Building Better BI Chatbots: Why Context and Triggers Matter

Over the past year, there has been a focused exploration of integrating AI chatbots into business intelligence (BI) and SaaS tools to enhance data understanding rather than merely adding AI capabilities indiscriminately. Preset has introduced AI Assist in SQL Lab to aid users in writing SQL queries, but realized that the real need is helping users understand their data contextually. Unlike traditional AI chat implementations that present users with blank input boxes, Preset's approach involves integrating AI seamlessly into the workflow, allowing users to engage with data directly from within their existing interfaces such as charts and dashboards. This makes the AI a conversational partner that provides context-aware assistance, reducing the inefficiency of having to describe data context manually. The system is designed to cater to both technical users, like data scientists who need detailed insights, and business users who seek straightforward answers. While the initial version is basic, with simple contextual triggers, the intention is to build a foundation of contextual awareness and rich object representations, enhancing the AI's ability to offer deeper insights over time. The development is guided by user feedback and built on the Model Context Protocol (MCP), which standardizes AI applications' connections to data sources, allowing for smarter integration across the ecosystem.

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