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AKORA

I build RAG-powered chatbots, web dashboards, and AI search systems for businesses | Python, OpenAI, Pinecone

📍 Located in: უგანდა 🕐
🎯🤝 ღიაა ერთჯერადი ან გრძელვადიანი სამუშაოსთვის 🌐 შეუძლია იმუშაოს თქვენს დროის სარტყელში
👤 ფრილანსერი
✔ ვერიფიცირებული
⭐ Zinner დონე 1
🔗 იპოვეთ AKORA ონლაინ
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შეისწავლეთ მათი ბმულები და ამ პროფილის დანარჩენი ნაწილი, რათა გაიგოთ, ვინ არიან ისინი. Zinn Hub ინახავს ყველა Zinner-ის პროფილს ღიად და გამჭვირვალედ — ასე რომ თქვენ შეგიძლიათ ნდობის მოპოვება და თავდაჯერებულად დაქირავება.

💰 ფასები
ჩვეულებრივი საათობრივი განაკვეთი: $12.00/სთ
დაინტერესებული ვარ პროექტებით, რომელთა ღირებულებაა: $12.00 – $15.00
🔒 გადახდები უსაფრთხოდ ინახება Zinn Hub-ის მიერ, სანამ არ დაკმაყოფილდებით · ბარათები, PayPal, კრიპტო და სხვა · მყიდველის საკომისიო არ არის
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დადასტურებული Zinner

AKORA დაამტკიცა თავისი უნარები Zinn Hub-ს და წარმოადგინა თავისი ნამუშევრების მტკიცებულება. ყოველი დამოწმებული Zinner გადის პრაქტიკულ ხარისხის უზრუნველყოფის შემოწმებას — ასე რომ თქვენ შეგიძლიათ დაიქირაოთ თავდაჯერებულად.

🛡️ დამოწმებულია October 2026-დან

წევრია
Oct 2026
ჩამოთვლილი Zinns
0
ბოლოს აქტიური
17 hours წინ

შესახებ

I am an AI Engineer who builds intelligent systems that help businesses unlock the value hidden inside their own data. My specialty is Retrieval-Augmented Generation (RAG) — the technology that transforms static documents, PDFs, and knowledge bases into interactive, AI-powered assistants that answer questions in plain English.

I discovered my passion for AI engineering through an intensive, hands-on scholarship program where I completed a comprehensive track covering the entire modern AI stack. Through this journey, I built multiple working systems from scratch — not just tutorials, but real, functional applications. I learned how to architect RAG pipelines, manage vector databases, engineer effective prompts, and deploy AI tools that solve actual problems.

რისი აშენება შემიძლია თქვენთვის:

RAG-Powered Chatbots: AI assistants that read your documents and answer customer or employee questions accurately, with citations to the original source.

Semantic Search Engines: Search tools that find information by meaning, not just keywords — so your team finds what they need even when they don't know the exact words.

Document Q&A Systems: Turn your PDFs, manuals, and knowledge bases into an instant-answer system that saves hours of manual searching.

Interactive AI Apps: User-friendly Streamlit applications that make your AI tools accessible to anyone, no technical skills required.

Data Processing Pipelines: Automated scripts that clean, batch, and prepare your data for AI systems using Pandas and NumPy.

My Technical Toolkit:

Python, Pandas, NumPy, OpenAI API (Embeddings, Chat Completions, Function Calling, Responses API), Pinecone, ChromaDB, LangChain, Streamlit, t-SNE, Matplotlib, Git, and REST API integration.

როგორ ვმუშაობ:

I believe in clean, working solutions — not overcomplicated theory. I communicate clearly, deliver on time, and focus on understanding your actual problem before writing a single line of code. Whether you need a small prototype to test an idea or a complete system to automate a workflow, I will work with you to build something that genuinely helps.

I am early in my freelance journey, which means you get dedicated attention, competitive rates, and a genuine commitment to your project's success. I am not here to collect a paycheck — I am here to build a reputation, one happy client at a time.

If you have a document-heavy workflow, a customer support challenge, or an idea for an AI tool, let's talk. Send me a message and tell me what you're trying to solve. I will tell you honestly whether I can help — and if I can, I will give it everything I have.

უნარები

ტექნოლოგიები და ექსპერტიზა, რომლებშიც ეს ფრილანსერი სპეციალიზდება.

კატეგორიები

მომსახურების სფეროები, რომლებშიც ეს ფრილანსერი ხელმისაწვდომია.

პორტფოლიო

Semantic Search Engine for Clothing Reviews

Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib

დეტალების ნახვა →

Semantic Search Engine for Clothing Reviews

Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib

დეტალების ნახვა →

Semantic Search Engine for Clothing Reviews

Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib

დეტალების ნახვა →

Semantic Search Engine for Clothing Reviews

Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib

დეტალების ნახვა →

Semantic Search Engine for Clothing Reviews

Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib

დეტალების ნახვა →

Semantic Search Engine for Clothing Reviews

Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib

დეტალების ნახვა →

Semantic Search Engine for Clothing Reviews

Built a semantic search engine that finds similar customer reviews based on meaning, not just keywords. What it does: Processes thousands of e-commerce clothing reviews from a CSV dataset Generates embeddings using OpenAI's API and stores them in ChromaDB Applies t-SNE dimensionality reduction to visualize review clusters in 2D Implements a search function that returns the 3 most semantically similar reviews to any query Demonstrates real-world applications like automated feedback categorization (Quality, Fit, Style, Comfort) Tech Stack: Python, ChromaDB, OpenAI Embeddings, Pandas, NumPy, scikit-learn (t-SNE), Matplotlib

დეტალების ნახვა →
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