You will get Production Agentic RAG System Over Your Documents & Data
Project details
Most RAG demos work on a clean PDF and fall apart on a real document set. Inconsistent formats, tables, scanned pages, contradictory versions, questions that span three documents at once.
I build RAG systems that hold up on real data. Chunking and retrieval strategy tuned to your actual query patterns, evaluation against real questions from your team, and citations so users can verify answers rather than trust them blindly.
I've built an offline-capable legal document system with a local/cloud model toggle for a team that couldn't send client files to a cloud API — fully air-gapped, running on their own hardware. Document lookups went from ~15 minutes to under 30 seconds.
Where data sensitivity matters, I can run everything locally with open models so nothing leaves your infrastructure. Where it doesn't, cloud APIs are usually faster and cheaper — I'll tell you which fits.
You get the system, the documentation, and a handover walkthrough.
I build RAG systems that hold up on real data. Chunking and retrieval strategy tuned to your actual query patterns, evaluation against real questions from your team, and citations so users can verify answers rather than trust them blindly.
I've built an offline-capable legal document system with a local/cloud model toggle for a team that couldn't send client files to a cloud API — fully air-gapped, running on their own hardware. Document lookups went from ~15 minutes to under 30 seconds.
Where data sensitivity matters, I can run everything locally with open models so nothing leaves your infrastructure. Where it doesn't, cloud APIs are usually faster and cheaper — I'll tell you which fits.
You get the system, the documentation, and a handover walkthrough.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, StreamlitAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$1,499
|
Standard
$3,499
|
Advanced
$5,999
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 20 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.
Monthly Maintenance & Model Monitoring
+$500Frequently asked questions
About Girish Kumar
n8n Automation & Agentic RAG Engineer | LangGraph, MCP, Python
Bengaluru, India - 12:44 am local time
unattended — not demos, not prototypes.
Most AI automation projects fail at the same place: they work in a
demo and break on real data. I've shipped four systems currently
running in production, including an n8n lead-scoring pipeline
processing 15+ signals into a live CRM, and an offline-capable legal
document RAG chatbot with a local/cloud model toggle for
air-gapped environments.
WHAT I BUILD
- n8n workflow automation — multi-step pipelines with error handling,
logging, retries, and human-in-the-loop approval gates
- Agentic RAG — retrieval + tool-calling + orchestration with LangGraph,
ChromaDB and other vector stores, sentence-transformers
- n8n as an MCP server with Claude as MCP client — the setup that lets
an LLM safely trigger your real business workflows
HOW I WORK
Fixed-scope, fixed-price packages. You get a working system, full
documentation, and a handover walkthrough — so you're not dependent
on me to run it.
Stack: n8n, LangGraph, Python, FastAPI, Docker, ChromaDB,
sentence-transformers, GCP, Streamlit.
Certified: IBM AI Engineering
I do not take on: generic ML model training, data labeling, or
staff-augmentation PM roles.
Let’s Work:
Currently taking on: production builds, agency white-label delivery,
and ongoing retainers.
If you have a workflow that breaks under real data — or a RAG system
that works in testing and fails in front of users — send me the
details and I'll tell you straight whether I'm the right fit.
Steps for completing your project
After purchasing the project, send requirements so Girish Kumar can start the project.
Delivery time starts when Girish Kumar receives requirements from you.
Girish Kumar works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and retrieval strategy
We define your document set, query patterns, and accuracy bar. I recommend a chunking, embedding, and retrieval approach before building.
Ingestion and indexing
I build the pipeline that parses, chunks, embeds, and indexes your documents into a vector store, handling messy real-world formats.