Retrieval-Augmented Generation Chatbots
Context-aware chatbots that combine ChatGPT with a retrieval layer so answers are grounded in the client's own reference material.
AI Chatbot Development in Toronto
A chatbot that makes things up is worse than no chatbot. The ones we build look up the answer in your own documents first, then reply from what they found.
That approach is called retrieval-augmented generation, or RAG. Below are three projects our ML lead has shipped: a document-grounded assistant, a translation bot that lives in Microsoft Teams, and a model that reads service tickets and sends them to the right team.
Chatbot and language projects
Microsoft Teams File Translator Bot
A Teams chatbot that translates PDF and DOCX files in place while preserving the original layout and formatting.
Ticket Classification NLP
NLP models that read incoming service tickets and route them to the right team automatically, replacing manual triage.
These projects were built by Pavlo Holubchykov, Raw Code's Lead ML/AI Developer, some of them before Raw Code was founded. More about Pavlo.
What these bots do differently
Answers that come from your documents
Darnytsia's chatbots pair the ChatGPT API with a retrieval layer over the company's own reference material. A Python backend finds the relevant passages, and the model writes its reply from them. It runs in Docker on Azure App Service.
Retrieval-Augmented Generation Chatbots case studyInside the tools your team already uses
Kormotech's bot sits in Microsoft Teams. Staff send it a PDF or Word file and get it back translated with the layout intact, including text inside images, which is read with OCR. It's built on the Microsoft Bot Framework and Azure Bot Service.
Microsoft Teams File Translator Bot case studyRouting, not just answering
Not every language problem needs a chat window. For Skanska, NLP models read each incoming service ticket and route it through the ServiceNow API, running on Azure Functions. The client reported about 90% fewer classification errors and 50% faster processing.
Ticket Classification NLP case studyProcess and what's included are on our AI Automation Consulting & Implementation page.
FAQ
What is a RAG chatbot?
RAG stands for retrieval-augmented generation. Before answering, the bot searches your documents for the passages that matter, and the language model writes its reply from those passages. That cuts down on invented answers and lets the bot point to where an answer came from.
How much does a custom AI chatbot cost in Toronto?
Chatbots fall under our AI automation work, which runs $3k–$30k project or retainer. A bot over one set of documents sits at the low end; one wired into several internal systems costs more. You get a fixed quote before the build starts.
Can the chatbot run in Microsoft Teams?
Yes. The Kormotech translation bot runs in Teams on the Microsoft Bot Framework and Azure Bot Service, so staff use it without opening another tool.
Will the chatbot ever give a wrong answer?
Sometimes, yes. Retrieval makes wrong answers rarer but doesn't remove them. Anyone promising zero errors is overselling. We test against real questions from your team before launch and show you where it still gets things wrong.
Where does our data go?
That's settled during scoping, before anything is built. The projects on this page ran on Microsoft Azure. We'll tell you which model provider and hosting a bot uses so you can check it against your own privacy obligations.
More of our work in Toronto
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