How a Fortune 100 Company Uses RelationalAI to Build Smarter Enterprise AI Applications
Overview
A Fortune 100 communications company is building smarter systems with advanced AI. From fighting robocalls to improving technician dispatch, the company has partnered with RelationalAI to solve tough challenges using graph reasoning, rule-based reasoning, predictive reasoning, and prescriptive reasoning. These solutions are helping streamline operations, cut costs, and improve customer experience.
Enterprise Knowledge Graphs
The Challenge
Internal teams needed answers to business questions—fast. But the data was trapped in tables and columns, which made it hard for non-technical users to find insights. For example, a team lead from an asset protection team might ask, “which shipment location has the most device loss?” But getting that answer took time, technical help, and multiple queries.
The Solution
Using RelationalAI, the company built an relational knowledge graph (RKG). This Relational Knowledge Graph was powered by a semantic layer that defined concepts, relationships, and business rules—validated by subject matter experts.
- The ontology let business users ask conceptual questions using plain language.
- Queries like “b in Bill” became possible, returning results without writing complex SQL.
- Rule-based reasoning extended the graph over time, enabling even more insights.
The Results
Hundreds of concepts were created from thousands of data tables. Teams made data more accessible to everyday users, reduced duplicate work, and improved operational efficiency—all while maintaining governance and security. A lean, repeatable process helped scale the effort.
Fraud Detection
The Challenge
Robocalls were damaging trust and customer satisfaction. Detecting them through traditional databases wasn’t scalable—querying millions of calls and relationships required complex, fragile logic.
The Solution
The company used graph reasoning with RelationalAI to detect suspicious calling patterns. The system analyzed short-duration calls from unfamiliar contacts and mapped trust networks over a 30-day period.
The Results
With rule-based and recursive reasoning, the organization created models that improved detection accuracy and speed while reducing the burden on data teams.
Project Information
Customer
Fortune 100
Category
Telecom
Applications
Enterprise knowledge graphs
Fraud robocall detection
Dispatch optimization
Multiple dispatch optimization
End-to-end finance lifecycle
