AI Knowledge Graph Cuts Hospitality Rule Analysis by 60 to 70 Percent

Overview

60 to 70 Percent Less Manual Analysis with AI Rule Intelligence

How a leading global timeshare and hospitality brand replaced slow, manual rule-change analysis across 3,500-plus properties and five brands with an AI-powered rules intelligence platform on AWS and Neo4j, letting teams simulate changes and see downstream impact before deployment.

Rule analysis that once took hours now takes minutes, positioning the client to reduce manual effort by 60 to 70 percent.

This was not a reporting or search solution. It was an AI-powered rules intelligence platform connecting business rules, enterprise context, and decision-making.

A leading global timeshare and hospitality brand managed thousands of business rules across dynamic pricing, inventory allocation, member eligibility, and club bookings, which made impact analysis slow, manual, and difficult to scale across 3,500-plus properties and five brands. Working with AWS, Myridius built an AI-powered rules intelligence platform using a Neo4j knowledge graph and Amazon Bedrock that lets teams query business rules in natural language, simulate changes, and understand downstream impacts before deployment.

Key Outcomes

Manual analysis reduced by 60 to 70 percent, with rule impact assessment that once took hours now completed in minutes.
Four of five platform objectives fully achieved, with the fifth nearing completion.
Faster decision speed, stronger governance, and greater operational confidence across pricing, inventory, and member operations.

The Challenge

As business rules expanded across pricing, inventory, bookings, and member eligibility, understanding how a single change would affect the broader operation became increasingly difficult. Fragmented systems, manual analysis, and disconnected rule logic made impact assessment slow, inconsistent, and risky, and much of the necessary knowledge lived with a small number of people. The client needed a connected, intelligent view of its business rules, one that could simulate changes, reveal downstream impacts, and enable confident decisions before they reached production.

Status Quo and Desired State

Status Quo	Desired State
Business rules across pricing, inventory, eligibility, and booking lived in separate systems, with no way to see how they connected.	A unified rules intelligence platform models every rule relationship across the business, providing a single source for analysis, simulation, and validation.
Assessing the impact of a rule change meant hours of manual investigation, dependent on institutional knowledge held by a few people.	Teams can ask a plain-language question about any rule and receive an immediate, auto-generated summary of its logic, dependencies, and downstream impact.
Rule changes were approved and deployed with no simulation step, creating exposure to unintended effects on pricing, inventory, and member experience.	Every proposed rule change can be simulated before deployment, with downstream effects visible across pricing, inventory, and eligibility before anything reaches production.

Transformation Goals

The engagement focused on three business outcomes: reducing operational risk, improving decision quality, and enabling faster, more confident rule management at scale.

Create a connected view of business rules: Unify pricing, inventory, eligibility, and booking rules into a single connected view so teams can understand dependencies and assess the impact of every change before deployment.

Accelerate decision-making with AI: Enable analysts to explore rule logic using natural-language queries, replacing manual investigation with AI-powered insights that make impact analysis faster and more accessible.

Reduce risk before production: Simulate rule changes before deployment to identify downstream impacts, reduce manual analysis by 60 to 70 percent, and improve confidence that every change protects revenue, operations, and guest experience.

Myridius Solution Approach

Myridius began by demonstrating a working MVP on EVOQ, our execution runtime, to validate the approach and accelerate the client's path from concept to production. Using Code to Insight, an AI-powered rules intelligence capability within EVOQ, Myridius transformed fragmented business rules into a connected knowledge graph that became the foundation for intelligent decision-making across pricing, inventory, member eligibility, and bookings. By combining enterprise context with AI, Myridius enabled analysts to understand rule relationships, simulate changes, and assess business impact before deployment.

Connected enterprise rule intelligence: Unified business rules from multiple source systems into a knowledge graph that mapped the relationships between pricing, inventory, eligibility, and booking logic, creating a trusted foundation for enterprise-wide impact analysis.

Embedded AI into decision-making: Enabled analysts to ask natural-language questions about business rules and receive grounded, context-aware insights generated from connected rule relationships rather than isolated documents or static reports.

Enabled continuous learning: Linked historical rule changes with booking, revenue, and operational outcomes, allowing teams to validate decisions, learn from previous changes, and continuously improve pricing, inventory, and member strategies.

Governance and Trust Layer

When business rules directly influence pricing, member eligibility, bookings, and revenue, trust in AI is essential. Myridius designed the platform so every AI-generated recommendation is grounded in connected business rules and enterprise context through GraphRAG, not generic model inference. Analysts can see the rule relationships, dependencies, and supporting evidence behind every response, keeping decisions transparent, explainable, and auditable, with an AWS API Gateway security boundary and IAM roles controlling access. To further improve decision confidence, the platform correlates historical rule changes with business outcomes such as pricing performance, inventory utilization, and member experience, so teams can validate proposed changes against past results and make every approval faster and easier to defend.

Measurable Impact

The engagement transformed how the client evaluates business rule changes. What once required hours of manual investigation across disconnected systems can now be completed in minutes through AI-powered impact analysis, enabling faster decisions, lower operational risk, and greater confidence before changes reach production. The platform has met its core business objectives and is progressing through final validation ahead of broader adoption.

