You will get Hybrid AI-Powered Product Search & Recommendation System
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Project details
The Problem:
The goal is to build a hybrid AI recommendation system that can intelligently combine traditional product database searches, semantic similarity retrieval, and AI-driven conversational suggestions into a single seamless experience. The challenge lies in integrating structured filters (like price, category, availability) with unstructured, intent-based understanding of user queries, while ensuring fast, accurate, and relevant results.
Approach / Solution:
Step 1: Data Foundation (Products + Metadata)
Step 2: Hybrid Search Layer (Keyword + Semantic)
Step 3: Embeddings + Vector Database
Step 4: Recommendation Engine (Multi-Strategy)
Step 5: AI Chatbot Layer (Conversational UX)
Step 6: Orchestration Layer
Step 7: Feedback Loop + Learning
Tech Stack
Python (FastAPI) or Node.js (NestJS)
Elasticsearch / OpenSearch
Vector DB: Pinecone / Weaviate / FAISS
OpenAI / Claude / Llama
LangChain / LlamaIndex (orchestration)
PostgreSQL / MongoDB
React.js / Next.js
AWS / GCP / Azure
Docker + Kubernetes
Deliverables:
End-to-end architecture diagram
Hybrid Search Engine
Recommendation Engine
AI Chatbot
Admin / Analytics Dashboard
Production-ready environment
The goal is to build a hybrid AI recommendation system that can intelligently combine traditional product database searches, semantic similarity retrieval, and AI-driven conversational suggestions into a single seamless experience. The challenge lies in integrating structured filters (like price, category, availability) with unstructured, intent-based understanding of user queries, while ensuring fast, accurate, and relevant results.
Approach / Solution:
Step 1: Data Foundation (Products + Metadata)
Step 2: Hybrid Search Layer (Keyword + Semantic)
Step 3: Embeddings + Vector Database
Step 4: Recommendation Engine (Multi-Strategy)
Step 5: AI Chatbot Layer (Conversational UX)
Step 6: Orchestration Layer
Step 7: Feedback Loop + Learning
Tech Stack
Python (FastAPI) or Node.js (NestJS)
Elasticsearch / OpenSearch
Vector DB: Pinecone / Weaviate / FAISS
OpenAI / Claude / Llama
LangChain / LlamaIndex (orchestration)
PostgreSQL / MongoDB
React.js / Next.js
AWS / GCP / Azure
Docker + Kubernetes
Deliverables:
End-to-end architecture diagram
Hybrid Search Engine
Recommendation Engine
AI Chatbot
Admin / Analytics Dashboard
Production-ready environment
Database Type
MySQL, MS SQL, MS Access, Oracle, SQLite, PostgreSQL, MongoDB, Couchbase, Teradata, Realm Database, Azure Cosmos DB, LevelDBWhat's included
| Service Tiers |
Starter
$20
|
Standard
$150
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 1 day | 5 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
Number of Tables Added | 2 | 10 | 20 |
Schema Diagram | |||
Permissions Setup | |||
Import/Export Data | |||
Admin Panel Setup |
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About Sunil
AI Engineer | Python | Machine Learning | Automation | Dashboard | AWS
91%
Job Success
Delhi, India - 6:56 pm local time
🏆 Top Rated 👨💻 𝘈𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦 immediately ✅ 100% Job Success 🚀 On-time Delivery
I design and develop intelligent AI-powered applications that automate business processes, improve decision-making, and enhance customer experiences. With expertise in Python, Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, SaaS platforms, and cloud technologies, I build scalable solutions from concept to production.
My experience includes developing AI chatbots, multi-agent systems, document intelligence platforms, recommendation engines, predictive analytics solutions, workflow automation tools, and enterprise SaaS applications. I specialize in integrating OpenAI, Claude, Gemini, and open-source LLMs with modern backend architectures using FastAPI and cloud infrastructure on AWS.
From data collection and model training to deployment, monitoring, and optimization, I deliver end-to-end AI solutions that help businesses reduce operational costs, increase efficiency, and unlock valuable insights from their data.
