You will get a secure private AI assistant deployed on your Linux server


Project details
I will deploy a secure private AI assistant on your Linux server using a compatible local large language model and a browser-based chat interface.
This service is designed for businesses that want more control over privacy, infrastructure, and AI usage without relying entirely on external cloud platforms.
Depending on the package selected, I can configure the Linux environment, Docker services, model runtime, browser interface, secure access, testing, optimization, documentation, and optional document-based RAG.
You must provide a compatible Linux server or workstation with sufficient CPU, RAM, storage, and GPU capacity for the selected model. Hardware, hosting fees, paid APIs, commercial licences, and major custom application development are not included.
You will receive a tested deployment, clear handover documentation, and practical guidance for operating the system after delivery.
This service is designed for businesses that want more control over privacy, infrastructure, and AI usage without relying entirely on external cloud platforms.
Depending on the package selected, I can configure the Linux environment, Docker services, model runtime, browser interface, secure access, testing, optimization, documentation, and optional document-based RAG.
You must provide a compatible Linux server or workstation with sufficient CPU, RAM, storage, and GPU capacity for the selected model. Hardware, hosting fees, paid APIs, commercial licences, and major custom application development are not included.
You will receive a tested deployment, clear handover documentation, and practical guidance for operating the system after delivery.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, StreamlitAI Models
BERT, LLaMA, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 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.
Fast Delivery
+$100 - $250
Additional Revision
+$75
Additional AI model
(+ 2 Days)
+$150
Domain, SSL, and secure access
(+ 1 Day)
+$120
Additional document collection
(+ 2 Days)
+$150Frequently asked questions
About Amirhesam
AI & Workflow Automation | Python, Playwright, RAG
Kajang, Malaysia - 2:22 am local time
My focus is not making a script click quickly. I map the workflow, validate inputs, test failure cases, verify outputs, and keep sensitive or consequential actions behind human approval.
Depending on the problem, I work with Python, APIs, Playwright, private AI, local language models, retrieval-augmented generation (RAG), Docker, and Linux. The first engagement uses synthetic or explicitly authorized data so we can evaluate feasibility without casually exposing production credentials or confidential information.
A good starter project is one clearly defined workflow with a measurable input, output, and approval boundary. You receive a working prototype, a short private demonstration, a risk and feasibility summary, and practical next steps for production.
Good fit:
• repetitive browser or data-entry workflows
• spreadsheet, document, and reporting automation
• private document search and RAG prototypes
• workflow reliability and failure testing
Not a fit:
• bypassing access controls or anti-bot protections
• handling passwords through chat or recorded video
• medical, legal, or financial decision automation
• guaranteed savings, revenue, accuracy, or compliance claims
Tell me what task is repeated, which tools are involved, and which final decision should remain human-controlled.
Steps for completing your project
After purchasing the project, send requirements so Amirhesam can start the project.
Delivery time starts when Amirhesam receives requirements from you.
Amirhesam works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and infrastructure
I will review the server specifications, access method, security requirements, intended use case, and selected package scope before deployment.
Prepare and secure the environment
I will configure the Linux environment, dependencies, Docker services, storage, permissions, and required network or firewall settings.

