delivery date.
I will make sure project will done on time..