🎯 Core Skills
✔ Artificial Intelligence (AI)
✔ Machine Learning (ML)
✔ Generative AI
✔ Large Language Models (LLMs)
✔ Retrieval-Augmented Generation (RAG)
✔ AI Agent Development
✔ Multi-Agent Systems
✔ Prompt Engineering
✔ Natural Language Processing (NLP)
✔ Deep Learning
✔ Predictive Analytics
✔ Data Science
✔ SaaS Development
✔ Backend Development
✔ API Development
✔ Workflow Automation
✔ Cloud Computing
✔ MLOps
✔ Data Engineering
✔ Dashboard & Reporting
⚙️ Tech Stack
➤ Programming Languages
Python, React, Node.js, Django, Flask, FastAPI, Next.js, jQuery, SQL, JavaScript, TypeScript, HTML, CSS
➤ AI & Machine Learning
OpenAI GPT, Claude, Gemini, Llama, LangChain, LangGraph, CrewAI, AutoGen, Hugging Face, TensorFlow, PyTorch, Scikit-Learn, XGBoost
➤ RAG & Vector Databases
Pinecone, Weaviate, ChromaDB, FAISS, Qdrant, Elasticsearch
➤ Backend Development
FastAPI, Flask, Django, REST APIs, GraphQL, WebSockets
➤ Databases Development
PostgreSQL, MySQL, MongoDB, Redis, Airtable, DynamoDB, Neo4j, Elasticsearch, OpenSearch
➤ Cloud & DevOps
AWS, Docker, Kubernetes, Terraform, GitHub Actions, CI/CD Pipelines, Linux
➤ Data Visualization
Power BI, Tableau, Looker, Kibana, Grafana, Elasticsearch, Streamlit, Plotly, Dash, R
➤ Automation & Integration
OpenClaw, Zapier, Make, n8n, Apache Airflow, Pipedream, Celery
➤ CRM & Sales Automation
HubSpot, Salesforce, GoHighLevel (GHL), Pipedrive, Zoho CRM, Streak CRM
➤ Communication Automation
Twilio, SendGrid, Gmail API, Outlook API, Slack, Microsoft Teams, WhatsApp Business API
🏢 𝗜𝗡𝗗𝗨𝗦𝗧𝗥𝗜𝗘𝗦
Healthcare, Finance & FinTech, E-commerce, Real Estate, Education, Legal Tech, Marketing & Sales, Logistics & Supply Chain, Human Resources, Customer Support, Manufacturing, SaaS Startups & Enterprises
Keywords: Data Scientist, AI Developer, Machine Learning Engineer, Business Intelligence Developer, Power BI Expert, Dashboard Developer, Python Developer, Data Analyst, AI Agent Developer, Generative AI, ChatGPT, OpenAI, RAG, ETL, SQL, Data Engineering, SaaS Development, Analytics Consultant, KPI Dashboard, Data Visualization, Backend Engineer, Full Stack Developer, API Integration, AI Engineer, MCP Server, LLM Apps, RAG Pipeline, Vector Database, Graph Database, LangChain, LangGraph, GraphRAG, LangSmith, Neo4j, Elasticsearch, Kibana, Grafana, OpenClaw, OpenSearch, AI Bot, Agentic AI, Workflow Automation, AI Automation.
I offer 𝟯𝟬 𝗺𝗶𝗻 𝗽𝗮𝗶𝗱 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝘁𝗶𝗼𝗻𝘀 for AI architecture review, build vs. buy decisions, and MVP scoping.
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💬 LET’S BUILD
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👉 Send me a message — I’ll help you design and build it the right way.
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After purchasing the project, send requirements so Sunil can start the project.
Delivery time starts when Sunil receives requirements from you.
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Revisions may occur after the delivery date.
Hybrid AI-Powered Product Search & Recommendation System
The goal is to build a hybrid AI recommendation system that can intelligently combine traditional product database searches, semantic similarity retrieval, and AI-driven conversational suggestions into a single seamless experience.