You will get Next-Gen AI Product Search for eCommerce (Semantic & Fast)
Project details
🧠Description
I will build a smart semantic search system for your eCommerce or data platform that understands user intent instead of relying on keyword matching.
This means users can search naturally (e.g., “IT setup products”) and still get highly relevant results like laptops, monitors, desks, and office equipment.
The system works by converting your data into embeddings and using a vector database like Pinecone to find the most similar results instantly.
⚙️ What you get:
• Intent-based search (not keyword matching)
• Fast vector search using Pinecone
• Precomputed embeddings for performance
• Accurate and relevant product/results ranking
• Scalable architecture for large datasets
🧠Tech Stack:
Hugging Face · Sentence Transformers · Pinecone · Vector Embeddings · Python / Node.js
đź’ˇ Best for:
• eCommerce product search
• Recommendation systems
• Knowledge base search
• AI-powered discovery engines
👉 Search what your users mean, not what they type.
I will build a smart semantic search system for your eCommerce or data platform that understands user intent instead of relying on keyword matching.
This means users can search naturally (e.g., “IT setup products”) and still get highly relevant results like laptops, monitors, desks, and office equipment.
The system works by converting your data into embeddings and using a vector database like Pinecone to find the most similar results instantly.
⚙️ What you get:
• Intent-based search (not keyword matching)
• Fast vector search using Pinecone
• Precomputed embeddings for performance
• Accurate and relevant product/results ranking
• Scalable architecture for large datasets
🧠Tech Stack:
Hugging Face · Sentence Transformers · Pinecone · Vector Embeddings · Python / Node.js
đź’ˇ Best for:
• eCommerce product search
• Recommendation systems
• Knowledge base search
• AI-powered discovery engines
👉 Search what your users mean, not what they type.
AI Development Type
Knowledge Representation, Recommendation System, Software MaintenanceAI Development Language
PythonWhat's included $150
These options are included with the project scope.
$150
- Delivery Time 5 days
- Number of Revisions 1
- AI Model Integration
- Source Code
About Hani
AI Software Engineer | RAG, LLM, Next.js, Node.js, Python
Surat, India - 4:28 am local time
Over the past two years I've shipped production systems using OpenAI, Hugging Face, Pinecone, ChromaDB, LangChain, Ollama, and Gemini APIs, alongside a strong full-stack foundation in React, Next.js, Node.js, and PostgreSQL.
Recent AI projects include:
→ RAG PDF Retriever (fully local) — a private document Q&A system using ChromaDB, SBERT embeddings, and Ollama. Zero data leaves the client's infrastructure.
→ Semantic Ecommerce Search — Hugging Face embeddings + Pinecone vectors with intelligent reranking, delivering meaning-aware product search far beyond keyword matching.
→ Invoice Image → JSON Extractor — uses Gemini Vision APIs to parse unstructured invoice images into structured JSON, cutting manual data entry entirely.
→ AI Feedback Review System — OpenAI + Firebase functions to auto-categorize 2,000+ feedback submissions, reducing admin time by 70%.
I care about data privacy, production reliability, and building AI that actually integrates cleanly into existing stacks — not just demos. Whether you need a standalone AI feature or a full application, I can take it from architecture to deployment.
Let's talk about what you're building.
Steps for completing your project
After purchasing the project, send requirements so Hani can start the project.
Delivery time starts when Hani receives requirements from you.
Hani works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preparation & Embedding Setup
I will clean your dataset, generate embeddings, and store them in a vector database for fast semantic search.