Zinn Hub
0
Il tuo carrello
0

AKORA

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

📍 Located in: Uganda 🕐
🎯🤝 Aperto a lavori una tantum o a lungo termine 🌐 Può lavorare nel tuo fuso orario
👤 Freelancer
✔ Verificato
⭐ Livello Zinner 1
🔗 Trova AKORA online
🔍

Esplora i loro link e il resto di questo profilo per conoscere chi sono. Zinn Hub mantiene il profilo di ogni Zinner aperto e trasparente, così puoi costruire fiducia e assumere con sicurezza.

💰 Prezzi
Tariffa oraria usuale: $12.00/ora
Interessato a progetti valutati: $12.00 – $15.00
🔒 Pagamenti tenuti al sicuro da Zinn Hub finché non sei soddisfatto · Carte, PayPal, Cripto e altro · Nessuna commissione per l'acquirente
✓

Competenze verificate da Zinn Hub

Zinner verificato

AKORA ha dimostrato le proprie competenze a Zinn Hub e fornito prove del proprio lavoro. Ogni Zinner verificato supera una revisione pratica di garanzia della qualità — così puoi assumere con fiducia.

🛡️ Verificato dal October 2026

Membro dal
Oct 2026
Zinns elencati
0
Ultima attività
17 hours fa

Chi Siamo

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.

Cosa posso costruire per te:

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.

Come lavoro:

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.

Competenze

Tecnologie e competenze in cui questo freelancer è specializzato.

Categorie

Aree di servizio per le quali questo freelancer è disponibile.

Portfolio

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

Visualizza dettagli →

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

Visualizza dettagli →

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

Visualizza dettagli →

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

Visualizza dettagli →

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

Visualizza dettagli →

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

Visualizza dettagli →

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

Visualizza dettagli →
📤 Condividi questo profilo
𝕏 Condividi LinkedIn Facebook WhatsApp Telegram

Scarica l'app Zinn Hub

Notifiche · Accesso più rapido · Schermo intero

Tocca Condividi nel tuo browser

➜ Quindi tocca "Aggiungi a Schermata Home"