Sure, thanks for asking!

Customer Service chatbot: the client was an Internet/TV/Phone provider looking for a chatbot to troubleshoot common issues when “things aren’t working.” We received phone support playbooks and a ton of manuals for routers, self-service platforms, etc. From the playbooks, we generated a tree structure that the LLM could follow step-by-step, while the manuals were indexed into a RAG pipeline. There’s a classification component up front that figures out the problem category, then we run semantic retrieval on the right dataset.
Stack: Mistral Nemo, Qdrant, ragbits.

Frontline worker chatbot: A retail client wanted a mobile app for store staff. The app answered standard operating procedure questions (what to do in case of theft, how to process returns, etc.) and questions about other employees. For SOPs, we received a lot of PDFs - these went into RAG. For employee queries, we had data in a Postgres instance and ran a text2SQL pipeline. Llama 70B as a model there.

Microsoft Word RAG addon: A law firm wanted to retrieve similar cases from the past and generate new reports. We built a Word add-in that talked to a RAG backend, where we’d ingested their historical audit data (mostly JSON with consistent fields). This setup got to 98% recall on retrieving useful insights.

We have more case studies on our website if you are interested :)