Building a Low-Code Conversational Platform: Learnings and Architecture Considerations

Abstract

Conversational tools are now expected in most enterprise systems, but building them in a consistent and scalable way is still difficult. In many cases, teams end up repeating business logic, managing fragmented NLP models, or struggling with long development cycles. To address these issues, we designed and deployed a Low-Code platform that allowed both developers and nondevelopers to create conversational skills within a single governed framework. A key design choice was to separate natural language processing, orchestration, and business logic, which let the NLP group improve core models while application teams focused only on skill behaviour. The platform grew steadily in use: within three years it handled roughly 300,000 conversations each day; by year five it supported 3,000 tenants; and by year six it was available in nine languages and across six different channels. Development time dropped from months to weeks, while governance, testing, and monitoring were built directly into the framework. This paper shares the architecture, the practical challenges we encountered, and the lessons learned. More broadly, it shows how Low-Code methods can be adapted to enterprise conversational AI, and what this means for future large-scale SaaS and agent-based platforms.