You will get a highly accurate agentic RAG chatbot for diverse data with a prod backend
Project details
You will get a production-ready RAG (Retrieval-Augmented Generation) chatbot tailored to your data and use case. I specialize in building intelligent systems that don’t just retrieve information, but understand context and deliver accurate, reliable responses.
With hands-on experience in building real-world RAG pipelines and agentic workflows, I focus on performance, scalability, and clean architecture. Whether you need a simple document-based chatbot or a backend API with advanced retrieval, I design solutions that are efficient and easy to extend.
The system I deliver is optimized for high-quality responses, supports multiple data formats, and includes clean code, setup instructions, and optional deployment support. My goal is to help you turn your data into a smart, usable AI system and not just a demo.
With hands-on experience in building real-world RAG pipelines and agentic workflows, I focus on performance, scalability, and clean architecture. Whether you need a simple document-based chatbot or a backend API with advanced retrieval, I design solutions that are efficient and easy to extend.
The system I deliver is optimized for high-quality responses, supports multiple data formats, and includes clean code, setup instructions, and optional deployment support. My goal is to help you turn your data into a smart, usable AI system and not just a demo.
AI Algorithms
Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, PyTorch, Streamlit, Word2vecAI Models
GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$200
|
Standard
$500
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
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
+$50 - $200Frequently asked questions
About Akash
LLM / GenAI Engineer | RAG, Agents, AI Backends
Bengaluru, India - 3:30 am local time
I design and deploy end-to-end AI systems that work in real-world environments — from document intelligence and knowledge graph retrieval to scalable LLM-powered applications.
AI engineer with 3+ years of experience building production-grade GenAI systems, RAG pipelines, multi-agent workflows, and AI backends for real-world use cases. I have worked on document intelligence platforms, knowledge graph retrieval systems, LLM-powered chat systems, and secure on-premise deployments for high-stakes environments.
My focus is on building reliable AI systems that solve business problems end to end — from data ingestion and retrieval to orchestration, deployment, and optimization. I work with LangChain, LangGraph, FastAPI, Neo4j, MongoDB, vector databases, and LLM inference stacks, with experience spanning backend architecture, hybrid retrieval, and domain-specific model adaptation.
If you need an engineer who can design and deliver practical AI systems rather than just prototypes, I can help.
What I build:
- RAG pipelines (multi-source, hybrid retrieval)
- Knowledge graph + Neo4j systems
- Multi-agent AI workflows (LangGraph, LangChain)
- LLM-powered chat systems (API + integrations)
- On-prem / secure AI deployments
What makes my work different:
- Production-first (not just prototypes)
- Handles large-scale data with optimized retrieval
- Token-efficient context orchestration
- Clean, modular backend architecture
Tech I work with:
LangChain, LangGraph, FastAPI, Neo4j, MongoDB, Vector DBs, LLM APIs, Docker, vLLM, llama.cpp, JS, TS
Steps for completing your project
After purchasing the project, send requirements so Akash can start the project.
Delivery time starts when Akash receives requirements from you.
Akash works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & System Design
Understand use case, data sources, and design RAG architecture
Data Processing & Embeddings
Clean, chunk, and convert documents into vector embeddings based on modality and expected working