The Problem

In enterprise LIMS (Laboratory Information Management Systems), every customer wanted custom dashboards. Different labs needed different metrics, different visualizations, different reports. The traditional approach was to build each dashboard manually—expensive, slow, and impossible to scale.

The root problem: users knew what questions they wanted to ask their data, but they couldn't ask those questions directly. They needed a developer to translate their request into SQL queries and dashboard components. This created a bottleneck that made product teams reactive instead of proactive.

Key Decisions

• Natural language as the interface: Users type questions in plain English instead of learning SQL or BI tool syntax.
• AI generates visualizations on demand: Instead of pre-configured charts, the system analyzes the query and data to determine the best visualization type.
• Built for customer portal use case: Designed specifically to be embedded in enterprise LIMS customer portals, where non-technical users need to explore their lab data.
• Python + Streamlit for rapid prototyping: Chose Streamlit to ship a working POC quickly and demonstrate the concept without heavy infrastructure.
• Google Gemini API for language processing: Leveraged Gemini's ability to understand context and translate natural language into data operations.

What I Learned

AI can solve customization problems that traditional development approaches can't. Instead of building 50 different dashboards for 50 different customer needs, I built one system that adapts to each user's questions. This changes the unit economics of enterprise software—customization no longer scales linearly with development effort.

Prompt design matters more than model choice. The quality of results depended less on which AI model I used and more on how I structured the prompts: understanding the data schema, providing context about what visualizations make sense for different query types, and handling edge cases where the AI might misinterpret user intent.

POCs should answer specific product questions. I didn't build LexiQuery to show I could code. I built it to validate whether natural language interfaces could replace custom dashboard development. The answer: yes, with caveats. Users need guardrails to understand what questions are answerable. The AI needs schema context to generate accurate queries. But the core hypothesis held—non-technical users can explore complex data without developer intervention.