Customer conversations hold operational knowledge
Calls, emails, and chats contain more than a customer's immediate request. They reveal unclear procedures, missing knowledge, recurring friction, and the moments when an employee needs support. Turning that information into useful coaching means connecting language analysis with the work of agents and team leaders.
At Guidnce, Gilles's principal engineering role spans AI and ML, backend development, cloud architecture, multimodal LLM workflows, and specialised conversational agents. The engagement combines customer-support analysis with research into intelligent tutoring and emotion processing. The focus is on making conversation data useful within a coherent product.
Connect analysis, knowledge, and feedback
The work includes custom retrieval-augmented generation pipelines and agent workflows alongside coaching, feedback, and reporting. These capabilities address related needs: understand an interaction, find relevant knowledge, and turn the result into a useful next step. A conversational interface is only one part of that chain.
A clear engineering distinction is essential. Analysis describes what happened; retrieval supplies organisational context; coaching helps someone practise or improve. Keeping those purposes explicit makes the experience easier to understand and gives the product team a clearer way to investigate weak answers or unhelpful recommendations.
Build for the people using the insight
Coaching requires a different interaction from a generic chatbot. The user may need a concise explanation, a prompt to reflect, or a scenario in which to practise. A team leader needs a useful summary and enough context to guide a discussion. Real-time assistance adds another constraint: feedback must arrive when it can still help.
The engagement therefore extends beyond model selection into the implementation of workflows and supporting services. Backend and cloud engineering provide the foundation for analysis, conversational experiences, and reporting. Research into tutoring and emotional language informs the design questions, while the product context determines which capabilities are worth building.
An AI product with a clear operating purpose
The engineering contribution brings customer-support analysis and specialised agents into the same product as coaching and feedback capabilities. Guidnce connects conversation analysis with employee development and quality monitoring, giving each technical component a clear operating purpose and a place in the wider service workflow.
The broader lesson is that useful enterprise AI connects several layers of work. It needs language understanding, access to relevant knowledge, an application that fits the user's task, and dependable services underneath. When those layers are designed together, customer conversations become a practical starting point for learning and process improvement.
