You will get an AI Assistant Delivering Knowledgeable and Personalized Answers


Project details
I will build a versatile AI assistant that provides smart, customized, and context-aware answers by leveraging your own documents and data, with responses fine-tuned via LoRA for a clear and user-friendly style, powered by FAISS for deeper understanding, featuring a full-stack solution with an intuitive UI, Dockerized for easy deployment, and ready for cloud deployment, making it useful across healthcare, education, finance, customer support, legal, R&D, business, and creative fields, powered by RAG and deployable via API or locally.
Purpose
BusinessIndustry
Business Services & Consulting, Education, Financial Services, Legal, Marketing & Advertising, Medical & Pharmaceutical, Real Estate, Software, Sports & Fitness, Travel & TourismLanguage
EnglishWhat's included
| Service Tiers |
Starter
$99
|
Standard
$199
|
Advanced
$399
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Hours of Work | 12 | 20 | 30 |
Scriptwriting | |||
Summary Report | |||
Social Media Replies | - | ||
Email Support | - | ||
Live Chat Support |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $150About Alireza
Computer Vision & AI Specialist | IBM Certified Deep Learning Pro
Athens, Greece - 11:19 pm local time
My primary areas of interest in Computer Vision include:
-Object detection and tracking (YOLO, custom models)
-Face recognition and metric learning (FaceNet, embeddings + SVM)
-Pose estimation and human/hand tracking
-Semantic segmentation and image denoising
-Classification and real-time video analysis
-Camera calibration and preprocessing pipelines
I have experience with the following technologies:
1)Computer Vision & Deep Learning
-Python, TensorFlow, Keras, PyTorch
-OpenCV, Torchvision
-Ultralytics YOLO (detection and segmentation)
-Pytorch Metric Learning (face/feature embeddings)
-Segmentation Models Pytorch
-Scikit-learn, Albumentations
2)Infrastructure & Deployment:
-Docker and containerized AI pipelines
-Git / GitHub version control
-TensorFlow.js
-Azure and RunPod for cloud-based training and deployment
3)Supporting Tools & Data Processing:
-NumPy, Pandas, Matplotlib
-CVAT and Roboflow for dataset annotation and management
-PyTest and MyPy for testing and type-checking
I excel at end-to-end AI workflows, from dataset preparation and model training to deployment on cloud or embedded systems. My projects range from real-time face recognition and hand tracking to denoising and transformer-based prediction models.
Steps for completing your project
After purchasing the project, send requirements so Alireza can start the project.
Delivery time starts when Alireza receives requirements from you.
Alireza works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1 — Gather Requirements
Medical dataset / knowledge base Example queries / FAQ Preferred deployment method (Docker, cloud, local) Optional: UI branding, user flow
Step 2 — Data Preparation
Clean and preprocess the dataset: Text extraction (PDF → plain text) Normalize / remove irrelevant info Split into documents or passages for retrieval Optional for Tier 2: Prepare dataset for fine-tuning
