AI Chatbot Development: Custom RAG Chatbots for Your Business
I build custom AI chatbots that answer from your own content. Using retrieval-augmented generation (RAG) with OpenAI or Claude and pgvector, the chatbot searches your documents, courses or help articles, answers with that context, and only uses data the current user is allowed to see.
Who needs a custom AI chatbot
Businesses with a lot of their own knowledge, such as course material, help docs, policies or product data, who want customers or staff to get accurate answers instantly. A generic chatbot does not know your content. A RAG chatbot does.
I built course Q&A with RAG inside LeadsMind, so learners get answers grounded in the course material they are enrolled in.
What you get
Content ingestion
Your documents, pages or database content split into chunks, embedded and stored in Supabase Postgres with pgvector, with a job to keep it in sync.
Retrieval that respects permissions
Similarity search filtered by Row Level Security, so each user only retrieves content they have access to.
Answers with sources
Responses grounded in the retrieved passages, with links to the source so users can verify and read more.
Chat interface
A streaming chat UI embedded in your app or site, built with React and matching your product's design.
Guardrails
Clear fallbacks when the answer is not in your content, limits on usage per plan, and logging of questions you cannot yet answer.
Evaluation set
A list of real questions with expected answers, used to check quality before launch and after every change.
The stack I use
- OpenAI APIembeddings and chat completions
- Claude APIlong-context answers when needed
- pgvectorvector similarity search in Postgres
- Supabasestorage, auth and RLS for the knowledge base
- Next.jsstreaming responses from the server
- Edge Functionsingestion and re-embedding jobs
My process, step by step
Discovery call
We talk goals, users and constraints. You leave with a clear scope and honest timeline.
Plan and architecture
Data model, security rules and milestones agreed before a line of code is written.
Build in milestones
Working software at every milestone, with written progress reports so you always know where things stand.
Launch and support
Deployment, handover and ongoing improvements after you go live.
Engagement and timeline
- Fixed-price milestones through Upwork, the way I delivered LeadsMind: each milestone ends with working software you can test, plus a written progress report.
- [PLACEHOLDER: other engagement models you offer, e.g. hourly contracts or a monthly retainer]
- [PLACEHOLDER: your typical starting budget or day rate]
- [PLACEHOLDER: typical chatbot timeline, e.g. X to Y weeks]
I work remotely from Bahawalpur, Pakistan, and serve clients in Africa, the US and the MENA region. My working hours overlap with all three, and every project runs on written updates, so time zones never block progress.
Proof from shipped work
LeadsMind
Built course Q&A with RAG as part of an AI module suite.
Read the LeadsMind case study →Velocity
Co-founded an AI assistant that replies to missed calls in seconds.
Read the Velocity case study →World Teachers Academy
Built the data pipelines that keep job content fresh nightly.
Read the World Teachers Academy case study →On Upwork I am Top Rated with a 100% Job Success Score, which comes from saying what I will deliver and then delivering it, milestone by milestone.
Frequently asked questions
What is a RAG chatbot?
A chatbot that retrieves relevant passages from your own content before answering, then asks the language model to answer using those passages. It keeps answers grounded in your material, reduces made-up answers, and lets the chatbot show where each answer came from.
Which AI models do you use for chatbots?
OpenAI and Claude models, chosen per project for answer quality, speed and cost. Embeddings and chat can come from different providers, and I keep the integration swappable so you are not locked into one vendor.
Can the chatbot answer from private or per-customer data?
Yes. Content is stored in Supabase with Row Level Security, so retrieval only returns passages the signed-in user is allowed to read. This is the same approach I use for multi-tenant SaaS data.
What happens when the answer is not in our content?
The chatbot says it does not know and suggests a next step, such as contacting support, instead of inventing an answer. Unanswered questions are logged so you can see which content to add.
How long does it take to build a custom AI chatbot?
It depends mostly on how clean and accessible your content is, and where the chatbot needs to live. [PLACEHOLDER: your typical chatbot timeline]. You get a fixed scope after the discovery call.
Do you work with businesses outside the US?
Yes. I work remotely from Pakistan with clients in Africa, the US and the MENA region.
Related services and guides
Hire an AI Developer to Build and Ship Your AI Product
OpenAI and Claude integrations, RAG over your data, AI features inside production SaaS.
See the service →Hire a Supabase Developer for Secure SaaS Backends and RLS Audits
Schema design, Row Level Security policies and security audits for Supabase projects.
See the service →MVP Development for Startups
A production-ready first version with auth, multi-tenant data, billing and a real dashboard.
See the service →How to Build a RAG Chatbot for Your Business with OpenAI and pgvector
Build a RAG chatbot that answers from your own content: chunking, OpenAI embeddings, pgvector search in Supabase, permission-aware retrieval and evaluation.
Read the guide →How to Hire an AI Developer: What to Look For and What to Avoid
How to hire an AI developer who ships: the skills that matter beyond prompts, questions to ask, red flags to avoid, and how to start with a small milestone.
Read the guide →