You will get I will deploy a custom AI agent or LLM-powered API on AWS or Azure

Project details
I build production-ready AI agents and LLM-powered APIs deployed on Azure or AWS — fully containerised, secured, and monitored. Whether you need a customer-service bot, a document-processing pipeline, a RAG system over your knowledge base, or a multi-step automation agent, I design the architecture and ship working code. Integrations with OpenAI, Anthropic Claude, open-source models via Ollama, and any REST API. Every tier includes a live endpoint your team can call on day one.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorchAI Models
ChatGPT, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$249
|
Standard
$499
|
Advanced
$899
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 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 |
About Satyam
Senior Data Engineer
Aarhus C, Denmark - 6:27 am local time
Steps for completing your project
After purchasing the project, send requirements so Satyam can start the project.
Delivery time starts when Satyam receives requirements from you.
Satyam works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Architecture
Clarify use case, data sources, and API needs. Design agent architecture and select LLM provider and cloud platform.
Build & Integrate
Build the agent or API, connect LLM and tools, containerise with Docker, and deploy to Azure Container Apps or AWS Lambda/ECS.