RAG Chatbot Developer for Hire
I build chatbots that answer from your own documents and show where each answer came from, so your team can trust the output instead of double-checking it.
133 completed contracts, 106 five-star reviews and a 100% Job Success Score on Upwork. 71% of that work came from clients who hired me again.
Where these projects go wrong
Most document chatbots fail the same way. They answer confidently from the wrong paragraph, and nobody notices until a customer acts on it. The demo used ten clean PDFs. Production has ten thousand, half of them scans, many of them out of date, several contradicting each other.
The engineering that matters is not the prompt. It is chunking that respects document structure, retrieval you can measure, a citation the reader can click, and an explicit path for the cases where the system should say it does not know.
What I build
Document question answering
Staff ask in plain language and get an answer with the source passage attached, across PDFs, SOPs, contracts, manuals and help sites.
Support chatbots with citations
Customer-facing assistants grounded in your approved content, with a handover to a human when confidence drops below a threshold you set.
Internal knowledge systems
Search and answers over the material scattered across drives, wikis and inboxes, with access rules that match your existing permissions.
Evaluation you can defend
A question set drawn from real staff questions, a measured hit rate before launch, and the same test rerun whenever the content or the model changes.
How the work runs
- Scope the questions, not the documentsWe start from the twenty questions people actually ask. That decides what to ingest, and it becomes the evaluation set.
- Ingest and structureParsing, OCR where documents are scans, structure-aware chunking, embeddings and a vector index that can be rebuilt on a schedule.
- Retrieve, ground, citeRetrieval tuned against the question set, answers constrained to retrieved context, and every answer carrying its source.
- Measure, then deployHit rate and failure cases reported honestly before go-live, then deployment with logging so you can see what people really ask.
Proof
Document extraction backends
Google Vision and Document AI extraction for a document scanning app, and passport and licence OCR behind a React Native backend.
Behavioural AI authentication
An AI system measured honestly rather than marketed, including the numbers that did not flatter it.
Stack
OpenAI, Claude, Gemini, LangChain, LangGraph, LlamaIndex, pgvector, Pinecone, Supabase, Python, FastAPI, Next.js
Questions clients ask
How many documents can it handle?
Volume is rarely the limit. Document quality is. A few thousand clean documents are straightforward; scanned or inconsistent material needs an OCR and cleanup stage first, which I will tell you about before we start rather than after.
Will it make things up?
It can, if it is built to answer at any cost. I constrain answers to retrieved context, attach citations, and set a confidence threshold below which the assistant says it does not know and offers a human. That behaviour is a decision, not an accident.
How do I know it actually works before I roll it out?
We agree an evaluation set of real questions at the start. Before launch you get a measured hit rate against that set and a list of the questions it still gets wrong.
Can it use our existing permissions?
Yes. Retrieval can be filtered per user or per group so people only get answers from material they are already allowed to read.
Tell me what is broken
Send the workflow, the system or the idea. You get a straight answer on whether it is worth building, and what it would take. If I am not the right person, I will say so.