Federated Chatbots Consultancy

Large organizations rarely have one chatbot — they have many, built by different teams for different purposes, with no good way to share knowledge or capability between them. I consulted for a major German IT company wrestling with exactly this problem: how to move from a collection of siloed bots toward a federated architecture where multiple conversational agents could work together.

The Challenge

Federated chatbot systems raise questions that a single-bot deployment never has to answer: how do individual agents share context, how is a user’s request routed to the right one, and how do you evaluate quality across a system made of many independently built parts rather than one? The client’s existing modeling and evaluation pipeline hadn’t been built with that complexity in mind, and needed to evolve alongside the architecture itself.

My Approach

I advised the client on federated chatbot architecture and worked hands-on to improve their existing modeling and evaluation pipeline using Hugging Face and scikit-learn, tightening the feedback loop between model changes and measurable quality. As with most conversational AI systems, the model was only ever as strong as its training data, so I also led the data generation effort — helping the client build the labeled examples needed to train and evaluate individual agents within the larger federated system.

Outcome

The engagement gave the client both a clearer architectural path toward federated chatbots and a stronger, more rigorous evaluation pipeline to measure progress against as they built it out. It’s also directly connected to my own research: the consulting work fed into the doctoral research I completed the following years on modular, federated dialog systems — a case where industry problems and academic research sharpened each other.

Focus areas: Federated/multi-agent chatbot architecture, Hugging Face, scikit-learn, evaluation pipelines, data generation.