You will get a production-ready AI agent with RAG and MCP
Project details
I build custom AI agents and AI-powered systems tailored to your business needs. From RAG chatbots and API integrations to agentic workflows, MCP, automation, and production deployment, I deliver practical, scalable solutions using modern AI technologies.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer Model, YOLOAI Applications
AI Chatbot, AI-Enhanced Classification, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language Understanding, Synthetic Data GenerationAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$299
|
Standard
$799
|
Advanced
$1,999
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 7 |
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 |
Frequently asked questions
About Ankit
RAG and Agentic AI Engineer - AWS Bedrock, Azure OpenAI, FastAPI
Gurugram, India - 9:13 am local time
companies actually ship — not prototypes that stall before deployment.
4 years at Barclays: engineered GenAI platforms at enterprise scale —
50K+ documents indexed, 15K daily inference requests, RAG pipelines
serving 4 business units, and MCP-based agents cutting manual
coordination by 30%.
What I deliver for clients:
→ RAG Pipelines (end-to-end): chunking strategy, embedding, vector
indexing, metadata-filtered retrieval, evaluation with RAGAS,
production deployment on AWS/Azure
→ Agentic Workflows: MCP server development, tool-calling agents over
your internal systems (Jira, Confluence, databases), AWS Strands SDK
→ LLM to Production: take your working prototype and make it fast,
monitored, and scalable — batching, caching, async inference,
observability dashboards
→ Full-Stack AI Apps: FastAPI backend + Next.js frontend with streaming,
auth, payments, and real-time dashboards (built AGentX, a live
AI SaaS platform)
Stack: Python · FastAPI · LangChain · LangGraph · AWS Bedrock ·
Azure OpenAI · Claude API · Pinecone · FAISS · Supabase · Next.js 16 ·
Docker · CI/CD
If you're building something in the LLM/AI space and need it to work
in production — not just in a notebook — let's talk.
Steps for completing your project
After purchasing the project, send requirements so Ankit can start the project.
Delivery time starts when Ankit receives requirements from you.
Ankit works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Planning
Review your requirements, use case, data, integrations, and define the solution architecture.
AI Development
Build the AI agent, RAG pipeline, tools, APIs, workflows, and required functionality.