Zilliant Gen AI Pricing Analytics generates data-driven pricing intelligence to help you understand the next steps for achieving your business goals. It replaces manual SQL queries and spreadsheet analytics with an integrated, AI-powered approach.
The platform’s primary components include a pricing-specific analytics engine, embedded data visualizations, natural language interface for quick data visualizations and summaries, and integration capabilities with existing pricing management systems.
Analytical capabilities are built around several key technical features:
Real-time margin analysis with automated alert systems for negative margins
Product demand pattern recognition algorithms for identifying market dynamics
Natural language query processing for impromptu data analysis
Automated report generation based on predefined pricing metrics
A scalable backend infrastructure enables Gen AI Pricing Analytics to handle large-scale pricing data while maintaining real-time processing capabilities.
How Analytics works
Zilliant leverages a standard data model across all of its customers. Built on that data model, Analytics reports and visualizations are specifically optimized for pricing-related analysis. For details, read Data input specifications.
Analytics analyzes your data through the Gen AI Data Explorer AI engine to support natural language query processing for instant data analysis.
Analytics also analyzes your data through an Insights Engine to generate insights across a standard set of metrics: revenue, profit, margin, and sales volume. For details, read Insights.
Using Analytics
Upload your data into Zilliant.
View the current state of your pricing and business.
Analyze areas for potential price optimization.
Track business performance to ensure that previous pricing changes had the desired impact. You can further track performance based on a product or customer.
After uploading your data into Zilliant, use the Gen AI Data Explorer at any time to ask questions or give instructions to Analytics. This tool allows you to use natural language to navigate your data when you have simple questions, such as “Why did sales drop in fall of 2025?”