The Problem with AI Chatbots
Most AI chatbots stop at text responses. You ask a question, get an answer, and that’s it. But teams don’t just need answers—they need AI that can act on data, visualize insights, and integrate into existing workflows. A chatbot that only returns text is a demo, not a tool.
The Solution: AI That Acts
I co-hosted a Xano webinar called “Go Beyond Chat: Visualize and Act on Your Data with Xano and Thesys”, demonstrating how to build AI interfaces that go beyond conversation to become true operational tools.
Working with our partner Thesys, we showed how AI can:
- Query Xano backends via Xano MCP for real-time data
- Generate dynamic visualizations based on user questions
- Trigger backend operations (updates, workflows, integrations)
- Surface complex data in accessible, AI-driven UIs
Use Case: Real Estate Intelligence
We built a live demo using real estate data, where users could ask natural language questions like “Show me properties with the highest ROI in the last quarter” and receive:
- Intelligent query parsing that maps natural language to backend filters
- Visual responses with charts, maps, and data tables
- Actionable operations like flagging properties or triggering workflows
Instead of a static dashboard or a pure chatbot, the result was a generative UI that adapts to user intent in real-time.
Technical Architecture
- Backend: Xano APIs with robust data models and authentication
- AI Layer: Query parsing and intent recognition
- Frontend: Dynamic UI generation based on query results
- Integration: Thesys AI platform connected to Xano via API
The key insight: surface your data’s complexity through AI-driven interfaces, but keep your backend strong and auditable.
DevRel Role
As co-host, I:
- Designed the technical demo architecture
- Presented live walkthroughs of the integration patterns
- Created educational content around generative UI concepts
- Answered community questions about implementation
This session positioned me as someone who can bridge chatbot demos and AI-driven internal tools—showing teams how to move from proof-of-concept to production.
Business Value
Generative UI solves a critical problem: making powerful data accessible without building custom dashboards for every use case. Instead of maintaining dozens of static views, teams can let AI generate the exact interface users need, exactly when they need it.
This mirrors a broader AI implementation challenge I often address in consulting: how to make AI UIs accessible without sacrificing backend robustness.
Learnings
The future of AI UX isn’t pure chat, and it isn’t traditional dashboards. It’s adaptive interfaces that understand user intent and generate the right visualization, action, or workflow on demand.
Building these systems requires strong backend design, clear data models, and thoughtful AI integration—exactly the combination of skills I bring from my management consulting roots, human-centered UX training, and hands-on backend development.