You will get a RAG-powered AI chatbot for your product catalog
Rising Talent
Rising Talent
Project details
You will get a production-ready AI chatbot that lets your customers find products by asking natural language questions — no keywords, no filters, just answers. I build RAG systems specifically for e-commerce and SaaS products, with clean code, live deployment, and no scope surprises. Every project starts with a written spec and ends with a tested, documented delivery.
Programming Languages
PythonCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$249
|
Standard
$599
|
Advanced
$1,199
|
|---|---|---|---|
| Delivery Time | 7 days | 12 days | 20 days |
Number of Revisions | 1 | 2 | 3 |
Design Customization | - | ||
Content Upload | |||
Responsive Design | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$49Frequently asked questions
About Gill Jordan
LLM & RAG Developer for E-commerce and SaaS Products
Antananarivo, Madagascar - 2:57 am local time
If your team is sitting on product data, customer reviews, or documentation that isn't queryable yet, I turn that into working AI features your users can actually interact with.
Here's what I build:
— Catalog chatbots that answer customer questions in plain English (RAG + vector search)
— Product content generators that turn raw supplier data into SEO-ready listings at scale
— Review analysis APIs that classify sentiment, flag urgent issues, and deliver structured JSON
Every project starts with a written spec and clear milestones. You know exactly what's being built, when it ships, and what it costs — before a single line of code is written.
If you're looking for a reliable LLM developer who scopes precisely and delivers without back-and-forth, send me your brief and I'll have a proposal back within 24 hours.
Steps for completing your project
After purchasing the project, send requirements so Gill Jordan can start the project.
Delivery time starts when Gill Jordan receives requirements from you.
Gill Jordan works on your project following the steps below.
Revisions may occur after the delivery date.
Catalog ingestion & vector indexing
I will process your product catalog, chunk each product entry, and index it into a vector database so your chatbot can retrieve the most relevant products instantly.
RAG pipeline setup
I will build the retrieval-augmented generation pipeline connecting your indexed catalog to the LLM, ensuring answers are always grounded in your actual product data.