You will get an AI Retrieval-Augmented Generation(RAG) and MCP Developer
Rising Talent
Rising Talent
Project details
Stop building fragile AI integrations. Start building standardised, scalable AI Agents.
I develop next-generation AI applications using RAG for accurate data retrieval and the Model Context Protocol (MCP) for secure, standardised tool connections.
Whether you need a chatbot that talks to your internal PDFs or an autonomous agent that connects to your GitHub, Google Drive, and Slack without hallucinating, I can build it.
Why Choose This Service?
RAG Expertise: I prevent hallucinations by grounding your AI in your data (PDFs, SQL, Notion).
MCP Standard: I use the new industry-standard protocol (MCP) to connect LLMs to your tools
Tech Stack: LangChain, LlamaIndex, Pinecone, and Anthropic's Claude / OpenAI's GPT-4.
3. Technologies & Areas
LLMs: GPT-4o, Claude 3.5 Sonnet (Best for MCP), Llama 3 (Open Source).
Orchestration: LangChain, LangGraph, LlamaIndex.
Vector Databases (for RAG): Pinecone, Weaviate, ChromaDB, Qdrant.
Protocols: Model Context Protocol (MCP) (SDKs in TypeScript/Python).
Backend: Python (FastAPI), Node.js.
Specific Use Cases:
"Chat With Your Data" RAG Systems:
Ingest PDF, CSV, or Docx files into a vector store.
Users ask questions and get answers with citations.
I develop next-generation AI applications using RAG for accurate data retrieval and the Model Context Protocol (MCP) for secure, standardised tool connections.
Whether you need a chatbot that talks to your internal PDFs or an autonomous agent that connects to your GitHub, Google Drive, and Slack without hallucinating, I can build it.
Why Choose This Service?
RAG Expertise: I prevent hallucinations by grounding your AI in your data (PDFs, SQL, Notion).
MCP Standard: I use the new industry-standard protocol (MCP) to connect LLMs to your tools
Tech Stack: LangChain, LlamaIndex, Pinecone, and Anthropic's Claude / OpenAI's GPT-4.
3. Technologies & Areas
LLMs: GPT-4o, Claude 3.5 Sonnet (Best for MCP), Llama 3 (Open Source).
Orchestration: LangChain, LangGraph, LlamaIndex.
Vector Databases (for RAG): Pinecone, Weaviate, ChromaDB, Qdrant.
Protocols: Model Context Protocol (MCP) (SDKs in TypeScript/Python).
Backend: Python (FastAPI), Node.js.
Specific Use Cases:
"Chat With Your Data" RAG Systems:
Ingest PDF, CSV, or Docx files into a vector store.
Users ask questions and get answers with citations.
AI Algorithms
Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AI-Generated Code, Conversational AI, Machine Translation, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, Copy.ai, GitHub Copilot, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, DALL-E, GPT-4, LLaMA, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$300
|
Standard
$800
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 0 | 1 | 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
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JZ
Jared Z.
Feb 14, 2026
NVIDIA Jetson GStreamer Developer: Video Sync Prototype (MUST OWN HARDWARE)
It was great working with Chidananda and hoping to work together again soon on our upcoming projects
AC
Akhilesh C.
Jan 12, 2026
Integrating API Response with WordPress Website
Chidananda and his team did an excellent job with the website and completed all the requirements on time, after giving accurate effort estimates and ETAs for completion. I will definitely look forward to working with them again in the future, and I strongly recommend them for their professionalism and quality of service. Kudos!
AT
Abhishek T.
Nov 26, 2025
Urgent: 8 html page template
RR
Ravi R.
Feb 22, 2025
Need help building a web app
GR
Good R.
Apr 8, 2024
Set up page in 3Js
About Chidananda
AI Architect, Full Stack, mobile and embedded developer
100%
Job Success
Bengaluru, India - 11:41 am local time
Bridging the gap between hardware, cloud, and modern AI.
With over 23 years of software engineering experience and 5+ years specialized in AI, I don't just write code—I architect complete, end-to-end ecosystems. My background spans from silicon-level embedded programming at industry giants like ZiLOG and Honeywell to building cutting-edge Generative AI and Edge Computing solutions for modern startups.
I specialize in "Edge AI"—bringing intelligence out of the cloud and onto devices. Whether you need a RAG system for complex data processing, a computer vision model running on an ESP32/Nvidia Jetson, or a full-stack Flutter application with voice capabilities, I deliver robust, scalable, and patent-worthy solutions.
