Conversational Analytics Prototype - Beyond Dashboards

I Built a Prototype to Show What Comes After Dashboards

From Theory to Reality: A Working Conversational Analytics Experience

Try it yourself:
Live Prototype: Conversational Analytics Demo

In my previous articles, I wrote about why dashboards fail and what conversational analytics could look like in practice.

But I wanted to go beyond theory.

So I built a working prototype that demonstrates natural language data exploration. No filters. No training. Just ask your question and get an answer.

The Problem I’m Solving

Traditional Dashboard

Traditional Dashboards

  • Need training to use effectively
  • Complex filter navigation
  • Static, pre-defined views
  • High friction to get answers
Conversational Analytics

Conversational Analytics

  • No training needed
  • Ask questions in plain language
  • Dynamic, contextual responses
  • Immediate insights

Most people don’t want to become dashboard experts. They want answers.

What the Prototype Does

Core Features

Natural Language
Natural Language Queries
Ask questions in plain English. No SQL knowledge required.
Interactive Results
Automatic Visualizations
Charts are generated based on the data and your question.
Follow-up Questions
Iterative Exploration
Each answer leads naturally to the next question.
Open Source
Open Source
Full code available on GitHub.

Example Interactions You Can Try

Instead of navigating through dropdown menus and date pickers, just type:

"Show me top selling products last quarter"
"Which regions had declining sales in 2024?"
"Compare revenue between Q1 and Q2"
"What's the trend for Product Category X?"
"Show anomalies in the last 30 days"

The system:

  1. Understands your intent
  2. Generates the query
  3. Fetches the data
  4. Creates visualizations
  5. Returns actionable insights

Technical Architecture

Frontend

React React for UI
TypeScript TypeScript for type safety
Vite Vite for fast development

Data & AI

AI LLM for natural language understanding
Python Python for data processing
Data Sample dataset for demo

Deployment

GitHub GitHub Pages hosting
Git Version control & CI/CD

Key Learnings from Building This

1. Natural Language Is Hard

Getting the LLM to correctly interpret ambiguous queries took significant prompt engineering. Edge cases like “last month” versus “past 30 days” or “sales” versus “revenue” required careful context management.

But when it works, the user experience is worth it.

2. Visualizations Must Be Smart

You can’t just show tables for everything. The system needs to:

  • Detect data types (time series, categories, metrics)
  • Choose appropriate chart types automatically
  • Handle edge cases (no data, single data point, etc.)

3. Speed Matters More Than Perfection

Users expect instant feedback. A 3-second delay kills the conversational flow. This meant:

  • Optimizing query generation
  • Pre-processing common patterns
  • Using streaming responses where possible

4. Zero Training Needed

When I tested this with non-technical users, they understood it immediately. No tutorial. No “how do I filter this?” questions.

That’s what makes this approach viable.

What’s Next?

This prototype is just the beginning. Here’s what I’m exploring next:

Phase 2: Proactive Insights

  • System suggests questions based on data anomalies
  • Automated alerts for significant changes
  • Pattern detection and recommendations

Phase 3: Multi-Source Integration

  • Connect to real databases (Snowflake, BigQuery, Databricks)
  • Cross-reference multiple data sources
  • Real-time data streaming

Phase 4: Collaboration Features

  • Share insights with team members
  • Annotate findings
  • Build insight libraries

Try the Prototype

No login required. No installation needed.

Launch Prototype

Why This Matters

Dashboards aren’t going away. But analytics is moving toward:

Accessible
Anyone can explore data
Fast
Insights in seconds
Intuitive
No training required
Dynamic
Adapts to context

The companies that make data genuinely accessible to everyone will have the advantage.

Source Code

GitHub

Built with: React, TypeScript, Vite, LLM APIs

Status: Live prototype

Interested in implementing this for your organization? Get in touch

Interested in similar projects?

I help companies build modern data solutions and web applications. Let's discuss your next project!

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