You will get a hybrid knowledge graph, agentic RAG system for project intelligence
Project details
You will get a production-ready project intelligence backend that combines knowledge graphs, retrieval-augmented generation, and semantic search to help teams understand complex project data with ease. I build systems that ingest documents and diagrams, extract structured relationships, and store them in Neo4j alongside vector embeddings for fast, relevant retrieval.
What makes this project different is the orchestration layer: it can answer project-specific questions, summarize workflows, and explain architecture in natural language while managing large context safely through token budgeting, compression, and scoped retrieval. The result is grounded, scalable, and built for real-world use rather than a simple demo.
What makes this project different is the orchestration layer: it can answer project-specific questions, summarize workflows, and explain architecture in natural language while managing large context safely through token budgeting, compression, and scoped retrieval. The result is grounded, scalable, and built for real-world use rather than a simple demo.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer Model, Variational AutoencoderAI Applications
AI Chatbot, AI-Generated Code, AIOps, Conversational AI, Natural Language Understanding, Sequence ModelingAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, NVIDIA AI Platform, PyTorchAI Models
ChatGPT, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$400
|
Standard
$800
|
Advanced
$1,300
|
|---|---|---|---|
| Delivery Time | 5 days | 10 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 |
Frequently asked questions
About Akash
LLM / GenAI Engineer | RAG, Agents, AI Backends
Bengaluru, India - 11:57 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 graph + RAG architecture
Data Ingestion & Graph Setup
Process documents, create embeddings, and build Neo4j knowledge graph