Browse categories
Explore
Fiverr Pro
English
$
USD
I will build a custom ai chatbot and rag web application
Abdullah A
About this gig
Need an AI assistant that answers strictly from your business data, manuals, or specific curriculum without inventing facts? You need a Retrieval-Augmented Generation (RAG) system.
I build custom, document-grounded AI chatbots and Next.js web applications tailored to your exact data. Instead of guessing, this architecture searches your actual documents, cites exact sources, and explicitly refuses to answer if the information is missing from your corpus.
What I Deliver:
- Strictly Grounded AI: High-accuracy responses generated exclusively from your uploaded files (PDFs, text, CSV).
- Vector Search Database: Fast, semantic data retrieval using Supabase pgvector and top-tier embedding models.
- Confidence Guardrails: Custom backend logic that prevents AI hallucinations by blocking out-of-scope questions.
- Full-Stack Web App: A responsive Next.js frontend with chat history, file ingestion, and secure user authentication.
- Multimodal UI: Voice-to-text integration (Whisper) for seamless conversational interaction.
Let's discuss your project scope! Send me a message before ordering so we can align on the best architecture for your SaaS.
Get to know Abdullah A
Abdullah A
Software Engineer
- FromPakistan
- Member sinceSep 2026
Languages
English
I’m a full-stack developer and startup co-founder specializing in Next.js, React, Python, and Django. While currently completing my university degree, I actively build robust SaaS applications—from AI-powered educational tools (RAG) to complex, multi-tenant management systems. I focus on creating clean, intuitive user interfaces backed by scalable architectures. Whether you need a custom web app, seamless AI integration, or a high-performance backend, I bring real-world startup experience and a meticulous eye for detail to your project. Let's build something exceptional.
My Portfolio
FAQ
What types of documents or data can I use to ground the AI?
You can use PDFs, TXT, CSV, DOCX files, or direct database connections. The system extracts, chunks, and indexes your content into vector embeddings for fast, context-aware retrieval.
How do you prevent the AI from hallucinating or guessing?
A custom confidence guardrail evaluates the vector retrieval score before sending data to the language model. If your documents do not contain relevant information for a user's question, the system blocks the LLM call and informs the user that the topic isn't covered.
What tech stack will my application use?
The core stack includes Next.js (React), TypeScript, and Tailwind CSS for the web application, with Supabase (PostgreSQL & pgvector) handling authentication and vector storage. AI processing relies on models like Google Gemini, OpenAI API, or Anthropic Claude paired with Jina AI or OpenAI embeddings
Will I receive the complete source code?
Yes. Every package includes full source code ownership, clean repository structure, and instructions so you can host, run, and scale the application independently.
Do I need to provide my own API keys and hosting?
Yes. You will need your own accounts/keys for services like OpenAI or Gemini, and database/hosting services like Supabase and Vercel. Setup guidance is provided to help you configure your environment keys smoothly.