The result:

Minutes instead of hours: business teams can instantly assess the downstream impact of rule changes across pricing, inventory, member eligibility, and bookings, with four of five platform objectives fully achieved and the fifth nearing completion.
60 to 70 percent less manual analysis: AI-powered impact assessment replaces time-consuming investigation with an intelligent, scalable model that improves productivity across pricing, inventory, and member operations.
Faster, more confident decisions: pricing analysts, inventory managers, and club operations teams can ask natural-language questions, simulate proposed rule changes, and validate outcomes within their existing workflow without learning new tools or processes.

Operational Transformation

Operational Area	Before Myridius	After Myridius
Rule Visibility	Pricing, inventory, eligibility, and booking rules lived in separate systems, with no view of how changing one affected the others.	Every rule is connected in a single intelligence platform, queryable in plain language with immediate visibility into relationships.
Rule Change Analysis Speed	Assessing a rule change took hours of manual investigation, dependent on a few people who knew where the rules lived.	The same analysis now takes minutes, with automatic dependency tracing and a grounded impact summary from the rule graph.
Pre-Deployment Risk Control	Rule changes went to production with no simulation step, exposing pricing, inventory, and member experience to unmodeled risk.	Every proposed change can be simulated before deployment, with downstream effects visible before anything goes live.
Analyst Capacity and Focus	Analysts spent significant capacity on reactive, manual rule investigation rather than value-creating work.	With a 60 to 70 percent reduction in manual analysis in reach, capacity shifts from investigation to decision-making and optimization.
Revenue and Experience Confidence	There was no way to know whether past rule changes delivered the intended pricing, inventory, or member outcomes.	Historical rule changes are correlated to booking and revenue outcomes, giving teams an evidence base for every future decision.

Technology Stack

Functional Area	Technologies Used	Business Purpose
Knowledge Graph and Rules Modeling	Neo4j graph database, AuraDB managed cloud graph store	The connected rules model that makes pricing, inventory, eligibility, and booking rules queryable and simulatable as a unified system.
Generative AI and Retrieval	Amazon Bedrock foundation models, GraphRAG	AI-powered impact analysis and plain-language rule summaries, grounded in the actual rule graph so every answer is accurate and defensible.
Rules Data Pipeline	Databricks source catalog, Amazon S3 staging	Connects the source of truth for rules data, including historical changes and booking outcomes used for validation.
Conversational User Interface	Azure single-page application, natural-language query interface	Delivers the rules intelligence capability inside the tools teams already use, with zero adoption friction.
Security and Access Control	AWS API Gateway, IAM roles	Secure access between the client's existing application environment and the backend.
Infrastructure and Orchestration	AWS Fargate serverless containers, AWS API Gateway	Handles query processing and routing at scale without operational overhead, so performance stays consistent as usage grows.

Frequently Asked Questions

How much faster is rule-change impact analysis on the new platform?

Analysis that once took hours of manual investigation across disconnected systems now takes minutes. Business teams can instantly assess the downstream impact of a rule change across pricing, inventory, member eligibility, and bookings. This positions the client to reduce manual analysis effort by 60 to 70 percent, with four of five platform objectives fully achieved and the fifth nearing completion.

How does the platform keep AI answers accurate and auditable?

Every AI-generated recommendation is grounded in the connected business rules and enterprise context through GraphRAG, rather than generic model inference. Analysts can see the rule relationships, dependencies, and supporting evidence behind each response, so decisions stay transparent, explainable, and auditable. An AWS API Gateway security boundary and IAM roles control access to the platform.

Why use a knowledge graph instead of standard document retrieval?

Business rules are deeply interconnected, so understanding one change means understanding its relationships to pricing, inventory, eligibility, and booking logic. A Neo4j knowledge graph models those relationships directly, and GraphRAG grounds AI answers in that structured graph rather than in flat documents. This produces accurate, relationship-aware impact analysis that isolated documents or static reports cannot deliver.

Do teams have to learn a new tool to use it?

No. The full rules intelligence capability is embedded within the client's existing Azure application, so pricing analysts, inventory managers, and club operations teams work in the tools they already use. They can ask natural-language questions, simulate proposed changes, and validate outcomes with zero adoption friction and no new application to learn.

Rule Changes Are Business Decisions. Make Them with Confidence.

In vacation ownership, every business rule shapes an outcome. Pricing influences revenue, inventory determines availability, and member eligibility defines guest experience. When those decisions are made without understanding their downstream impact, organizations increase operational risk, erode customer trust, and leave revenue on the table. This case shows how an AI-powered rules intelligence platform can connect business rules, enterprise context, and decision-making, giving teams the confidence to simulate changes, understand their impact, and execute with greater speed and precision.

This was not a reporting or search solution. It was an engineered rules intelligence platform that makes downstream impact visible before a change is made.

What's Next

If your teams manage complex pricing, inventory, eligibility, or operational rules without clear visibility into their downstream impact, Myridius can help. We build AI-powered intelligence platforms that enable faster decisions, reduce operational risk, and give organizations the confidence to execute change at scale.

Talk to a Myridius expert and send us a message at Contact Myridius.