Why work with me?
Deep Industry Roots: Experience with major players (Honeywell, Cricut, ZiLOG) and 2 filed patents.
Full-Cycle Architect: I handle everything from PCB-level constraints and firmware to Cloud AI APIs and mobile app UIs.
Agile & Reliable: Certified Scrum Master with a focus on System Engineering discipline—I deliver on time and budget.
Featured Projects & Case Studies
1. Edge AI & Embedded Vision
Smart IoT Vision System (ESP32-S3 & OV5640): Engineered a low-power IoT camera solution capable of on-device image capturing and processing. Handled driver integration and memory optimization for the ESP32 architecture.
Edge Computing Optimization (Nvidia Jetson): Architected AI solutions utilizing Edge GPUs and NPUs. Focused on migrating heavy cloud workloads to local hardware for reduced latency and offline capability.
Consumer Electronics (Honeywell & Cricut):
Architected a Next-Gen DAS (DIY Awareness & Security) device integrating Amazon Echo, Z-Wave, Zigbee, and proprietary wireless protocols.
Developed embedded firmware for an IoT Thermostat competing directly with Nest.
Led development for Universal Remote Controls and IR Blasters.
2. Generative AI (LLMs) & RAG Systems
RAG System for Insurance Processing: Designed and built a Retrieval-Augmented Generation (RAG) system to automate insurance claim analysis. The system ingests complex policy documents and provides accurate, context-aware answers to claims adjusters.
Voice-First AI Agents:
Resort Receptionist Agent: Developed a conversational AI voice agent to handle booking inquiries and guest services autonomously.
In-Car Voice Assistant: Architected a voice-command application for automotive use, enabling hands-free booking, shopping, and travel planning while driving.
Document Intelligence Engine: Led full-stack development of a tool using ChatGPT APIs to extract structured data from resumes and unstructured legal documents.
3. Full-Stack Web & Mobile Applications
HealthTech Mobile Platform ("Qavach" & "E-pad"): Developed comprehensive preventive healthcare solutions for children. Features include appointment booking, prescription digitization via camera (OCR), and secure cloud storage. Built using Flutter for mobile and AWS for the backend.
Digital Wellness App ("Disconnect Daily"): Created a gamified mobile application (Flutter) aimed at reducing screen time for children (ages 5-11). Worked closely with medical professionals to implement logic that promotes healthy digital habits.
Commercial Security Mobile App: Built a cross-platform Flutter app for managing commercial security systems, featuring real-time alerts and device management.
Technical Arsenal
🤖 Artificial Intelligence & Machine Learning
Generative AI: LLM Integration (OpenAI/ChatGPT, Gemini, DeepSeek, Llama, Mistral), RAG Pipelines, AI Agents.
Computer Vision: OpenCV, Object Detection, Face Recognition, On-device Vision.
Edge AI: Nvidia Jetson, NPU optimization, TinyML.
Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, Pandas, NumPy.
📱 Full-Stack & Mobile
Mobile: Flutter (Dart), React Native, Kotlin, Swift.
Web: Node.js, React.js, Next.js, Python, PHP.
Databases: Google Firestore, PostgreSQL, MongoDB, MySQL
🔌 Embedded & IoT
Hardware: ESP32, 8/16/32-bit Microcontrollers, Raspberry Pi.
Protocols: MQTT, Z-Wave, Zigbee, BLE, Wi-Fi.
Languages: C, C++, Embedded C.
☁️ Cloud & DevOps
Platforms: AWS, Microsoft Azure, Google Cloud Platform (GCP).
Tools: Docker, CI/CD Pipelines, Git.
Certifications & Governance
Patents: 2 Filed Patents (US 9743252 and US 20190221092).
Process: Certified Agile Scrum Master.
Governance: Expertise in AI Ethics, bias mitigation, and data privacy/security standards.
Steps for completing your project
After purchasing the project, send requirements so Chidananda can start the project.
Delivery time starts when Chidananda receives requirements from you.
Chidananda works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Access Setup
We start with a kickoff discussion to define your exact use case. I will review your data sources (PDFs, SQL, APIs) and set up the necessary environment credentials (API keys for OpenAI/Anthropic, Vector DB access).
Data Ingestion & RAG Pipeline
I will build the ingestion pipeline to clean and chunk your data. This involves selecting the right embedding model and setting up the Vector Database (Pinecone/Weaviate) to ensure the AI retrieves accurate information.