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 director of asset protection 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’s app on Snowflake’s AI Data Cloud, the company built an enterprise knowledge graph (EKG). This EKG 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 Robocall 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 even non-coders could query. This improved detection accuracy and speed while reducing the burden on data teams.
Dispatch Optimization
The Challenge
The company needed to match the right technician to the right job—at the right time. Human dispatchers using spreadsheets couldn’t scale this reliably across large regions.
The Solution
RelationalAI built a two-step optimization model:
Mapped historical activity to highlight demand hotspots
Used prescriptive reasoning (Mixed-Integer Programming) to assign daily appointments efficiently
The Results
The model outperformed manual dispatching. It cut drive times, boosted customer service capacity, and balanced technician workloads—without switching between multiple systems.
Multiple dispatch optimization
The Challenge
Missed appointments wasted time and risked customer churn. The company needed a way to predict whether customers would be home.
The Solution
RelationalAI enabled predictive reasoning through a machine learning model that estimated appointment attendance. This model was run directly in their native app—no external tools needed.
The Results
Technicians were dispatched more efficiently. The company reduced no-shows and improved resource use—while increasing the chance of keeping customers.
End-to-End Finance Lifecycle
The Challenge
Chargebacks were costing the company millions. But identifying at-risk customers meant joining massive datasets across multiple systems—something traditional SQL couldn’t handle well.
The Solution
With RelationalAI’s guidance, the company used Object-Role Modeling (ORM) with an open-source tool (NORMA). They built a conceptual model and joined datasets with ease, even handling 4-way joins.
The Results
The system is now ready to run algorithms that detect billing risks. Once fully deployed, it’s expected to improve revenue protection and reduce financial waste.
Project Information
Client
Fortune 100
Category
Telecom
Applications
Enterprise knowledge graphs
Fraud robocall detection
Dispatch optimization
Multiple dispatch optimization
End-to-end finance lifecycle
