RAG Chatbot on Company Documents: A Buyer Guide

Who builds RAG chatbots on company documents in Florida?

Cuanto Labs, a founder-led AI agency based in Fort Lauderdale, builds chatbots that answer from a company's own documents using retrieval (RAG) on OpenAI and Anthropic models. Projects start at $5K, fixed price, and typically take 2 to 6 weeks. Eliran Yihye scopes each one, and we meet clients in person across South Florida.

If you want the service page first, see AI chatbots on your documents. The rest of this guide explains what the technology does, what a project involves and how to judge any vendor's work, including ours.

What is a RAG chatbot?

RAG stands for retrieval-augmented generation. When someone asks a question, the system first searches your documents for the relevant passages, then gives those passages to a language model, which writes the answer from them. The answer is grounded in your files instead of the model's general memory.

That matters because a general chatbot does not know your return policy, your onboarding manual or the clause in last year's contract. A RAG chatbot reads them at the moment of the question. When you update a document, the next answer reflects it, with no retraining involved.

The table shows how it differs from the two things people usually compare it with.

How does a RAG chatbot work in practice?

There are four steps, and most of the quality is decided in the first two. Your documents are cut into passages and indexed. A question is matched to the passages most likely to answer it. The model writes an answer using only those passages. The answer links back to the source so a person can check it.

• • Ingest. PDFs, Word files, web pages and exports from a wiki or help center are read and split into passages.
• • Search. The system finds the best passages for each question, usually by meaning and by keyword together.
• • Answer. The model writes a short reply from those passages and nothing else.
• • Cite and hand off. The reply shows its source, and a question the documents do not cover goes to a person.

What does a RAG chatbot cost?

A RAG chatbot is priced as an AI agent project at Cuanto Labs: fixed price from $5K, typically 2 to 6 weeks, with the final number written into a proposal after a scoping call. The published band for an AI agent implementation trained on your data is $5K to $15K.

What moves the number is the state of the documents and the number of places they live. A single clean folder of PDFs is a small build. Documents spread across a shared drive, a wiki, an email archive and a CRM, with different permissions on each, are a larger one. Running costs after launch are the model provider's usage charges, usually modest at a small business's volume.

What documents can a chatbot read?

Most text-based business files work: PDFs, Word documents, spreadsheets, web pages, help-center articles and exported wiki pages. Scanned pages work when the scan is clean enough to read, and poor scans cost more to get right. We confirm the formats during scoping, before the price is fixed, using samples of your real files.

Images, diagrams and handwritten notes are harder. If the answers you need live in drawings or photographs, tell us early, because it changes the design and sometimes the answer to whether a chatbot is the right tool.

How do you stop a chatbot from making things up?

You cannot remove the risk completely, but you can reduce it a lot by design. The chatbot answers only from retrieved passages, shows the source for each answer and says it does not know when the documents do not cover a question. We then test it with real questions from your team before it goes live.

Three habits keep it honest after launch:

• • Keep a list of test questions with known answers and rerun it after every change to the documents.
• • Read a sample of real conversations each month, and fix the documents or rules behind any wrong answer.
• • Route anything sensitive, such as legal, medical or financial advice, to a person.

Is it safe to put company documents in a chatbot?

It can be, when the design limits what the system sees. We write down which folders and systems it may read, who can ask it questions and what it may never answer. We use business-tier API accounts whose terms exclude training on your data, and we log each question. Regulated data gets extra review before we price.

Permissions matter most. A chatbot that reads everything can reveal an HR document to the wrong employee. We design it so each person only gets answers drawn from files they could already open.

Who should use a RAG chatbot, and who should not?

It fits teams that lose time answering the same questions from the same documents: a support team with a long help center, a professional office with policies and precedents, or a property manager with association rules. See how this looks for Coral Gables firms, Doral trade businesses and West Palm Beach offices.

It does not fit when the documents are few, when answers need a fresh judgment each time, or when nobody keeps the documents up to date. A chatbot on stale files gives confident, outdated answers. In those cases we suggest tidying the documents first, or a simpler search page.

Frequently asked questions

Does it work in Spanish?

Yes. Chatbots can answer in English and Spanish, including from documents written in either language. Decide this before the build, since it changes the testing.

Will our documents be used to train a public model?

Not by us. We use business-tier API accounts whose terms exclude training on your data, and we write down what the chatbot may read.

How long does it take?

Typically 2 to 6 weeks, depending on how many document sources and systems it must connect to.

Who owns it afterwards?

You do. Code, repositories and accounts transfer to you at handover.

Can staff see where an answer came from?

Yes. Each answer shows the source document so a person can check it.

Next step

• • AI chatbots on your documents
• • AI chatbot development in Florida
• • AI agency in Coral Gables
• • AI agency in Doral
• • AI agent vs chatbot

Written by Eliran Yihye, founder of Cuanto Labs. Updated 2026-10-16.