You will get a personalized recommendation system for your app
Project details
You will receive a machine learning powered recommendation system that suggests relevant items based on user behavior and interaction data.
Recommendation systems are widely used in e-commerce platforms, streaming services, and content platforms to help users discover products, movies, articles, or other items they are likely to enjoy.
I will develop a recommendation model that analyzes patterns in your dataset and generates personalized suggestions. The system can work with different types of data such as user activity, ratings, product catalogs, or content interaction history.
The final solution includes a trained recommendation model, structured outputs, and documentation so the system can be easily integrated into your application or platform.
My focus is on building practical and scalable machine learning solutions that help improve user engagement and content discovery.
Recommendation systems are widely used in e-commerce platforms, streaming services, and content platforms to help users discover products, movies, articles, or other items they are likely to enjoy.
I will develop a recommendation model that analyzes patterns in your dataset and generates personalized suggestions. The system can work with different types of data such as user activity, ratings, product catalogs, or content interaction history.
The final solution includes a trained recommendation model, structured outputs, and documentation so the system can be easily integrated into your application or platform.
My focus is on building practical and scalable machine learning solutions that help improve user engagement and content discovery.
Machine Learning Tools
NumPy, pandas, Python, PyTorchWhat's included
| Service Tiers |
Starter
$100
|
Standard
$200
|
Advanced
$400
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional dataset integration
(+ 1 Day)
+$40Frequently asked questions
About Prashant
AI Engineer | RAG Chatbots, LLM Apps, Computer Vision | Python
Delhi, India - 12:22 am local time
I design and deploy AI systems such as RAG chatbots, document intelligence tools, recommendation engines, and computer vision pipelines that automate information retrieval and decision-making.
Companies often struggle to extract useful insights from large amounts of information such as PDFs, internal documentation, or customer data. I help solve this by building AI systems that can understand, retrieve, and generate accurate responses using modern language models.
Background & Experience
• ~2 years of hands-on experience building AI and machine learning systems for real-world applications
• Developed RAG-based AI assistants capable of answering questions from documents and knowledge sources
• Built generative AI pipelines using Stable Diffusion for prompt-based image generation and visual transformations
• Implemented computer vision systems using YOLOv8 for object detection and scene understanding
• Designed production-style ML systems including recommendation engines, AI APIs, and modular ML pipelines
• Experienced in building scalable AI backends using Python, FastAPI, and modern ML frameworks
What I can help you build
• AI chatbots trained on company documents, PDFs, and knowledge bases
• RAG systems that allow users to ask natural questions and retrieve accurate answers from internal data
• AI assistants integrated into SaaS products, internal tools, or customer support platforms
• Intelligent search systems powered by embeddings and vector databases
• Scalable backend APIs for AI applications using Python and FastAPI
Technologies I commonly work with
Python, FastAPI, LangChain, vector databases, embeddings, and modern large language models.
I focus on building AI systems that are reliable, scalable, and easy to integrate into existing products.
If you're planning to build an AI chatbot, document assistant, or any LLM-powered feature, feel free to reach out. I’d be happy to discuss your project and help design a practical AI solution that fits your product and goals.
Steps for completing your project
After purchasing the project, send requirements so Prashant can start the project.
Delivery time starts when Prashant receives requirements from you.
Prashant works on your project following the steps below.
Revisions may occur after the delivery date.
Review project requirements
I analyze the dataset and project requirements to understand the recommendation objective and prepare the data.
Data preparation and processing
The dataset is cleaned and structured so it can be used effectively for machine learning model development.