I will build a rag chatbot or ai search for your documents or app


About this gig
You need AI that actually works on real data not a demo that
breaks the moment your documents get messy.
I build RAG systems and LLM integrations for real products. I built
the LLM/RAG pipeline for a live borrower-protection platform that
turns 60-page legal documents into a plain-language risk report. I
built a RAG system from scratch (crawler, embeddings, vector search,
chat interface) that indexes and answers from technical
documentation. I also led the backend for an 11-agent AI system built
in a national hackathon final.
What I deliver:
- Chatbots and AI search grounded in your own documents or data
- LLM integration using OpenAI, Claude, or open-source models
- Full RAG pipelines: embeddings, vector database, retrieval
- The app around it API, database, frontend, deployment if you
need it built, not just the AI layer
I ask a few scoping questions before starting so what I deliver
actually matches what you need. No vague scope, no surprises at
delivery.
Message me before ordering if your project doesn't fit neatly into
one of the packages happy to scope something custom.
Get to know Dhruvin
Fullstack developer and AI engineer
- FromIndia
- Member sinceAug 2026
- Avg. response time1 hour
Languages
English, Hindi
My Portfolio
FAQ
What data or documents do you need from me to get started?
Whatever you want the AI to answer from — PDFs, a website's content, a knowledge base, product docs, or a database. Send me a sample first and I'll confirm it's a good fit before we start.
Which AI models do you work with?
OpenAI (GPT), Anthropic (Claude), and open-source models via Hugging Face, OpenRouter, or AWS Bedrock. I'll recommend the right one for your budget and accuracy needs.
Do I need an existing app, or can you build one from scratch?
Either works. Basic and Standard packages integrate into an app you already have. Premium includes building the full app around the AI feature if you don't have one yet.
How do you handle accuracy , will the chatbot make things up?
I build retrieval (RAG) so answers are grounded in your actual documents, not the model's general knowledge. This significantly reduces made-up answers compared to a plain chatbot.
Can you fine-tune a model instead of using RAG?
Full fine-tuning is outside these packages, but I offer basic prompt/model tuning as an add-on. Message me if your project specifically needs fine-tuning . happy to scope it separately.

